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
4 changed files with 289 additions and 186 deletions
--- 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?