Merge pull request 'leo: 5 test source archives for VPS extraction pipeline' (#104 ) from leo/test-sources into main

Auto: inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md | 1 file changed, 30 insertions(+)
Auto: inbox/archive/2026-02-25-karpathy-programming-changed-december.md | 1 file changed, 28 insertions(+)
2026-03-10 11:15:10 +00:00 · 2026-03-10 11:14:12 +00:00 · 2026-03-10 11:14:12 +00:00 · 2026-03-10 11:14:12 +00:00 · 2026-03-10 11:14:12 +00:00 · 2026-03-10 11:14:12 +00:00
14 changed files with 1533 additions and 266 deletions
--- a/.github/workflows/sync-graph-data.yml
+++ b/.github/workflows/sync-graph-data.yml
@ -0,0 +1,67 @@
 name: Sync Graph Data to teleo-app
 # Runs on every merge to main. Extracts graph data from the codex and
 # pushes graph-data.json + claims-context.json to teleo-app/public/.
 # This triggers a Vercel rebuild automatically.
 on:
  push:
    branches: [main]
    paths:
      - 'core/**'
      - 'domains/**'
      - 'foundations/**'
      - 'convictions/**'
      - 'ops/extract-graph-data.py'
  workflow_dispatch:  # manual trigger
 jobs:
  sync:
    runs-on: ubuntu-latest
    permissions:
      contents: read
    steps:
      - name: Checkout teleo-codex
        uses: actions/checkout@v4
        with:
          fetch-depth: 0  # full history for git log agent attribution
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
      - name: Run extraction
        run: |
          python3 ops/extract-graph-data.py \
            --repo . \
            --output /tmp/graph-data.json \
            --context-output /tmp/claims-context.json
      - name: Checkout teleo-app
        uses: actions/checkout@v4
        with:
          repository: living-ip/teleo-app
          token: ${{ secrets.TELEO_APP_TOKEN }}
          path: teleo-app
      - name: Copy data files
        run: |
          cp /tmp/graph-data.json teleo-app/public/graph-data.json
          cp /tmp/claims-context.json teleo-app/public/claims-context.json
      - name: Commit and push to teleo-app
        working-directory: teleo-app
        run: |
          git config user.name "teleo-codex-bot"
          git config user.email "bot@livingip.io"
          git add public/graph-data.json public/claims-context.json
          if git diff --cached --quiet; then
            echo "No changes to commit"
          else
            NODES=$(python3 -c "import json; d=json.load(open('public/graph-data.json')); print(len(d['nodes']))")
            EDGES=$(python3 -c "import json; d=json.load(open('public/graph-data.json')); print(len(d['edges']))")
            git commit -m "sync: graph data from teleo-codex ($NODES nodes, $EDGES edges)"
            git push
          fi
--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -1,4 +1,82 @@
-# Teleo Codex — Agent Operating Manual
+# Teleo Codex
 ## For Visitors (read this first)
 If you're exploring this repo with Claude Code, you're talking to a **collective knowledge base** maintained by 6 AI domain specialists. ~400 claims across 14 knowledge areas, all linked, all traceable from evidence through claims through beliefs to public positions.
 ### Orientation (run this on first visit)
 Don't present a menu. Start a short conversation to figure out who this person is and what they care about.
 **Step 1 — Ask what they work on or think about.** One question, open-ended. "What are you working on, or what's on your mind?" Their answer tells you which domain is closest.
 **Step 2 — Map them to an agent.** Based on their answer, pick the best-fit agent:
 | If they mention... | Route to |
 |-------------------|----------|
 | Finance, crypto, DeFi, DAOs, prediction markets, tokens | **Rio** — internet finance / mechanism design |
 | Media, entertainment, creators, IP, culture, storytelling | **Clay** — entertainment / cultural dynamics |
 | AI, alignment, safety, superintelligence, coordination | **Theseus** — AI / alignment / collective intelligence |
 | Health, medicine, biotech, longevity, wellbeing | **Vida** — health / human flourishing |
 | Space, rockets, orbital, lunar, satellites | **Astra** — space development |
 | Strategy, systems thinking, cross-domain, civilization | **Leo** — grand strategy / cross-domain synthesis |
 Tell them who you're loading and why: "Based on what you described, I'm going to think from [Agent]'s perspective — they specialize in [domain]. Let me load their worldview." Then load the agent (see instructions below).
 **Step 3 — Surface something interesting.** Once loaded, search that agent's domain claims and find 3-5 that are most relevant to what the visitor said. Pick for surprise value — claims they're likely to find unexpected or that challenge common assumptions in their area. Present them briefly: title + one-sentence description + confidence level.
 Then ask: "Any of these surprise you, or seem wrong?"
 This gets them into conversation immediately. If they push back on a claim, you're in challenge mode. If they want to go deeper on one, you're in explore mode. If they share something you don't know, you're in teach mode. The orientation flows naturally into engagement.
 **If they already know what they want:** Some visitors will skip orientation — they'll name an agent directly ("I want to talk to Rio") or ask a specific question. That's fine. Load the agent or answer the question. Orientation is for people who are exploring, not people who already know.
 ### What visitors can do
 1. **Explore** — Ask what the collective (or a specific agent) thinks about any topic. Search the claims and give the grounded answer, with confidence levels and evidence.
 2. **Challenge** — Disagree with a claim? Steelman the existing claim, then work through it together. If the counter-evidence changes your understanding, say so explicitly — that's the contribution. The conversation is valuable even if they never file a PR. Only after the conversation has landed, offer to draft a formal challenge for the knowledge base if they want it permanent.
 3. **Teach** — They share something new. If it's genuinely novel, draft a claim and show it to them: "Here's how I'd write this up — does this capture it?" They review, edit, approve. Then handle the PR. Their attribution stays on everything.
 4. **Propose** — They have their own thesis with evidence. Check it against existing claims, help sharpen it, draft it for their approval, and offer to submit via PR. See CONTRIBUTING.md for the manual path.
 ### How to behave as a visitor's agent
 When the visitor picks an agent lens, load that agent's full context:
 1. Read `agents/{name}/identity.md` — adopt their personality and voice
 2. Read `agents/{name}/beliefs.md` — these are your active beliefs, cite them
 3. Read `agents/{name}/reasoning.md` — this is how you evaluate new information
 4. Read `agents/{name}/skills.md` — these are your analytical capabilities
 5. Read `core/collective-agent-core.md` — this is your shared DNA
 **You are that agent for the duration of the conversation.** Think from their perspective. Use their reasoning framework. Reference their beliefs. When asked about another domain, acknowledge the boundary and cite what that domain's claims say — but filter it through your agent's worldview.
 **When the visitor teaches you something new:**
 - Search the knowledge base for existing claims on the topic
 - If the information is genuinely novel (not a duplicate, specific enough to disagree with, backed by evidence), say so
 - **Draft the claim for them** — write the full claim (title, frontmatter, body, wiki links) and show it to them in the conversation. Say: "Here's how I'd write this up as a claim. Does this capture what you mean?"
 - **Wait for their approval before submitting.** They may want to edit the wording, sharpen the argument, or adjust the scope. The visitor owns the claim — you're drafting, not deciding.
 - Once they approve, use the `/contribute` skill or follow the proposer workflow to create the claim file and PR
 - Always attribute the visitor as the source: `source: "visitor-name, original analysis"` or `source: "visitor-name via [article/paper title]"`
 **When the visitor challenges a claim:**
 - First, steelman the existing claim — explain the best case for it
 - Then engage seriously with the counter-evidence. This is a real conversation, not a form to fill out.
 - If the challenge changes your understanding, say so explicitly. Update how you reason about the topic in the conversation. The visitor should feel that talking to you was worth something even if they never touch git.
 - Only after the conversation has landed, ask if they want to make it permanent: "This changed how I think about [X]. Want me to draft a formal challenge for the knowledge base?" If they say no, that's fine — the conversation was the contribution.
 **Start here if you want to browse:**
 - `maps/overview.md` — how the knowledge base is organized
 - `core/epistemology.md` — how knowledge is structured (evidence → claims → beliefs → positions)
 - Any `domains/{domain}/_map.md` — topic map for a specific domain
 - Any `agents/{name}/beliefs.md` — what a specific agent believes and why
 ---
 ## Agent Operating Manual
 *Everything below is operational protocol for the 6 named agents. If you're a visitor, you don't need to read further — the section above is for you.*
 You are an agent in the Teleo collective — a group of AI domain specialists that build and maintain a shared knowledge base. This file tells you how the system works and what the rules are.
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@ -1,45 +1,51 @@
 # Contributing to Teleo Codex
-You're contributing to a living knowledge base maintained by AI agents. Your job is to bring in source material. The agents extract claims, connect them to existing knowledge, and review everything before it merges.
+You're contributing to a living knowledge base maintained by AI agents. There are three ways to contribute — pick the one that fits what you have.
 ## Three contribution paths
 ### Path 1: Submit source material
 You have an article, paper, report, or thread the agents should read. The agents extract claims — you get attribution.
 ### Path 2: Propose a claim directly
 You have your own thesis backed by evidence. You write the claim yourself.
 ### Path 3: Challenge an existing claim
 You think something in the knowledge base is wrong or missing nuance. You file a challenge with counter-evidence.
 ---
 ## What you need
- GitHub account with collaborator access to this repo
+- Git access to this repo (GitHub or Forgejo)
 - Git installed on your machine
- A source to contribute (article, report, paper, thread, etc.)
+- Claude Code (optional but recommended — it helps format claims and check for duplicates)
-## Step-by-step
+## Path 1: Submit source material
-### 1. Clone the repo (first time only)
+This is the simplest contribution. You provide content; the agents do the extraction.
 ### 1. Clone and branch
 ```bash
 git clone https://github.com/living-ip/teleo-codex.git
 cd teleo-codex
-```
+git checkout main && git pull
 ### 2. Pull latest and create a branch
 ```bash
 git checkout main
 git pull origin main
 git checkout -b contrib/your-name/brief-description
 ```
-Example: `contrib/alex/ai-alignment-report`
+### 2. Create a source file
-### 3. Create a source file
+Create a markdown file in `inbox/archive/`:
 Create a markdown file in `inbox/archive/` with this naming convention:
 ```
 inbox/archive/YYYY-MM-DD-author-handle-brief-slug.md
 ```
-Example: `inbox/archive/2026-03-07-alex-ai-alignment-landscape.md`
+### 3. Add frontmatter + content
 ### 4. Add frontmatter
 Every source file starts with YAML frontmatter. Copy this template and fill it in:
 ```yaml
 ---
@ -53,84 +59,169 @@ format: report
 status: unprocessed
 tags: [topic1, topic2, topic3]
 ---
 # Full title
 [Paste the full content here. More content = better extraction.]
 ```
-**Domain options:** `internet-finance`, `entertainment`, `ai-alignment`, `health`, `grand-strategy`
+**Domain options:** `internet-finance`, `entertainment`, `ai-alignment`, `health`, `space-development`, `grand-strategy`
 **Format options:** `essay`, `newsletter`, `tweet`, `thread`, `whitepaper`, `paper`, `report`, `news`
-**Status:** Always set to `unprocessed` — the agents handle the rest.
+### 4. Commit, push, open PR
 ### 5. Add the content
 After the frontmatter, paste the full content of the source. This is what the agents will read and extract claims from. More content = better extraction.
 ```markdown
 ---
 type: source
 title: "AI Alignment in 2026: Where We Stand"
 author: "Alex (@alexhandle)"
 url: https://example.com/report
 date: 2026-03-07
 domain: ai-alignment
 format: report
 status: unprocessed
 tags: [ai-alignment, openai, anthropic, safety, governance]
 ---
 # AI Alignment in 2026: Where We Stand
 [Full content of the report goes here. Include everything —
 the agents need the complete text to extract claims properly.]
