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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
2026-03-10 10:42:25 +00:00
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
9 changed files with 1345 additions and 266 deletions

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.github/workflows/sync-graph-data.yml vendored Normal file
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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

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# 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.

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# 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
```
### 2. Pull latest and create a branch
```bash
git checkout main
git pull origin main
git checkout main && git pull
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/` with this naming convention:
Create a markdown file in `inbox/archive/`:
```
inbox/archive/YYYY-MM-DD-author-handle-brief-slug.md
```
Example: `inbox/archive/2026-03-07-alex-ai-alignment-landscape.md`
### 4. Add frontmatter
Every source file starts with YAML frontmatter. Copy this template and fill it in:
### 3. Add frontmatter + content
```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.
### 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
### 4. Commit, push, open PR
```bash
git add inbox/archive/your-file.md
git commit -m "contrib: add AI alignment landscape report
Source: [brief description of what this is and why it matters]"
git commit -m "contrib: add [brief description]
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
gh pr create --title "contrib: AI alignment landscape report" --body "Source material for agent extraction.
## Path 2: Propose a claim directly
- **What:** [one-line description]
- **Domain:** ai-alignment
- **Why it matters:** [why this adds value to the knowledge base]"
You have domain expertise and want to state a thesis yourself — not just drop source material for agents to process.
### 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
2. **Leo** (the evaluator) reviews the extracted claims for quality
3. You'll see their feedback as PR comments
4. Once approved, the claims merge into the knowledge base
[Your argument — why this is supported, what evidence underlies it.
Cite sources, data, studies inline. This is where you make the case.]
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.
- **Pick the right domain.** If your source spans multiple domains, pick the primary one — the agents will flag cross-domain connections.
- **One source per file.** Don't combine multiple articles into one file.
- **Original analysis welcome.** Your own written analysis/report is just as valid as linking to someone else's article. Put yourself as the author.
- **Don't extract claims yourself.** Just provide the source material. The agents handle extraction — that's their job.
- **More context is better.** For source submissions, paste the full text, not just a link.
- **Pick the right domain.** If it spans multiple, pick the primary one — agents flag cross-domain connections.
- **One source per file, one claim per file.** Atomic contributions are easier to review and link.
- **Original analysis is welcome.** Your own written analysis is as valid as citing someone else's work.
- **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?

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# 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.

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# 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?

View file

@ -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)
# - Domain agent verdict is "Approve" (parsed from comment)
# - Leo's comment contains "**Verdict:** approve"
# - 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
;;
esac
fi
echo "$agent $domain"
}
@ -112,8 +124,8 @@ if ! command -v claude >/dev/null 2>&1; then
exit 1
fi
# Check for dirty working tree (ignore ops/ and .claude/ which may contain uncommitted scripts)
DIRTY_FILES=$(git status --porcelain | grep -v '^?? ops/' | grep -v '^ M ops/' | grep -v '^?? \.claude/' | grep -v '^ M \.claude/' || true)
# Check for dirty working tree (ignore ops/, .claude/, .github/ which may contain local-only files)
DIRTY_FILES=$(git status --porcelain | grep -v '^?? ops/' | grep -v '^ M ops/' | grep -v '^?? \.claude/' | grep -v '^ M \.claude/' | grep -v '^?? \.github/' | grep -v '^ M \.github/' || true)
if [ -n "$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/$pr/reviews" \
--jq 'map(select(.state != "DISMISSED")) | sort_by(.submitted_at) | last | .submitted_at' 2>/dev/null || echo "")
# Check if this PR already has a Leo verdict comment (avoid re-reviewing)
LEO_COMMENTED=$(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
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
# Gate 2: Check Leo's verdict from issue comments
local leo_verdict
leo_verdict=$(gh pr view "$pr_number" --json comments \
--jq '.comments[] | select(.body | test("## Leo Review")) | .body' 2>/dev/null \
| grep -oiE '\*\*Verdict:[^*]+\*\*' | tail -1 || echo "")
--jq '[.comments[] | select(.body | test("VERDICT:LEO:")) | .body] | last' 2>/dev/null || echo "")
if echo "$leo_verdict" | grep -qi "approve"; then
echo "Leo: APPROVED (via comment verdict)"
elif echo "$leo_verdict" | grep -qi "request changes\|reject"; then
echo "BLOCK: Leo verdict: $leo_verdict"
if echo "$leo_verdict" | grep -q "VERDICT:LEO:APPROVE"; then
echo "Leo: APPROVED"
elif echo "$leo_verdict" | grep -q "VERDICT:LEO:REQUEST_CHANGES"; then
echo "BLOCK: Leo requested changes"
return 1
else
echo "BLOCK: Could not determine Leo's verdict"
echo "BLOCK: Could not find Leo's verdict marker in PR comments"
return 1
fi
fi
# Gate 3: Check domain agent verdict (if applicable)
if [ -n "$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 \
| grep -oiE '\*\*Verdict:[^*]+\*\*' | tail -1 || echo "")
--jq "[.comments[] | select(.body | test(\"VERDICT:${domain_key}:\")) | .body] | last" 2>/dev/null || echo "")
if [ -z "$domain_verdict" ]; then
# Also check review API for domain agent approval
# Since all agents use the same GitHub account, we check for multiple approvals
local approval_count
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: $domain_verdict"
if echo "$domain_verdict" | grep -q "VERDICT:${domain_key}:APPROVE"; then
echo "Domain agent ($domain_agent): APPROVED"
elif echo "$domain_verdict" | grep -q "VERDICT:${domain_key}:REQUEST_CHANGES"; then
echo "BLOCK: $domain_agent requested changes"
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}
If ANY claim needs changes: gh pr review ${pr} --request-changes --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):
<!-- 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

179
ops/extract-cron.sh Executable file
View file

@ -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)"

520
ops/extract-graph-data.py Normal file
View file

@ -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()

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# 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.