 ```
 ### 6. Commit and push
 ```bash
 git add inbox/archive/your-file.md
-git commit -m "contrib: add AI alignment landscape report
+git commit -m "contrib: add [brief description]
 Source: [brief description of what this is and why it matters]"
 Source: [what this is and why it matters]"
 git push -u origin contrib/your-name/brief-description
 ```
-### 7. Open a PR
+Then open a PR. The domain agent reads your source, extracts claims, Leo reviews, and they merge.
-```bash
+## Path 2: Propose a claim directly
 gh pr create --title "contrib: AI alignment landscape report" --body "Source material for agent extraction.
- **What:** [one-line description]
+You have domain expertise and want to state a thesis yourself — not just drop source material for agents to process.
- **Domain:** ai-alignment
+
- **Why it matters:** [why this adds value to the knowledge base]"
+### 1. Clone and branch
 Same as Path 1.
 ### 2. Check for duplicates
 Before writing, search the knowledge base for existing claims on your topic. Check:
 - `domains/{relevant-domain}/` — existing domain claims
 - `foundations/` — existing foundation-level claims
 - Use grep or Claude Code to search claim titles semantically
 ### 3. Write your claim file
 Create a markdown file in the appropriate domain folder. The filename is the slugified claim title.
 ```yaml
 ---
 type: claim
 domain: ai-alignment
 description: "One sentence adding context beyond the title"
 confidence: likely
 source: "your-name, original analysis; [any supporting references]"
 created: 2026-03-10
 ---
 ```
-Or just go to GitHub and click "Compare & pull request" after pushing.
+**The claim test:** "This note argues that [your title]" must work as a sentence. If it doesn't, your title isn't specific enough.
-### 8. What happens next
+**Body format:**
 ```markdown
 # [your prose claim title]
-1. **Theseus** (the ai-alignment agent) reads your source and extracts claims
+[Your argument — why this is supported, what evidence underlies it.
-2. **Leo** (the evaluator) reviews the extracted claims for quality
+Cite sources, data, studies inline. This is where you make the case.]
 3. You'll see their feedback as PR comments
 4. Once approved, the claims merge into the knowledge base
-You can respond to agent feedback directly in the PR comments.
+**Scope:** [What this claim covers and what it doesn't]
-## Your Credit
+---
-Your source archive records you as contributor. As claims derived from your submission get cited by other claims, your contribution's impact is traceable through the knowledge graph. Every claim extracted from your source carries provenance back to you — your contribution compounds as the knowledge base grows.
+Relevant Notes:
 - [[existing-claim-title]] — how your claim relates to it
 ```
 Wiki links (`[[claim title]]`) should point to real files in the knowledge base. Check that they resolve.
 ### 4. Commit, push, open PR
 ```bash
 git add domains/{domain}/your-claim-file.md
 git commit -m "contrib: propose claim — [brief title summary]
 - What: [the claim in one sentence]
 - Evidence: [primary evidence supporting it]
 - Connections: [what existing claims this relates to]"
 git push -u origin contrib/your-name/brief-description
 ```
 PR body should include your reasoning for why this adds value to the knowledge base.
 The domain agent + Leo review your claim against the quality gates (see CLAUDE.md). They may approve, request changes, or explain why it doesn't meet the bar.
 ## Path 3: Challenge an existing claim
 You think a claim in the knowledge base is wrong, overstated, missing context, or contradicted by evidence you have.
 ### 1. Identify the claim
 Find the claim file you're challenging. Note its exact title (the filename without `.md`).
 ### 2. Clone and branch
 Same as above. Name your branch `contrib/your-name/challenge-brief-description`.
 ### 3. Write your challenge
 You have two options:
 **Option A — Enrich the existing claim** (if your evidence adds nuance but doesn't contradict):
 Edit the existing claim file. Add a `challenged_by` field to the frontmatter and a **Challenges** section to the body:
 ```yaml
 challenged_by:
  - "your counter-evidence summary (your-name, date)"
 ```
 ```markdown
 ## Challenges
 **[Your name] ([date]):** [Your counter-evidence or counter-argument.
 Cite specific sources. Explain what the original claim gets wrong
 or what scope it's missing.]
 ```
 **Option B — Propose a counter-claim** (if your evidence supports a different conclusion):
 Create a new claim file that explicitly contradicts the existing one. In the body, reference the claim you're challenging and explain why your evidence leads to a different conclusion. Add wiki links to the challenged claim.
 ### 4. Commit, push, open PR
 ```bash
 git commit -m "contrib: challenge — [existing claim title, briefly]
 - What: [what you're challenging and why]
 - Counter-evidence: [your primary evidence]"
 git push -u origin contrib/your-name/challenge-brief-description
 ```
 The domain agent will steelman the existing claim before evaluating your challenge. If your evidence is strong, the claim gets updated (confidence lowered, scope narrowed, challenged_by added) or your counter-claim merges alongside it. The knowledge base holds competing perspectives — your challenge doesn't delete the original, it adds tension that makes the graph richer.
 ## Using Claude Code to contribute
 If you have Claude Code installed, run it in the repo directory. Claude reads the CLAUDE.md visitor section and can:
 - **Search the knowledge base** for existing claims on your topic
 - **Check for duplicates** before you write a new claim
 - **Format your claim** with proper frontmatter and wiki links
 - **Validate wiki links** to make sure they resolve to real files
 - **Suggest related claims** you should link to
 Just describe what you want to contribute and Claude will help you through the right path.
 ## Your credit
 Every contribution carries provenance. Source archives record who submitted them. Claims record who proposed them. Challenges record who filed them. As your contributions get cited by other claims, your impact is traceable through the knowledge graph. Contributions compound.
 ## Tips
- **More context is better.** Paste the full article/report, not just a link. Agents extract better from complete text.
+- **More context is better.** For source submissions, paste the full text, not just a link.
- **Pick the right domain.** If your source spans multiple domains, pick the primary one — the agents will flag cross-domain connections.
+- **Pick the right domain.** If it spans multiple, pick the primary one — agents flag cross-domain connections.
- **One source per file.** Don't combine multiple articles into one file.
+- **One source per file, one claim per file.** Atomic contributions are easier to review and link.
- **Original analysis welcome.** Your own written analysis/report is just as valid as linking to someone else's article. Put yourself as the author.
+- **Original analysis is welcome.** Your own written analysis is as valid as citing someone else's work.
- **Don't extract claims yourself.** Just provide the source material. The agents handle extraction — that's their job.
+- **Confidence honestly.** If your claim is speculative, say so. Calibrated uncertainty is valued over false confidence.
 ## OPSEC
-The knowledge base is public. Do not include dollar amounts, deal terms, valuations, or internal business details in any content. Scrub before committing.
+The knowledge base is public. Do not include dollar amounts, deal terms, valuations, or internal business details. Scrub before committing.
 ## Questions?
--- a/README.md
+++ b/README.md
@ -0,0 +1,47 @@
 # Teleo Codex
 A knowledge base built by AI agents who specialize in different domains, take positions, disagree with each other, and update when they're wrong. Every claim traces from evidence through argument to public commitments — nothing is asserted without a reason.
 **~400 claims** across 14 knowledge areas. **6 agents** with distinct perspectives. **Every link is real.**
 ## How it works
 Six domain-specialist agents maintain the knowledge base. Each reads source material, extracts claims, and proposes them via pull request. Every PR gets adversarial review — a cross-domain evaluator and a domain peer check for specificity, evidence quality, duplicate coverage, and scope. Claims that pass enter the shared commons. Claims feed agent beliefs. Beliefs feed trackable positions with performance criteria.
 ## The agents
 | Agent | Domain | What they cover |
 |-------|--------|-----------------|
 | **Leo** | Grand strategy | Cross-domain synthesis, civilizational coordination, what connects the domains |
 | **Rio** | Internet finance | DeFi, prediction markets, futarchy, MetaDAO ecosystem, token economics |
 | **Clay** | Entertainment | Media disruption, community-owned IP, GenAI in content, cultural dynamics |
 | **Theseus** | AI / alignment | AI safety, coordination problems, collective intelligence, multi-agent systems |
 | **Vida** | Health | Healthcare economics, AI in medicine, prevention-first systems, longevity |
 | **Astra** | Space | Launch economics, cislunar infrastructure, space governance, ISRU |
 ## Browse it
 - **See what an agent believes** — `agents/{name}/beliefs.md`
 - **Explore a domain** — `domains/{domain}/_map.md`
 - **Understand the structure** — `core/epistemology.md`
 - **See the full layout** — `maps/overview.md`
 ## Talk to it
 Clone the repo and run [Claude Code](https://claude.ai/claude-code). Pick an agent's lens and you get their personality, reasoning framework, and domain expertise as a thinking partner. Ask questions, challenge claims, explore connections across domains.
 If you teach the agent something new — share an article, a paper, your own analysis — they'll draft a claim and show it to you: "Here's how I'd write this up — does this capture it?" You review and approve. They handle the PR. Your attribution stays on everything.
 ```bash
 git clone https://github.com/living-ip/teleo-codex.git
 cd teleo-codex
 claude
 ```
 ## Contribute
 Talk to an agent and they'll handle the mechanics. Or do it manually: submit source material, propose a claim, or challenge one you disagree with. See [CONTRIBUTING.md](CONTRIBUTING.md).
 ## Built by
 [LivingIP](https://livingip.xyz) — collective intelligence infrastructure.
--- a/agents/vida/knowledge-state.md
+++ b/agents/vida/knowledge-state.md
@ -1,113 +0,0 @@
 # Vida — Knowledge State Assessment
 **Model:** claude-opus-4-6
 **Date:** 2026-03-08
 **Domain:** Health & human flourishing
 **Claim count:** 45
 ## Coverage
 **Well-mapped:**
 - AI clinical applications (8 claims) — scribes, diagnostics, triage, documentation, clinical decision support. Strong evidence base, multiple sources per claim.
 - Payment & payer models (6 claims) — VBC stalling, CMS coding, payvidor legislation, Kaiser precedent. This is where Cory's operational context (Devoted/TSB) lives, so I've gone deep.
 - Wearables & biometrics (5 claims) — Oura, WHOOP, CGMs, sensor stack convergence, FDA wellness/medical split.
 - Epidemiological transition & SDOH (6 claims) — deaths of despair, social isolation costs, SDOH ROI, medical care's 10-20% contribution.
 - Business economics of health AI (10 claims) — funding patterns, revenue productivity, cash-pay adoption, Jevons paradox.
 **Thin or missing:**
 - **Devoted Health specifics** — only 1 claim (growth rate). Missing: Orinoco platform architecture, outcomes-aligned economics, MA risk adjustment strategy, DJ Patil's clinical AI philosophy. This is the biggest gap given Cory's context.
 - **GLP-1 durability and adherence** — 1 claim on launch size, nothing on weight regain, adherence cliffs, or behavioral vs. pharmacological intervention tradeoffs.
 - **Behavioral health infrastructure** — mental health supply gap covered, but nothing on measurement-based care, collaborative care models, or psychedelic therapy pathways.
 - **Provider consolidation** — anti-payvidor legislation covered, but nothing on Optum/UHG vertical integration mechanics, provider burnout economics, or independent practice viability.
 - **Global health systems** — zero claims. No comparative health system analysis (NHS, Singapore, Nordic models). US-centric.
 - **Genomics/precision medicine** — gene editing and mRNA vaccines covered, but nothing on polygenic risk scores, pharmacogenomics, or population-level genomic screening.
 - **Health equity** — SDOH and deaths of despair touch this, but no explicit claims about structural racism in healthcare, maternal mortality disparities, or rural access gaps.
 ## Confidence
 **Distribution:**
 | Level | Count | % |
 |-------|-------|---|
 | Proven | 7 | 16% |
 | Likely | 37 | 82% |
 | Experimental | 1 | 2% |
 | Speculative | 0 | 0% |
 **Assessment: likely-heavy, speculative-absent.** This is a problem. 82% of claims at the same confidence level means the label isn't doing much work. Either I'm genuinely well-calibrated on 37 claims (unlikely — some of these should be experimental or speculative) or I'm defaulting to "likely" as a comfortable middle.
 Specific concerns:
 - **Probably overconfident:** "healthcare AI creates a Jevons paradox" (likely) — this is a structural analogy applied to healthcare, not empirically demonstrated in this domain. Should be experimental.
 - **Probably overconfident:** "the healthcare attractor state is a prevention-first system..." (likely) — this is a derived prediction, not an observed trend. Should be experimental or speculative.
 - **Probably overconfident:** "the physician role shifts from information processor to relationship manager" (likely) — directionally right but the timeline and mechanism are speculative. Evidence is thin.
 - **Probably underconfident:** "AI scribes reached 92% provider adoption" (likely) — this has hard data. Could be proven.
 - **0 speculative claims is wrong.** I have views about where healthcare is going that I haven't written down because they'd be speculative. That's a gap, not discipline. The knowledge base should represent the full confidence spectrum, including bets.
 ## Sources
 **Count:** ~114 unique sources across 45 claims. Ratio of ~2.5 sources per claim is healthy.
 **Diversity assessment:**
 - **Strong:** Mix of peer-reviewed (JAMA, Lancet, NEJM Catalyst), industry reports (Bessemer, Rock Health, Grand View Research), regulatory documents (FDA, CMS), business filings, and journalism (STAT News, Healthcare Dive).
 - **Weak:** No primary interviews or original data. No international sources (WHO mentioned once, no Lancet Global Health, no international health system analyses). Over-indexed on US healthcare.
 - **Source monoculture risk:** Bessemer State of Health AI 2026 sourced 5 claims in one extraction. Not a problem yet, but if I keep pulling multiple claims from single sources, I'll inherit their framing biases.
 - **Missing source types:** No patient perspective sources. No provider survey data beyond adoption rates. No health economics modeling (no QALY analyses, no cost-effectiveness studies). No actuarial data despite covering MA and VBC.
 ## Staleness
 **All 45 claims created 2026-02-15 to 2026-03-08.** Nothing is stale yet — the domain was seeded 3 weeks ago.
 **What will go stale fastest:**
 - CMS regulatory claims (2027 chart review exclusion, AI reimbursement codes) — regulatory landscape shifts quarterly.
 - Funding pattern claims (winner-take-most, cash-pay adoption) — dependent on 2025-2026 funding data that will be superseded.
 - Devoted growth rate (121%) — single data point, needs updating with each earnings cycle.
 - GLP-1 market data — this category is moving weekly.
 **Structural staleness risk:** I have no refresh mechanism. No source watchlist, no trigger for "this claim's evidence base has changed." The vital signs spec addresses this (evidence freshness metric) but it's not built yet.
 ## Connections
 **Cross-domain link count:** 34+ distinct cross-domain wiki links across 45 claims.
 **Well-connected to:**
 - `core/grand-strategy/` — attractor states, proxy inertia, disruption theory, bottleneck positions. Healthcare maps naturally to grand strategy frameworks.
 - `foundations/critical-systems/` — CAS theory, clockwork paradigm, Jevons paradox. Healthcare IS a complex adaptive system.
 - `foundations/collective-intelligence/` — coordination failures, principal-agent problems. Healthcare incentive misalignment is a coordination failure.
 - `domains/space-development/` — one link (killer app sequence). Thin but real.
 **Poorly connected to:**
 - `domains/entertainment/` — zero links. There should be connections: content-as-loss-leader parallels wellness-as-loss-leader, fan engagement ladders parallel patient engagement, creator economy parallels provider autonomy.
 - `domains/internet-finance/` — zero direct links. Should connect: futarchy for health policy decisions, prediction markets for clinical trial outcomes, token economics for health behavior incentives.
 - `domains/ai-alignment/` — one indirect link (emergent misalignment). Should connect: clinical AI safety, HITL degradation as alignment problem, AI autonomy in medical decisions.
 - `foundations/cultural-dynamics/` — zero links. Should connect: health behavior as cultural contagion, deaths of despair as memetic collapse, wellness culture as memeplex.
 **Self-assessment:** My cross-domain ratio looks decent (34 links) but it's concentrated in grand-strategy and critical-systems. The other three domains are essentially unlinked. This is exactly the siloing my linkage density vital sign is designed to detect.
 ## Tensions
 **Unresolved contradictions in the knowledge base:**
 1. **HITL paradox:** "human-in-the-loop clinical AI degrades to worse-than-AI-alone" vs. the collective's broader commitment to human-in-the-loop architecture. If HITL degrades in clinical settings, does it degrade in knowledge work too? Theseus's coordination claims assume HITL works. My clinical evidence says it doesn't — at least not in the way people assume.
 2. **Jevons paradox vs. attractor state:** I claim healthcare AI creates a Jevons paradox (more capacity → more sick care demand) AND that the attractor state is prevention-first. If the Jevons paradox holds, what breaks the loop? My implicit answer is "aligned payment" but I haven't written the claim that connects these.
 3. **Complexity vs. simple rules:** I claim healthcare is a CAS requiring simple enabling rules, but my coverage of regulatory and legislative detail (CMS codes, anti-payvidor bills, FDA pathways) implies that the devil is in the complicated details, not simple rules. Am I contradicting myself or is the resolution that simple rules require complicated implementation?
 4. **Provider autonomy:** "healthcare is a CAS requiring simple enabling rules not complicated management because standardized processes erode clinical autonomy" sits in tension with "AI scribes reached 92% adoption" — scribes ARE standardized processes. Resolution may be that automation ≠ standardization, but I haven't articulated this.
 ## Gaps
 **Questions I should be able to answer but can't:**
 1. **What is Devoted Health's actual clinical AI architecture?** I cover the growth rate but not the mechanism. How does Orinoco work? What's the care model? How do they use AI differently from Optum/Humana?
 2. **What's the cost-effectiveness of prevention vs. treatment?** I assert prevention-first is the attractor state but have no cost-effectiveness data. No QALYs, no NNT comparisons, no actuarial modeling.
 3. **How does value-based care actually work financially?** I say VBC stalls at the payment boundary but I can't explain the mechanics of risk adjustment, MLR calculations, or how capitation contracts are structured.
 4. **What's the evidence base for health behavior change?** I have claims about deaths of despair and social isolation but nothing about what actually changes health behavior — nudge theory, habit formation, community-based interventions, financial incentives.
 5. **How do other countries' health systems handle the transitions I describe?** Singapore's 3M system, NHS integrated care, Nordic prevention models — all absent.
 6. **What's the realistic timeline for the attractor state?** I describe where healthcare must go but have no claims about how long the transition takes or what the intermediate states look like.
 7. **What does the clinical AI safety evidence actually show?** Beyond HITL degradation, what do we know about AI diagnostic errors, liability frameworks, malpractice implications, and patient trust?
--- a/inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md
+++ b/inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md
@ -0,0 +1,30 @@
 ---
 type: source
 title: "CLIs are exciting because they're legacy technology — AI agents can natively use them, combine them, interact via terminal"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2026360908398862478
 date: 2026-02-24
 domain: ai-alignment
 secondary_domains: [teleological-economics]
 format: tweet
 status: unprocessed
 priority: medium
 tags: [cli, agents, terminal, developer-tools, legacy-systems]
 ---
 ## Content
 CLIs are super exciting precisely because they are a "legacy" technology, which means AI agents can natively and easily use them, combine them, interact with them via the entire terminal toolkit.
 E.g ask your Claude/Codex agent to install this new Polymarket CLI and ask for any arbitrary dashboards or interfaces or logic. The agents will build it for you. Install the Github CLI too and you can ask them to navigate the repo, see issues, PRs, discussions, even the code itself.
 ## Agent Notes
 **Why this matters:** 11.7K likes. This is the theoretical justification for why Claude Code (CLI-based) is structurally advantaged over GUI-based AI interfaces. Legacy text protocols are more agent-friendly than modern visual interfaces. This is relevant to our own architecture — the agents work through git CLI, Forgejo API, terminal tools.
 **KB connections:** Validates our architectural choice of CLI-based agent coordination. Connects to [[collaborative knowledge infrastructure requires separating the versioning problem from the knowledge evolution problem because git solves file history but not semantic disagreement]].
 **Extraction hints:** Claim: legacy text-based interfaces (CLIs) are structurally more accessible to AI agents than modern GUI interfaces because they were designed for composability and programmatic interaction.
 **Context:** Karpathy explicitly mentions Claude and Polymarket CLI — connecting AI agents with prediction markets through terminal tools. Relevant to the Teleo stack.
--- a/inbox/archive/2026-02-25-karpathy-programming-changed-december.md
+++ b/inbox/archive/2026-02-25-karpathy-programming-changed-december.md
@ -0,0 +1,28 @@
 ---
 type: source
 title: "Programming fundamentally changed in December 2025 — coding agents basically didn't work before and basically work since"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2026731645169185220
 date: 2026-02-25
 domain: ai-alignment
 secondary_domains: [teleological-economics]
 format: tweet
 status: unprocessed
 priority: medium
 tags: [coding-agents, ai-capability, phase-transition, software-development, disruption]
 ---
 ## Content
 It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn't work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.
 ## Agent Notes
 **Why this matters:** 37K likes — Karpathy's most viral tweet in this dataset. This is the "phase transition" observation from the most authoritative voice in AI dev tooling. December 2025 as the inflection point for coding agents.
 **KB connections:** Supports [[as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build]]. Relates to [[the gap between theoretical AI capability and observed deployment is massive across all occupations]] — but suggests the gap is closing fast for software specifically.
 **Extraction hints:** Claim candidate: coding agent capability crossed a usability threshold in December 2025, representing a phase transition not gradual improvement. Evidence: Karpathy's direct experience running agents on nanochat.
 **Context:** This tweet preceded the autoresearch project by ~10 days. The 37K likes suggest massive resonance across the developer community. The "asterisks" he mentions are important qualifiers that a good extraction should preserve.
--- a/inbox/archive/2026-02-27-karpathy-8-agent-research-org.md
+++ b/inbox/archive/2026-02-27-karpathy-8-agent-research-org.md
@ -0,0 +1,44 @@
 ---
 type: source
 title: "8-agent research org experiments reveal agents generate bad ideas but execute well — the source code is now the org design"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2027521323275325622
 date: 2026-02-27
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: tweet
 status: unprocessed
 priority: high
 tags: [multi-agent, research-org, agent-collaboration, prompt-engineering, organizational-design]
 flagged_for_theseus: ["Multi-model collaboration evidence — 8 agents, different setups, empirical failure modes"]
 ---
 ## Content
 I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :)
 I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p.
 But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them.
 But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?
 ## Agent Notes
 **Why this matters:** This is empirical evidence from the most credible source possible (Karpathy, running 8 agents on real GPU tasks) about what multi-agent collaboration actually looks like today. Key finding: agents execute well but generate bad ideas. They don't do experiment design, don't control for confounds, don't think critically. This is EXACTLY why our adversarial review pipeline matters — without it, agents accumulate spurious results.
 **KB connections:**
 - Validates [[AI capability and reliability are independent dimensions]] — agents can implement perfectly but reason poorly about what to implement
 - Validates [[adversarial PR review produces higher quality knowledge than self-review]] — Karpathy had to manually catch a spurious result the agent couldn't see
 - The "source code is the org design" framing is exactly what Pentagon is: prompts, skills, tools, processes as organizational architecture
 - Connects to [[coordination protocol design produces larger capability gains than model scaling]] — same agents, different org structure, different results
 - His 4 claude + 4 codex setup is evidence for [[all agents running the same model family creates correlated blind spots]]
 **Extraction hints:**
 - Claim: AI agents execute well-scoped tasks reliably but generate poor research hypotheses — the bottleneck is idea generation not implementation
 - Claim: multi-agent research orgs are now programmable organizations where the source code is prompts, skills, tools and processes
 - Claim: different organizational structures (solo vs hierarchical) produce different research outcomes with identical agents
 - Claim: agents fail at experimental methodology (confound control, baseline comparison, ablation) even at highest intelligence settings
 **Context:** Follow-up to the autoresearch SETI@home tweet. Karpathy tried multiple org structures: 8 independent, 1 chief + 8 juniors, etc. Used git worktrees for isolation (we use the same pattern in Pentagon). This is the most detailed public account of someone running a multi-agent research organization.
--- a/inbox/archive/2026-03-04-theiaresearch-permissionless-metadao-launches.md
+++ b/inbox/archive/2026-03-04-theiaresearch-permissionless-metadao-launches.md
@ -0,0 +1,39 @@
 ---
 type: source
 title: "Permissionless MetaDAO launches create new cultural primitives around fundraising"
 author: "Felipe Montealegre (@TheiaResearch)"
 twitter_id: "1511793131884318720"
 url: https://x.com/TheiaResearch/status/2029231349425684521
 date: 2026-03-04
 domain: internet-finance
 format: tweet
 status: unprocessed
 priority: high
 tags: [metadao, futardio, fundraising, permissionless-launch, capital-formation]
 ---
 ## Content
 Permissionless MetaDAO launches will lead to entirely different cultural primitives around fundraising.
 1. Continuous Fundraising: It only takes a few days to fundraise so don't take more than you need
 2. Liquidation Pivot: You built an MVP but didn't find product-market fit and now you have been liquidated. Try again on another product or strategy.
 3. Multiple Attempts: You didn't fill your minimum raise? Speak to some investors, build out an MVP, put together a deck, and come back in ~3 weeks.
 4. Public on Day 1: Communicating with markets and liquid investors is a core founder skillset.
 5. 10x Upside Case: Many companies with 5-10x upside case outcomes don't get funded right now because venture funds all want venture outcomes (>100x on $20M). What if you just want to build a $25M company with a decent probability of success? Raise $1M and the math works fine for Futardio investors.
 Futardio is a paradigm shift for capital markets. We will fund you - quickly and efficiently - and give you community support but you are public and accountable from day one. Welcome to the arena.
 ## Agent Notes
 **Why this matters:** This is the clearest articulation yet of how permissionless futarchy-governed launches create fundamentally different founder behavior — not just faster fundraising but different cultural norms (continuous raises, liquidation as pivot, public accountability from day 1).
 **KB connections:** Directly extends [[internet capital markets compress fundraising from months to days]] and [[futarchy-governed liquidation is the enforcement mechanism that makes unruggable ICOs credible]]. The "10x upside case" point challenges the VC model — connects to [[cryptos primary use case is capital formation not payments or store of value]].
 **Extraction hints:** At least 2-3 claims here: (1) permissionless launches create new fundraising cultural norms, (2) the 10x upside gap in traditional VC is a market failure that futarchy-governed launches solve, (3) public accountability from day 1 is a feature not a bug.
 **Context:** Felipe Montealegre runs Theia Research, a crypto-native investment firm focused on MetaDAO ecosystem. He's been one of the most articulate proponents of the futarchy-governed capital formation thesis. This tweet got 118 likes — high engagement for crypto-finance X.
--- a/inbox/archive/2026-03-08-karpathy-autoresearch-collaborative-agents.md
+++ b/inbox/archive/2026-03-08-karpathy-autoresearch-collaborative-agents.md
@ -0,0 +1,47 @@
 ---
 type: source
 title: "Autoresearch must become asynchronously massively collaborative for agents — emulating a research community, not a single PhD student"
 author: "Andrej Karpathy (@karpathy)"
 twitter_id: "33836629"
 url: https://x.com/karpathy/status/2030705271627284816
 date: 2026-03-08
 domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: tweet
 status: unprocessed
 priority: high
 tags: [autoresearch, multi-agent, git-coordination, collective-intelligence, agent-collaboration]
 flagged_for_theseus: ["Core AI agent coordination architecture — directly relevant to multi-model collaboration claims"]
 flagged_for_leo: ["Cross-domain synthesis — this is what we're building with the Teleo collective"]
 ---
 ## Content
 The next step for autoresearch is that it has to be asynchronously massively collaborative for agents (think: SETI@home style). The goal is not to emulate a single PhD student, it's to emulate a research community of them.
 Current code synchronously grows a single thread of commits in a particular research direction. But the original repo is more of a seed, from which could sprout commits contributed by agents on all kinds of different research directions or for different compute platforms. Git(Hub) is *almost* but not really suited for this. It has a softly built in assumption of one "master" branch, which temporarily forks off into PRs just to merge back a bit later.
 I tried to prototype something super lightweight that could have a flavor of this, e.g. just a Discussion, written by my agent as a summary of its overnight run:
 https://t.co/tmZeqyDY1W
 Alternatively, a PR has the benefit of exact commits:
 https://t.co/CZIbuJIqlk
 but you'd never want to actually merge it... You'd just want to "adopt" and accumulate branches of commits. But even in this lightweight way, you could ask your agent to first read the Discussions/PRs using GitHub CLI for inspiration, and after its research is done, contribute a little "paper" of findings back.
 I'm not actually exactly sure what this should look like, but it's a big idea that is more general than just the autoresearch repo specifically. Agents can in principle easily juggle and collaborate on thousands of commits across arbitrary branch structures. Existing abstractions will accumulate stress as intelligence, attention and tenacity cease to be bottlenecks.
 ## Agent Notes
 **Why this matters:** Karpathy (3M+ followers, former Tesla AI director) is independently arriving at the same architecture we're building with the Teleo collective — agents coordinating through git, PRs as knowledge contributions, branches as research directions. His framing of "emulate a research community, not a single PhD student" IS our thesis. And his observation that Git's assumptions break under agent-scale collaboration is a problem we're actively solving.
 **KB connections:**
 - Directly validates [[coordination protocol design produces larger capability gains than model scaling]]
 - Challenges/extends [[the same coordination protocol applied to different AI models produces radically different problem-solving strategies]] — Karpathy found that 8 agents with different setups (solo vs hierarchical) produced different results
 - Relevant to [[domain specialization with cross-domain synthesis produces better collective intelligence]]
 - His "existing abstractions will accumulate stress" connects to the git-as-coordination-substrate thesis
 **Extraction hints:**
 - Claim: agent research communities outperform single-agent research because the goal is to emulate a community not an individual
 - Claim: git's branch-merge model is insufficient for agent-scale collaboration because it assumes one master branch with temporary forks
 - Claim: when intelligence and attention cease to be bottlenecks, existing coordination abstractions (git, PRs, branches) accumulate stress
 **Context:** This is part of a series of tweets about karpathy's autoresearch project — AI agents autonomously iterating on nanochat (minimal GPT training code). He's running multiple agents on GPU clusters doing automated ML research. The Feb 27 thread about 8 agents is critical companion reading (separate source).
--- a/ops/evaluate-trigger.sh
+++ b/ops/evaluate-trigger.sh
@ -6,8 +6,8 @@
 #   2. Domain agent — domain expertise, duplicate check, technical accuracy
 #
 # After both reviews, auto-merges if:
-#   - Leo approved (gh pr review --approve)
+#   - Leo's comment contains "**Verdict:** approve"
-#   - Domain agent verdict is "Approve" (parsed from comment)
+#   - Domain agent's comment contains "**Verdict:** approve"
 #   - No territory violations (files outside proposer's domain)
 #
 # Usage:
@ -26,8 +26,14 @@
 #   - Lockfile prevents concurrent runs
 #   - Auto-merge requires ALL reviewers to approve + no territory violations
 #   - Each PR runs sequentially to avoid branch conflicts
-#   - Timeout: 10 minutes per agent per PR
+#   - Timeout: 20 minutes per agent per PR
 #   - Pre-flight checks: clean working tree, gh auth
 #
 # Verdict protocol:
 #   All agents use `gh pr comment` (NOT `gh pr review`) because all agents
 #   share the m3taversal GitHub account — `gh pr review --approve` fails
 #   when the PR author and reviewer are the same user. The merge check
 #   parses issue comments for structured verdict markers instead.
 set -euo pipefail
@ -39,7 +45,7 @@ cd "$REPO_ROOT"
 LOCKFILE="/tmp/evaluate-trigger.lock"
 LOG_DIR="$REPO_ROOT/ops/sessions"
-TIMEOUT_SECONDS=600
+TIMEOUT_SECONDS=1200
 DRY_RUN=false
 LEO_ONLY=false
 NO_MERGE=false
@ -62,8 +68,17 @@ detect_domain_agent() {
    vida/*|*/health*)          agent="vida"; domain="health" ;;
    astra/*|*/space-development*) agent="astra"; domain="space-development" ;;
    leo/*|*/grand-strategy*)   agent="leo"; domain="grand-strategy" ;;
    contrib/*)
      # External contributor — detect domain from changed files (fall through to file check)
      agent=""; domain=""
      ;;
    *)
-      # Fall back to checking which domain directory has changed files
+      agent=""; domain=""
      ;;
  esac
  # If no agent detected from branch prefix, check changed files
  if [ -z "$agent" ]; then
    if echo "$files" | grep -q "domains/internet-finance/"; then
      agent="rio"; domain="internet-finance"
    elif echo "$files" | grep -q "domains/entertainment/"; then
@ -74,11 +89,8 @@ detect_domain_agent() {
      agent="vida"; domain="health"
    elif echo "$files" | grep -q "domains/space-development/"; then
      agent="astra"; domain="space-development"
      else
        agent=""; domain=""
    fi
-      ;;
+  fi
  esac
  echo "$agent $domain"
 }
@ -112,8 +124,8 @@ if ! command -v claude >/dev/null 2>&1; then
  exit 1
 fi
-# Check for dirty working tree (ignore ops/ and .claude/ which may contain uncommitted scripts)
+# Check for dirty working tree (ignore ops/, .claude/, .github/ which may contain local-only files)
-DIRTY_FILES=$(git status --porcelain | grep -v '^?? ops/' | grep -v '^ M ops/' | grep -v '^?? \.claude/' | grep -v '^ M \.claude/' || true)
+DIRTY_FILES=$(git status --porcelain | grep -v '^?? ops/' | grep -v '^ M ops/' | grep -v '^?? \.claude/' | grep -v '^ M \.claude/' | grep -v '^?? \.github/' | grep -v '^ M \.github/' || true)
 if [ -n "$DIRTY_FILES" ]; then
  echo "ERROR: Working tree is dirty. Clean up before running."
  echo "$DIRTY_FILES"
@ -145,7 +157,8 @@ if [ -n "$SPECIFIC_PR" ]; then
  fi
  PRS_TO_REVIEW="$SPECIFIC_PR"
 else
-  OPEN_PRS=$(gh pr list --state open --json number --jq '.[].number' 2>/dev/null || echo "")
+  # NOTE: gh pr list silently returns empty in some worktree configs; use gh api instead
  OPEN_PRS=$(gh api repos/:owner/:repo/pulls --jq '.[].number' 2>/dev/null || echo "")
  if [ -z "$OPEN_PRS" ]; then
    echo "No open PRs found. Nothing to review."
@ -154,17 +167,23 @@ else
  PRS_TO_REVIEW=""
  for pr in $OPEN_PRS; do
-    LAST_REVIEW_DATE=$(gh api "repos/{owner}/{repo}/pulls/$pr/reviews" \
+    # Check if this PR already has a Leo verdict comment (avoid re-reviewing)
-      --jq 'map(select(.state != "DISMISSED")) | sort_by(.submitted_at) | last | .submitted_at' 2>/dev/null || echo "")
+    LEO_COMMENTED=$(gh pr view "$pr" --json comments \
      --jq '[.comments[] | select(.body | test("VERDICT:LEO:(APPROVE|REQUEST_CHANGES)"))] | length' 2>/dev/null || echo "0")
    LAST_COMMIT_DATE=$(gh pr view "$pr" --json commits --jq '.commits[-1].committedDate' 2>/dev/null || echo "")
-    if [ -z "$LAST_REVIEW_DATE" ]; then
+    if [ "$LEO_COMMENTED" = "0" ]; then
      PRS_TO_REVIEW="$PRS_TO_REVIEW $pr"
-    elif [ -n "$LAST_COMMIT_DATE" ] && [[ "$LAST_COMMIT_DATE" > "$LAST_REVIEW_DATE" ]]; then
+    else
      # Check if new commits since last Leo review
      LAST_LEO_DATE=$(gh pr view "$pr" --json comments \
        --jq '[.comments[] | select(.body | test("VERDICT:LEO:")) | .createdAt] | last' 2>/dev/null || echo "")
      if [ -n "$LAST_COMMIT_DATE" ] && [ -n "$LAST_LEO_DATE" ] && [[ "$LAST_COMMIT_DATE" > "$LAST_LEO_DATE" ]]; then
        echo "PR #$pr: New commits since last review. Queuing for re-review."
        PRS_TO_REVIEW="$PRS_TO_REVIEW $pr"
      else
-      echo "PR #$pr: No new commits since last review. Skipping."
+        echo "PR #$pr: Already reviewed. Skipping."
      fi
    fi
  done
@ -195,7 +214,7 @@ run_agent_review() {
  log_file="$LOG_DIR/${agent_name}-review-pr${pr}-${timestamp}.log"
  review_file="/tmp/${agent_name}-review-pr${pr}.md"
-  echo "  Running ${agent_name}..."
+  echo "  Running ${agent_name} (model: ${model})..."
  echo "  Log: $log_file"
  if perl -e "alarm $TIMEOUT_SECONDS; exec @ARGV" claude -p \
@ -240,6 +259,7 @@ check_territory_violations() {
    vida)    allowed_domains="domains/health/" ;;
    astra)   allowed_domains="domains/space-development/" ;;
    leo)     allowed_domains="core/|foundations/" ;;
    contrib) echo ""; return 0 ;;  # External contributors — skip territory check
    *)       echo ""; return 0 ;;  # Unknown proposer — skip check
  esac
@ -266,74 +286,51 @@ check_territory_violations() {
 }
 # --- Auto-merge check ---
-# Returns 0 if PR should be merged, 1 if not
+# Parses issue comments for structured verdict markers.
 # Verdict protocol: agents post `<!-- VERDICT:AGENT_KEY:APPROVE -->` or
 # `<!-- VERDICT:AGENT_KEY:REQUEST_CHANGES -->` as HTML comments in their review.
 # This is machine-parseable and invisible in the rendered comment.
 check_merge_eligible() {
  local pr_number="$1"
  local domain_agent="$2"
  local leo_passed="$3"
-  # Gate 1: Leo must have passed
+  # Gate 1: Leo must have completed without timeout/error
  if [ "$leo_passed" != "true" ]; then
    echo "BLOCK: Leo review failed or timed out"
    return 1
  fi
-  # Gate 2: Check Leo's review state via GitHub API
+  # Gate 2: Check Leo's verdict from issue comments
  local leo_review_state
  leo_review_state=$(gh api "repos/{owner}/{repo}/pulls/${pr_number}/reviews" \
    --jq '[.[] | select(.state != "DISMISSED" and .state != "PENDING")] | last | .state' 2>/dev/null || echo "")
  if [ "$leo_review_state" = "APPROVED" ]; then
    echo "Leo: APPROVED (via review API)"
  elif [ "$leo_review_state" = "CHANGES_REQUESTED" ]; then
    echo "BLOCK: Leo requested changes (review API state: CHANGES_REQUESTED)"
    return 1
  else
    # Fallback: check PR comments for Leo's verdict
  local leo_verdict
  leo_verdict=$(gh pr view "$pr_number" --json comments \
-      --jq '.comments[] | select(.body | test("## Leo Review")) | .body' 2>/dev/null \
+    --jq '[.comments[] | select(.body | test("VERDICT:LEO:")) | .body] | last' 2>/dev/null || echo "")
      | grep -oiE '\*\*Verdict:[^*]+\*\*' | tail -1 || echo "")
-    if echo "$leo_verdict" | grep -qi "approve"; then
+  if echo "$leo_verdict" | grep -q "VERDICT:LEO:APPROVE"; then
-      echo "Leo: APPROVED (via comment verdict)"
+    echo "Leo: APPROVED"
-    elif echo "$leo_verdict" | grep -qi "request changes\|reject"; then
+  elif echo "$leo_verdict" | grep -q "VERDICT:LEO:REQUEST_CHANGES"; then
-      echo "BLOCK: Leo verdict: $leo_verdict"
+    echo "BLOCK: Leo requested changes"
    return 1
  else
-      echo "BLOCK: Could not determine Leo's verdict"
+    echo "BLOCK: Could not find Leo's verdict marker in PR comments"
    return 1
  fi
  fi
  # Gate 3: Check domain agent verdict (if applicable)
  if [ -n "$domain_agent" ] && [ "$domain_agent" != "leo" ]; then
    local domain_key
    domain_key=$(echo "$domain_agent" | tr '[:lower:]' '[:upper:]')
    local domain_verdict
    # Search for verdict in domain agent's review — match agent name, "domain reviewer", or "Domain Review"
    domain_verdict=$(gh pr view "$pr_number" --json comments \
-      --jq ".comments[] | select(.body | test(\"domain review|${domain_agent}|peer review\"; \"i\")) | .body" 2>/dev/null \
+      --jq "[.comments[] | select(.body | test(\"VERDICT:${domain_key}:\")) | .body] | last" 2>/dev/null || echo "")
      | grep -oiE '\*\*Verdict:[^*]+\*\*' | tail -1 || echo "")
-    if [ -z "$domain_verdict" ]; then
+    if echo "$domain_verdict" | grep -q "VERDICT:${domain_key}:APPROVE"; then
-      # Also check review API for domain agent approval
+      echo "Domain agent ($domain_agent): APPROVED"
-      # Since all agents use the same GitHub account, we check for multiple approvals
+    elif echo "$domain_verdict" | grep -q "VERDICT:${domain_key}:REQUEST_CHANGES"; then
-      local approval_count
+      echo "BLOCK: $domain_agent requested changes"
      approval_count=$(gh api "repos/{owner}/{repo}/pulls/${pr_number}/reviews" \
        --jq '[.[] | select(.state == "APPROVED")] | length' 2>/dev/null || echo "0")
      if [ "$approval_count" -ge 2 ]; then
        echo "Domain agent: APPROVED (multiple approvals via review API)"
      else
        echo "BLOCK: No domain agent verdict found"
        return 1
      fi
    elif echo "$domain_verdict" | grep -qi "approve"; then
      echo "Domain agent ($domain_agent): APPROVED (via comment verdict)"
    elif echo "$domain_verdict" | grep -qi "request changes\|reject"; then
      echo "BLOCK: Domain agent verdict: $domain_verdict"
      return 1
    else
-      echo "BLOCK: Unclear domain agent verdict: $domain_verdict"
+      echo "BLOCK: No verdict marker found for $domain_agent"
      return 1
    fi
  else
@ -403,11 +400,15 @@ Also check:
 - Cross-domain connections that the proposer may have missed
 Write your complete review to ${LEO_REVIEW_FILE}
 Then post it with: gh pr review ${pr} --comment --body-file ${LEO_REVIEW_FILE}
-If ALL claims pass quality gates: gh pr review ${pr} --approve --body-file ${LEO_REVIEW_FILE}
+CRITICAL — Verdict format: Your review MUST end with exactly one of these verdict markers (as an HTML comment on its own line):
-If ANY claim needs changes: gh pr review ${pr} --request-changes --body-file ${LEO_REVIEW_FILE}
+  <!-- VERDICT:LEO:APPROVE -->
  <!-- VERDICT:LEO:REQUEST_CHANGES -->
 Then post the review as an issue comment:
  gh pr comment ${pr} --body-file ${LEO_REVIEW_FILE}
 IMPORTANT: Use 'gh pr comment' NOT 'gh pr review'. We use a shared GitHub account so gh pr review --approve fails.
 DO NOT merge — the orchestrator handles merge decisions after all reviews are posted.
 Work autonomously. Do not ask for confirmation."
@ -432,6 +433,7 @@ Work autonomously. Do not ask for confirmation."
  else
    DOMAIN_REVIEW_FILE="/tmp/${DOMAIN_AGENT}-review-pr${pr}.md"
    AGENT_NAME_UPPER=$(echo "${DOMAIN_AGENT}" | awk '{print toupper(substr($0,1,1)) substr($0,2)}')
    AGENT_KEY_UPPER=$(echo "${DOMAIN_AGENT}" | tr '[:lower:]' '[:upper:]')
    DOMAIN_PROMPT="You are ${AGENT_NAME_UPPER}. Read agents/${DOMAIN_AGENT}/identity.md, agents/${DOMAIN_AGENT}/beliefs.md, and skills/evaluate.md.
 You are reviewing PR #${pr} as the domain expert for ${DOMAIN}.
@ -452,8 +454,15 @@ Your review focuses on DOMAIN EXPERTISE — things only a ${DOMAIN} specialist w
 6. **Confidence calibration** — From your domain expertise, is the confidence level right?
 Write your review to ${DOMAIN_REVIEW_FILE}
 Post it with: gh pr review ${pr} --comment --body-file ${DOMAIN_REVIEW_FILE}
 CRITICAL — Verdict format: Your review MUST end with exactly one of these verdict markers (as an HTML comment on its own line):
  <!-- VERDICT:${AGENT_KEY_UPPER}:APPROVE -->
  <!-- VERDICT:${AGENT_KEY_UPPER}:REQUEST_CHANGES -->
 Then post the review as an issue comment:
  gh pr comment ${pr} --body-file ${DOMAIN_REVIEW_FILE}
 IMPORTANT: Use 'gh pr comment' NOT 'gh pr review'. We use a shared GitHub account so gh pr review --approve fails.
 Sign your review as ${AGENT_NAME_UPPER} (domain reviewer for ${DOMAIN}).
 DO NOT duplicate Leo's quality gate checks — he covers those.
 DO NOT merge — the orchestrator handles merge decisions after all reviews are posted.
@ -486,7 +495,7 @@ Work autonomously. Do not ask for confirmation."
    if [ "$MERGE_RESULT" -eq 0 ]; then
      echo "  Auto-merge: ALL GATES PASSED — merging PR #$pr"
-      if gh pr merge "$pr" --squash --delete-branch 2>&1; then
+      if gh pr merge "$pr" --squash 2>&1; then
        echo "  PR #$pr: MERGED successfully."
        MERGED=$((MERGED + 1))
      else
--- a/ops/extract-cron.sh
+++ b/ops/extract-cron.sh
@ -0,0 +1,179 @@
 #!/bin/bash
 # Extract claims from unprocessed sources in inbox/archive/
 # Runs via cron on VPS every 15 minutes.
 #
 # Concurrency model:
 #   - Lockfile prevents overlapping runs
 #   - MAX_SOURCES=5 per cycle (works through backlog over multiple runs)
 #   - Sequential processing (one source at a time)
 #   - 50 sources landing at once = ~10 cron cycles to clear, not 50 parallel agents
 #
 # Domain routing:
 #   - Reads domain: field from source frontmatter
 #   - Maps to the domain agent (rio, clay, theseus, vida, astra, leo)
 #   - Runs extraction AS that agent — their territory, their extraction
 #   - Skips sources with status: processing (agent handling it themselves)
 #
 # Flow:
 #   1. Pull latest main
 #   2. Find sources with status: unprocessed (skip processing/processed/null-result)
 #   3. For each: run Claude headless to extract claims as the domain agent
 #   4. Commit extractions, push, open PR
 #   5. Update source status to processed
 #
 # The eval pipeline (webhook.py) handles review and merge separately.
 set -euo pipefail
 REPO_DIR="/opt/teleo-eval/workspaces/extract"
 REPO_URL="http://m3taversal:$(cat /opt/teleo-eval/secrets/forgejo-admin-token)@localhost:3000/teleo/teleo-codex.git"
 CLAUDE_BIN="/home/teleo/.local/bin/claude"
 LOG_DIR="/opt/teleo-eval/logs"
 LOG="$LOG_DIR/extract-cron.log"
 LOCKFILE="/tmp/extract-cron.lock"
 MAX_SOURCES=5  # Process at most 5 sources per run to limit cost
 log() { echo "[$(date -Iseconds)] $*" >> "$LOG"; }
 # --- Lock ---
 if [ -f "$LOCKFILE" ]; then
    pid=$(cat "$LOCKFILE" 2>/dev/null)
    if kill -0 "$pid" 2>/dev/null; then
        log "SKIP: already running (pid $pid)"
        exit 0
    fi
    log "WARN: stale lockfile, removing"
    rm -f "$LOCKFILE"
 fi
 echo $$ > "$LOCKFILE"
 trap 'rm -f "$LOCKFILE"' EXIT
 # --- Ensure repo clone ---
 if [ ! -d "$REPO_DIR/.git" ]; then
    log "Cloning repo..."
    git clone "$REPO_URL" "$REPO_DIR" >> "$LOG" 2>&1
 fi
 cd "$REPO_DIR"
 # --- Pull latest main ---
 git checkout main >> "$LOG" 2>&1
 git pull --rebase >> "$LOG" 2>&1
 # --- Find unprocessed sources ---
 UNPROCESSED=$(grep -rl '^status: unprocessed' inbox/archive/ 2>/dev/null | head -n "$MAX_SOURCES" || true)
 if [ -z "$UNPROCESSED" ]; then
    log "No unprocessed sources found"
    exit 0
 fi
 COUNT=$(echo "$UNPROCESSED" | wc -l | tr -d ' ')
 log "Found $COUNT unprocessed source(s)"
 # --- Process each source ---
 for SOURCE_FILE in $UNPROCESSED; do
    SLUG=$(basename "$SOURCE_FILE" .md)
    BRANCH="extract/$SLUG"
    log "Processing: $SOURCE_FILE → branch $BRANCH"
    # Create branch from main
    git checkout main >> "$LOG" 2>&1
    git branch -D "$BRANCH" 2>/dev/null || true
    git checkout -b "$BRANCH" >> "$LOG" 2>&1
    # Read domain from frontmatter
    DOMAIN=$(grep '^domain:' "$SOURCE_FILE" | head -1 | sed 's/domain: *//' | tr -d '"' | tr -d "'" | xargs)
    # Map domain to agent
    case "$DOMAIN" in
        internet-finance) AGENT="rio" ;;
        entertainment) AGENT="clay" ;;
        ai-alignment) AGENT="theseus" ;;
        health) AGENT="vida" ;;
        space-development) AGENT="astra" ;;
        *) AGENT="leo" ;;
    esac
    AGENT_TOKEN=$(cat "/opt/teleo-eval/secrets/forgejo-${AGENT}-token" 2>/dev/null || cat /opt/teleo-eval/secrets/forgejo-leo-token)
    log "Domain: $DOMAIN, Agent: $AGENT"
    # Run Claude headless to extract claims
    EXTRACT_PROMPT="You are $AGENT, a Teleo knowledge base agent. Extract claims from this source.
 READ these files first:
 - skills/extract.md (extraction process)
 - schemas/claim.md (claim format)
 - $SOURCE_FILE (the source to extract from)
 Then scan domains/$DOMAIN/ to check for duplicate claims.
 EXTRACT claims following the process in skills/extract.md:
 1. Read the source completely
 2. Separate evidence from interpretation
 3. Extract candidate claims (specific, disagreeable, evidence-backed)
 4. Check for duplicates against existing claims in domains/$DOMAIN/
 5. Write claim files to domains/$DOMAIN/ with proper YAML frontmatter
 6. Update $SOURCE_FILE: set status to 'processed', add processed_by: $AGENT, processed_date: $(date +%Y-%m-%d), and claims_extracted list
 If no claims can be extracted, update $SOURCE_FILE: set status to 'null-result' and add notes explaining why.
 IMPORTANT: Use the Edit tool to update the source file status. Use the Write tool to create new claim files. Do not create claims that duplicate existing ones."
    # Run extraction with timeout (10 minutes)
    timeout 600 "$CLAUDE_BIN" -p "$EXTRACT_PROMPT" \
        --allowedTools 'Read,Write,Edit,Glob,Grep' \
        --model sonnet \
        >> "$LOG" 2>&1 || {
        log "WARN: Claude extraction failed or timed out for $SOURCE_FILE"
        git checkout main >> "$LOG" 2>&1
        continue
    }
    # Check if any files were created/modified
    CHANGES=$(git status --porcelain | wc -l | tr -d ' ')
    if [ "$CHANGES" -eq 0 ]; then
        log "No changes produced for $SOURCE_FILE"
        git checkout main >> "$LOG" 2>&1
        continue
    fi
    # Stage and commit
    git add inbox/archive/ "domains/$DOMAIN/" >> "$LOG" 2>&1
    git commit -m "$AGENT: extract claims from $(basename "$SOURCE_FILE")
 - Source: $SOURCE_FILE
 - Domain: $DOMAIN
 - Extracted by: headless extraction cron
 Pentagon-Agent: $(echo "$AGENT" | sed 's/./\U&/') <HEADLESS>" >> "$LOG" 2>&1
    # Push branch
    git push -u "$REPO_URL" "$BRANCH" --force >> "$LOG" 2>&1
    # Open PR
    PR_TITLE="$AGENT: extract claims from $(basename "$SOURCE_FILE" .md)"
    PR_BODY="## Automated Extraction\n\nSource: \`$SOURCE_FILE\`\nDomain: $DOMAIN\nExtracted by: headless cron on VPS\n\nThis PR was created automatically by the extraction cron job. Claims were extracted using \`skills/extract.md\` process via Claude headless."
    curl -s -X POST "http://localhost:3000/api/v1/repos/teleo/teleo-codex/pulls" \
        -H "Authorization: token $AGENT_TOKEN" \
        -H "Content-Type: application/json" \
        -d "{
            \"title\": \"$PR_TITLE\",
            \"body\": \"$PR_BODY\",
            \"base\": \"main\",
            \"head\": \"$BRANCH\"
        }" >> "$LOG" 2>&1
    log "PR opened for $SOURCE_FILE"
    # Back to main for next source
    git checkout main >> "$LOG" 2>&1
    # Brief pause between extractions
    sleep 5
 done
 log "Extraction run complete: processed $COUNT source(s)"
--- a/ops/extract-graph-data.py
+++ b/ops/extract-graph-data.py
@ -0,0 +1,520 @@
 #!/usr/bin/env python3
 """
 extract-graph-data.py — Extract knowledge graph from teleo-codex markdown files.
 Reads all .md claim/conviction files, parses YAML frontmatter and wiki-links,
 and outputs graph-data.json matching the teleo-app GraphData interface.
 Usage:
    python3 ops/extract-graph-data.py [--output path/to/graph-data.json]
 Must be run from the teleo-codex repo root.
 """
 import argparse
 import json
 import os
 import re
 import subprocess
 import sys
 from datetime import datetime, timezone
 from pathlib import Path
 # ---------------------------------------------------------------------------
 # Config
 # ---------------------------------------------------------------------------
 SCAN_DIRS = ["core", "domains", "foundations", "convictions"]
 # Only extract these content types (from frontmatter `type` field).
 # If type is missing, include the file anyway (many claims lack explicit type).
 INCLUDE_TYPES = {"claim", "conviction", "analysis", "belief", "position", None}
 # Domain → default agent mapping (fallback when git attribution unavailable)
 DOMAIN_AGENT_MAP = {
    "internet-finance": "rio",
    "entertainment": "clay",
    "health": "vida",
    "ai-alignment": "theseus",
    "space-development": "astra",
    "grand-strategy": "leo",
    "mechanisms": "leo",
    "living-capital": "leo",
    "living-agents": "leo",
    "teleohumanity": "leo",
    "critical-systems": "leo",
    "collective-intelligence": "leo",
    "teleological-economics": "leo",
    "cultural-dynamics": "clay",
 }
 DOMAIN_COLORS = {
    "internet-finance": "#4A90D9",
    "entertainment": "#9B59B6",
    "health": "#2ECC71",
    "ai-alignment": "#E74C3C",
    "space-development": "#F39C12",
    "grand-strategy": "#D4AF37",
    "mechanisms": "#1ABC9C",
    "living-capital": "#3498DB",
    "living-agents": "#E67E22",
    "teleohumanity": "#F1C40F",
    "critical-systems": "#95A5A6",
    "collective-intelligence": "#BDC3C7",
    "teleological-economics": "#7F8C8D",
    "cultural-dynamics": "#C0392B",
 }
 KNOWN_AGENTS = {"leo", "rio", "clay", "vida", "theseus", "astra"}
 # Regex patterns
 FRONTMATTER_RE = re.compile(r"^---\s*\n(.*?)\n---", re.DOTALL)
 WIKILINK_RE = re.compile(r"\[\[([^\]]+)\]\]")
 YAML_FIELD_RE = re.compile(r"^(\w[\w_]*):\s*(.+)$", re.MULTILINE)
 YAML_LIST_ITEM_RE = re.compile(r'^\s*-\s+"?(.+?)"?\s*$', re.MULTILINE)
 COUNTER_EVIDENCE_RE = re.compile(r"^##\s+Counter[\s-]?evidence", re.MULTILINE | re.IGNORECASE)
 COUNTERARGUMENT_RE = re.compile(r"^\*\*Counter\s*argument", re.MULTILINE | re.IGNORECASE)
 # ---------------------------------------------------------------------------
 # Lightweight YAML-ish frontmatter parser (avoids PyYAML dependency)
 # ---------------------------------------------------------------------------
 def parse_frontmatter(text: str) -> dict:
    """Parse YAML frontmatter from markdown text. Returns dict of fields."""
    m = FRONTMATTER_RE.match(text)
    if not m:
        return {}
    yaml_block = m.group(1)
    result = {}
    for field_match in YAML_FIELD_RE.finditer(yaml_block):
        key = field_match.group(1)
        val = field_match.group(2).strip().strip('"').strip("'")
        # Handle list fields
        if val.startswith("["):
            # Inline YAML list: [item1, item2]
            items = re.findall(r'"([^"]+)"', val)
            if not items:
                items = [x.strip().strip('"').strip("'")
                         for x in val.strip("[]").split(",") if x.strip()]
            result[key] = items
        else:
            result[key] = val
    # Handle multi-line list fields (depends_on, challenged_by, secondary_domains)
    for list_key in ("depends_on", "challenged_by", "secondary_domains", "claims_extracted"):
        if list_key not in result:
            # Check for block-style list
            pattern = re.compile(
                rf"^{list_key}:\s*\n((?:\s+-\s+.+\n?)+)", re.MULTILINE
            )
            lm = pattern.search(yaml_block)
            if lm:
                items = YAML_LIST_ITEM_RE.findall(lm.group(1))
                result[list_key] = [i.strip('"').strip("'") for i in items]
    return result
 def extract_body(text: str) -> str:
    """Return the markdown body after frontmatter."""
    m = FRONTMATTER_RE.match(text)
    if m:
        return text[m.end():]
    return text
 # ---------------------------------------------------------------------------
 # Git-based agent attribution
 # ---------------------------------------------------------------------------
 def build_git_agent_map(repo_root: str) -> dict[str, str]:
    """Map file paths → agent name using git log commit message prefixes.
    Commit messages follow: '{agent}: description'
    We use the commit that first added each file.
    """
    file_agent = {}
    try:
        result = subprocess.run(
            ["git", "log", "--all", "--diff-filter=A", "--name-only",
             "--format=COMMIT_MSG:%s"],
            capture_output=True, text=True, cwd=repo_root, timeout=30,
        )
        current_agent = None
        for line in result.stdout.splitlines():
            line = line.strip()
            if not line:
                continue
            if line.startswith("COMMIT_MSG:"):
                msg = line[len("COMMIT_MSG:"):]
                # Parse "agent: description" pattern
                if ":" in msg:
                    prefix = msg.split(":")[0].strip().lower()
                    if prefix in KNOWN_AGENTS:
                        current_agent = prefix
                    else:
                        current_agent = None
                else:
                    current_agent = None
            elif current_agent and line.endswith(".md"):
                # Only set if not already attributed (first add wins)
                if line not in file_agent:
                    file_agent[line] = current_agent
    except (subprocess.TimeoutExpired, FileNotFoundError):
        pass
    return file_agent
 # ---------------------------------------------------------------------------
 # Wiki-link resolution
 # ---------------------------------------------------------------------------
 def build_title_index(all_files: list[str], repo_root: str) -> dict[str, str]:
    """Map lowercase claim titles → file paths for wiki-link resolution."""
    index = {}
    for fpath in all_files:
        # Title = filename without .md extension
        fname = os.path.basename(fpath)
        if fname.endswith(".md"):
            title = fname[:-3].lower()
            index[title] = fpath
        # Also index by relative path
        index[fpath.lower()] = fpath
    return index
 def resolve_wikilink(link_text: str, title_index: dict, source_dir: str) -> str | None:
    """Resolve a [[wiki-link]] target to a file path (node ID)."""
    text = link_text.strip()
    # Skip map links and non-claim references
    if text.startswith("_") or text == "_map":
        return None
    # Direct path match (with or without .md)
    for candidate in [text, text + ".md"]:
        if candidate.lower() in title_index:
            return title_index[candidate.lower()]
    # Title-only match
    title = text.lower()
    if title in title_index:
        return title_index[title]
    # Fuzzy: try adding .md to the basename
    basename = os.path.basename(text)
    if basename.lower() in title_index:
        return title_index[basename.lower()]
    return None
 # ---------------------------------------------------------------------------
 # PR/merge event extraction from git log
 # ---------------------------------------------------------------------------
 def extract_events(repo_root: str) -> list[dict]:
    """Extract PR merge events from git log for the events timeline."""
    events = []
    try:
        result = subprocess.run(
            ["git", "log", "--merges", "--format=%H|%s|%ai", "-50"],
            capture_output=True, text=True, cwd=repo_root, timeout=15,
        )
        for line in result.stdout.strip().splitlines():
            parts = line.split("|", 2)
            if len(parts) < 3:
                continue
            sha, msg, date_str = parts
            # Parse "Merge pull request #N from ..." or agent commit patterns
            pr_match = re.search(r"#(\d+)", msg)
            if not pr_match:
                continue
            pr_num = int(pr_match.group(1))
            # Try to determine agent from merge commit
            agent = "collective"
            for a in KNOWN_AGENTS:
                if a in msg.lower():
                    agent = a
                    break
            # Count files changed in this merge
            diff_result = subprocess.run(
                ["git", "diff", "--name-only", f"{sha}^..{sha}"],
                capture_output=True, text=True, cwd=repo_root, timeout=10,
            )
            claims_added = sum(
                1 for f in diff_result.stdout.splitlines()
                if f.endswith(".md") and any(f.startswith(d) for d in SCAN_DIRS)
            )
            if claims_added > 0:
                events.append({
                    "type": "pr-merge",
                    "number": pr_num,
                    "agent": agent,
                    "claims_added": claims_added,
                    "date": date_str[:10],
                })
    except (subprocess.TimeoutExpired, FileNotFoundError):
        pass
    return events
 # ---------------------------------------------------------------------------
 # Main extraction
 # ---------------------------------------------------------------------------
 def find_markdown_files(repo_root: str) -> list[str]:
    """Find all .md files in SCAN_DIRS, return relative paths."""
    files = []
    for scan_dir in SCAN_DIRS:
        dirpath = os.path.join(repo_root, scan_dir)
        if not os.path.isdir(dirpath):
            continue
        for root, _dirs, filenames in os.walk(dirpath):
            for fname in filenames:
                if fname.endswith(".md") and not fname.startswith("_"):
                    rel = os.path.relpath(os.path.join(root, fname), repo_root)
                    files.append(rel)
    return sorted(files)
 def _get_domain_cached(fpath: str, repo_root: str, cache: dict) -> str:
    """Get the domain of a file, caching results."""
    if fpath in cache:
        return cache[fpath]
    abs_path = os.path.join(repo_root, fpath)
    domain = ""
    try:
        text = open(abs_path, encoding="utf-8").read()
        fm = parse_frontmatter(text)
        domain = fm.get("domain", "")
    except (OSError, UnicodeDecodeError):
        pass
    cache[fpath] = domain
    return domain
 def extract_graph(repo_root: str) -> dict:
    """Extract the full knowledge graph from the codex."""
    all_files = find_markdown_files(repo_root)
    git_agents = build_git_agent_map(repo_root)
    title_index = build_title_index(all_files, repo_root)
    domain_cache: dict[str, str] = {}
    nodes = []
    edges = []
    node_ids = set()
    all_files_set = set(all_files)
    for fpath in all_files:
        abs_path = os.path.join(repo_root, fpath)
        try:
            text = open(abs_path, encoding="utf-8").read()
        except (OSError, UnicodeDecodeError):
            continue
        fm = parse_frontmatter(text)
        body = extract_body(text)
        # Filter by type
        ftype = fm.get("type")
        if ftype and ftype not in INCLUDE_TYPES:
            continue
        # Build node
        title = os.path.basename(fpath)[:-3]  # filename without .md
        domain = fm.get("domain", "")
        if not domain:
            # Infer domain from directory path
            parts = fpath.split(os.sep)
            if len(parts) >= 2:
                domain = parts[1] if parts[0] == "domains" else parts[1] if len(parts) > 2 else parts[0]
        # Agent attribution: git log → domain mapping → "collective"
        agent = git_agents.get(fpath, "")
        if not agent:
            agent = DOMAIN_AGENT_MAP.get(domain, "collective")
        created = fm.get("created", "")
        confidence = fm.get("confidence", "speculative")
        # Detect challenged status
        challenged_by_raw = fm.get("challenged_by", [])
        if isinstance(challenged_by_raw, str):
            challenged_by_raw = [challenged_by_raw] if challenged_by_raw else []
        has_challenged_by = bool(challenged_by_raw and any(c for c in challenged_by_raw))
        has_counter_section = bool(COUNTER_EVIDENCE_RE.search(body) or COUNTERARGUMENT_RE.search(body))
        is_challenged = has_challenged_by or has_counter_section
        # Extract challenge descriptions for the node
        challenges = []
        if isinstance(challenged_by_raw, list):
            for c in challenged_by_raw:
                if c and isinstance(c, str):
                    # Strip wiki-link syntax for display
                    cleaned = WIKILINK_RE.sub(lambda m: m.group(1), c)
                    # Strip markdown list artifacts: leading "- ", surrounding quotes
                    cleaned = re.sub(r'^-\s*', '', cleaned).strip()
                    cleaned = cleaned.strip('"').strip("'").strip()
                    if cleaned:
                        challenges.append(cleaned[:200])  # cap length
        node = {
            "id": fpath,
            "title": title,
            "domain": domain,
            "agent": agent,
            "created": created,
            "confidence": confidence,
            "challenged": is_challenged,
        }
        if challenges:
            node["challenges"] = challenges
        nodes.append(node)
        node_ids.add(fpath)
        domain_cache[fpath] = domain  # cache for edge lookups
        for link_text in WIKILINK_RE.findall(body):
            target = resolve_wikilink(link_text, title_index, os.path.dirname(fpath))
            if target and target != fpath and target in all_files_set:
                target_domain = _get_domain_cached(target, repo_root, domain_cache)
                edges.append({
                    "source": fpath,
                    "target": target,
                    "type": "wiki-link",
                    "cross_domain": domain != target_domain and bool(target_domain),
                })
        # Conflict edges from challenged_by (may contain [[wiki-links]] or prose)
        challenged_by = fm.get("challenged_by", [])
        if isinstance(challenged_by, str):
            challenged_by = [challenged_by]
        if isinstance(challenged_by, list):
            for challenge in challenged_by:
                if not challenge:
                    continue
                # Check for embedded wiki-links
                for link_text in WIKILINK_RE.findall(challenge):
                    target = resolve_wikilink(link_text, title_index, os.path.dirname(fpath))
                    if target and target != fpath and target in all_files_set:
                        target_domain = _get_domain_cached(target, repo_root, domain_cache)
                        edges.append({
                            "source": fpath,
                            "target": target,
                            "type": "conflict",
                            "cross_domain": domain != target_domain and bool(target_domain),
                        })
    # Deduplicate edges
    seen_edges = set()
    unique_edges = []
    for e in edges:
        key = (e["source"], e["target"], e.get("type", ""))
        if key not in seen_edges:
            seen_edges.add(key)
            unique_edges.append(e)
    # Only keep edges where both endpoints exist as nodes
    edges_filtered = [
        e for e in unique_edges
        if e["source"] in node_ids and e["target"] in node_ids
    ]
    events = extract_events(repo_root)
    return {
        "nodes": nodes,
        "edges": edges_filtered,
        "events": sorted(events, key=lambda e: e.get("date", "")),
        "domain_colors": DOMAIN_COLORS,
    }
 def build_claims_context(repo_root: str, nodes: list[dict]) -> dict:
    """Build claims-context.json for chat system prompt injection.
    Produces a lightweight claim index: title + description + domain + agent + confidence.
    Sorted by domain, then alphabetically within domain.
    Target: ~37KB for ~370 claims. Truncates descriptions at 100 chars if total > 100KB.
    """
    claims = []
    for node in nodes:
        fpath = node["id"]
        abs_path = os.path.join(repo_root, fpath)
        description = ""
        try:
            text = open(abs_path, encoding="utf-8").read()
            fm = parse_frontmatter(text)
            description = fm.get("description", "")
        except (OSError, UnicodeDecodeError):
            pass
        claims.append({
            "title": node["title"],
            "description": description,
            "domain": node["domain"],
            "agent": node["agent"],
            "confidence": node["confidence"],
        })
    # Sort by domain, then title
    claims.sort(key=lambda c: (c["domain"], c["title"]))
    context = {
        "generated": datetime.now(tz=timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
        "claimCount": len(claims),
        "claims": claims,
    }
    # Progressive description truncation if over 100KB.
    # Never drop descriptions entirely — short descriptions are better than none.
    for max_desc in (120, 100, 80, 60):
        test_json = json.dumps(context, ensure_ascii=False)
        if len(test_json) <= 100_000:
            break
        for c in claims:
            if len(c["description"]) > max_desc:
                c["description"] = c["description"][:max_desc] + "..."
    return context
 def main():
    parser = argparse.ArgumentParser(description="Extract graph data from teleo-codex")
    parser.add_argument("--output", "-o", default="graph-data.json",
                        help="Output file path (default: graph-data.json)")
    parser.add_argument("--context-output", "-c", default=None,
                        help="Output claims-context.json path (default: same dir as --output)")
    parser.add_argument("--repo", "-r", default=".",
                        help="Path to teleo-codex repo root (default: current dir)")
    args = parser.parse_args()
    repo_root = os.path.abspath(args.repo)
    if not os.path.isdir(os.path.join(repo_root, "core")):
        print(f"Error: {repo_root} doesn't look like a teleo-codex repo (no core/ dir)", file=sys.stderr)
        sys.exit(1)
    print(f"Scanning {repo_root}...")
    graph = extract_graph(repo_root)
    print(f"  Nodes: {len(graph['nodes'])}")
    print(f"  Edges: {len(graph['edges'])}")
    print(f"  Events: {len(graph['events'])}")
    challenged_count = sum(1 for n in graph["nodes"] if n.get("challenged"))
    print(f"  Challenged: {challenged_count}")
    # Write graph-data.json
    output_path = os.path.abspath(args.output)
    with open(output_path, "w", encoding="utf-8") as f:
        json.dump(graph, f, indent=2, ensure_ascii=False)
    size_kb = os.path.getsize(output_path) / 1024
    print(f"  graph-data.json: {output_path} ({size_kb:.1f} KB)")
    # Write claims-context.json
    context_path = args.context_output
    if not context_path:
        context_path = os.path.join(os.path.dirname(output_path), "claims-context.json")
    context_path = os.path.abspath(context_path)
    context = build_claims_context(repo_root, graph["nodes"])
    with open(context_path, "w", encoding="utf-8") as f:
        json.dump(context, f, indent=2, ensure_ascii=False)
    ctx_kb = os.path.getsize(context_path) / 1024
    print(f"  claims-context.json: {context_path} ({ctx_kb:.1f} KB)")
 if __name__ == "__main__":
    main()
--- a/skills/ingest.md
+++ b/skills/ingest.md
@ -0,0 +1,201 @@
 # Skill: Ingest
 Research your domain, find source material, and archive it in inbox/. You choose whether to extract claims yourself or let the VPS handle it.
 **Archive everything.** The inbox is a library, not a filter. If it's relevant to any Teleo domain, archive it. Null-result sources (no extractable claims) are still valuable — they prevent duplicate work and build domain context.
 ## Usage
 ```
 /ingest                    # Research loop: pull tweets, find sources, archive with notes
 /ingest @username          # Pull and archive a specific X account's content
 /ingest url <url>          # Archive a paper, article, or thread from URL
 /ingest scan               # Scan your network for new content since last pull
 /ingest extract            # Extract claims from sources you've already archived (Track A)
 ```
 ## Two Tracks
 ### Track A: Agent-driven extraction (full control)
 You research, archive, AND extract. You see exactly what you're proposing before it goes up.
 1. Archive sources with `status: processing`
 2. Extract claims yourself using `skills/extract.md`
 3. Open a PR with both source archives and claim files
 4. Eval pipeline reviews your claims
 **Use when:** You're doing a deep dive on a specific topic, care about extraction quality, or want to control the narrative around new claims.
 ### Track B: VPS extraction (hands-off)
 You research and archive. The VPS extracts headlessly.
 1. Archive sources with `status: unprocessed`
 2. Push source-only PR (merges fast — no claim changes)
 3. VPS cron picks up unprocessed sources every 15 minutes
 4. Extracts claims via Claude headless, opens a separate PR
 5. Eval pipeline reviews the extraction
 **Use when:** You're batch-archiving many sources, the content is straightforward, or you want to focus your session time on research rather than extraction.
 ### The switch is the status field
 | Status | What happens |
 |--------|-------------|
 | `unprocessed` | VPS will extract (Track B) |
 | `processing` | You're handling it (Track A) — VPS skips this source |
 | `processed` | Already extracted — no further action |
 | `null-result` | Reviewed, no claims — no further action |
 You can mix tracks freely. Archive 10 sources as `unprocessed` for the VPS, then set 2 high-priority ones to `processing` and extract those yourself.
 ## Prerequisites
 - API key at `~/.pentagon/secrets/twitterapi-io-key`
 - Your network file at `~/.pentagon/workspace/collective/x-ingestion/{your-name}-network.json`
 - Forgejo token at `~/.pentagon/secrets/forgejo-{your-name}-token`
 ## The Loop
 ### Step 1: Research
 Find source material relevant to your domain. Sources include:
 - **X/Twitter** — tweets, threads, debates from your network accounts
 - **Papers** — academic papers, preprints, whitepapers
 - **Articles** — blog posts, newsletters, news coverage
 - **Reports** — industry reports, data releases, government filings
 - **Conversations** — podcast transcripts, interview notes, voicenote transcripts
 For X accounts, use `/x-research pull @{username}` to pull tweets, then scan for anything worth archiving. Don't just archive the "best" tweets — archive anything substantive. A thread arguing a wrong position is as valuable as one arguing a right one.
 ### Step 2: Archive with notes
 For each source, create an archive file on your branch:
 **Filename:** `inbox/archive/YYYY-MM-DD-{author-handle}-{brief-slug}.md`
 ```yaml
 ---
 type: source
 title: "Descriptive title of the content"
 author: "Display Name (@handle)"
 twitter_id: "numeric_id_from_author_object"  # X sources only
 url: https://original-url
 date: YYYY-MM-DD
 domain: internet-finance | entertainment | ai-alignment | health | space-development | grand-strategy
 secondary_domains: [other-domain]  # if cross-domain
 format: tweet | thread | essay | paper | whitepaper | report | newsletter | news | transcript
 status: unprocessed | processing    # unprocessed = VPS extracts; processing = you extract
 priority: high | medium | low
 tags: [topic1, topic2]
 flagged_for_rio: ["reason"]  # if relevant to another agent's domain
 ---
 ```
 **Body:** Include the full source text, then your research notes.
 ```markdown
 ## Content
 [Full text of tweet/thread/article. For long papers, include abstract + key sections.]
 ## Agent Notes
 **Why this matters:** [1-2 sentences — what makes this worth archiving]
 **KB connections:** [Which existing claims does this relate to, support, or challenge?]
 **Extraction hints:** [What claims might the extractor pull from this? Flag specific passages.]
 **Context:** [Anything the extractor needs to know — who the author is, what debate this is part of, etc.]
 ```
 The "Agent Notes" section is critical for Track B. The VPS extractor is good at mechanical extraction but lacks your domain context. Your notes guide it. For Track A, you still benefit from writing notes — they organize your thinking before extraction.
 ### Step 3: Extract claims (Track A only)
 If you set `status: processing`, follow `skills/extract.md`:
 1. Read the source completely
 2. Separate evidence from interpretation
 3. Extract candidate claims (specific, disagreeable, evidence-backed)
 4. Check for duplicates against existing KB
 5. Write claim files to `domains/{your-domain}/`
 6. Update source: `status: processed`, `processed_by`, `processed_date`, `claims_extracted`
 ### Step 4: Cross-domain flagging
 When you find sources outside your domain:
 - Archive them anyway (you're already reading them)
 - Set the `domain` field to the correct domain, not yours
 - Add `flagged_for_{agent}: ["brief reason"]` to frontmatter
 - Set `priority: high` if it's urgent or challenges existing claims
 ### Step 5: Branch, commit, push
 ```bash
 # Branch
 git checkout -b {your-name}/sources-{date}-{brief-slug}
 # Stage — sources only (Track B) or sources + claims (Track A)
 git add inbox/archive/*.md
 git add domains/{your-domain}/*.md  # Track A only
 # Commit
 git commit -m "{your-name}: archive {N} sources — {brief description}
 - What: {N} sources from {list of authors/accounts}
 - Domains: {which domains these cover}
 - Track: A (agent-extracted) | B (VPS extraction pending)
 Pentagon-Agent: {Name} <{UUID}>"
 # Push
 FORGEJO_TOKEN=$(cat ~/.pentagon/secrets/forgejo-{your-name}-token)
 git push -u https://{your-name}:${FORGEJO_TOKEN}@git.livingip.xyz/teleo/teleo-codex.git {branch-name}
 ```
 Open a PR:
 ```bash
 curl -s -X POST "https://git.livingip.xyz/api/v1/repos/teleo/teleo-codex/pulls" \
  -H "Authorization: token ${FORGEJO_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "{your-name}: {archive N sources | extract N claims} — {brief description}",
    "body": "## Sources\n{numbered list with titles and domains}\n\n## Claims (Track A only)\n{claim titles}\n\n## Track B sources (VPS extraction pending)\n{list of unprocessed sources}",
    "base": "main",
    "head": "{branch-name}"
  }'
 ```
 ## Network Management
 Your network file (`{your-name}-network.json`) lists X accounts to monitor:
 ```json
 {
  "agent": "your-name",
  "domain": "your-domain",
  "accounts": [
    {"username": "example", "tier": "core", "why": "Reason this account matters"},
    {"username": "example2", "tier": "extended", "why": "Secondary but useful"}
  ]
 }
 ```
 **Tiers:**
 - `core` — Pull every session. High signal-to-noise.
 - `extended` — Pull weekly or when specifically relevant.
 - `watch` — Pull once to evaluate, then promote or drop.
 Agents without a network file should create one as their first task. Start with 5-10 seed accounts.
 ## Quality Controls
 - **Archive everything substantive.** Don't self-censor. The extractor decides what yields claims.
 - **Write good notes.** Your domain context is the difference between a useful source and a pile of text.
 - **Check for duplicates.** Don't re-archive sources already in `inbox/archive/`.
 - **Flag cross-domain.** If you see something relevant to another agent, flag it — don't assume they'll find it.
 - **Log API costs.** Every X pull gets logged to `~/.pentagon/workspace/collective/x-ingestion/pull-log.jsonl`.
 - **Source diversity.** If you're archiving 10+ items from one account in a batch, note it — the extractor should be aware of monoculture risk.
Author	SHA1	Message	Date
m3taversal	2a2a94635c	Merge pull request 'leo: 5 test source archives for VPS extraction pipeline' (#104 ) from leo/test-sources into main	2026-03-10 11:15:10 +00:00
m3taversal	d2beae7c2a	Auto: inbox/archive/2026-02-24-karpathy-clis-legacy-tech-agents.md \| 1 file changed, 30 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	48998b64d6	Auto: inbox/archive/2026-02-25-karpathy-programming-changed-december.md \| 1 file changed, 28 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	85f146ca94	Auto: inbox/archive/2026-02-27-karpathy-8-agent-research-org.md \| 1 file changed, 44 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	533ee40d9d	Auto: inbox/archive/2026-03-08-karpathy-autoresearch-collaborative-agents.md \| 1 file changed, 47 insertions(+)	2026-03-10 11:14:12 +00:00
m3taversal	0226ffe9bd	Auto: inbox/archive/2026-03-04-theiaresearch-permissionless-metadao-launches.md \| 1 file changed, 39 insertions(+)	2026-03-10 11:14:12 +00:00
Leo	75f1709110	leo: add ingest skill — full X-to-claims pipeline (#103 ) Some checks are pending Sync Graph Data to teleo-app / sync (push) Waiting to run Details	2026-03-10 10:42:25 +00:00
Clay	ae66f37975	clay: visitor experience — agent lens selection, README, CONTRIBUTING overhaul (#79 ) Co-authored-by: Clay <clay@agents.livingip.xyz> Co-committed-by: Clay <clay@agents.livingip.xyz>	2026-03-09 22:51:48 +00:00