Polycentric Labcoat

The rigorous net-new research-investigation engine. Fan a question across a live multi-model fleet, then kill every finding that can't be traced to a primary source, validate the survivors three ways, and return a ruthlessly ranked synthesis. Built to be wrong-proof, not fast.

A Claude Code skill (Agent Skills standard) backed by a standalone, provider-agnostic Python multi-model fleet runner, a fail-closed redaction gate, and a novelty source-log.

Repo Polycentric-Labs/labcoat · skill name polycentric-labcoat — invoke /polycentric-labcoat; to install, junction this repo into ~/.claude/skills/polycentric-labcoat.

Why it exists

Opinion models hallucinate proper nouns. In the run that produced this skill, an 8-model fleet fabricated ~9 CVE IDs and ~6 academic citations — including a real arXiv ID paired with a hallucinated title — and per-item web-grounding caught every one. On its first real investigation it also caught the opposite failure: all four models in the fleet unanimously denied a real standard existed. A research method that trusts model output for a CVE number, a repo name, a version string, or a citation will confidently ship fiction.

So labcoat treats every fleet-produced proper noun (CVE/advisory ID, arXiv ID, repo, version, citation, tool name, statistic) as a claim to disprove, not a fact to relay. The fleet is an idea generator; only a primary-source check produces a fact.

The 6-phase pipeline

Every phase ends at a stop-and-ask gate — no phase auto-advances past a material decision.

0. Scope + redact + cost — decompose the question, challenge the premise, pull prior research, run the redaction gate on every outbound prompt, pick a depth tier and show the cost estimate.
1. Harvest — parallel agents answer non-overlapping sub-questions, writing to disk as they go.
2. Multi-model divergence — route each query, then fan it across a live, multi-vendor fleet (Gemini + GPT + Grok + DeepSeek as a floor). Disagreement is signal.
3. Hard-skeptic web-grounded verification — the spine. Try to kill each finding; web-confirm every proper noun against a primary source (gh for repos, arXiv/Semantic Scholar for papers, NVD/vendor for CVEs, fetch/Playwright for pages). Quarantine the unconfirmed.
4. 3× adversarial validation — three distinct lenses: fidelity, soundness, completeness.
5. Rank + synthesize — a ruthless ranking with per-item evidence and an honest skip/kill list.
6. Capture — record high-signal sources, hand the synthesis off for cadence-based refresh.

Components

Path	Role
SKILL.md	The 6-phase orchestration, the gates, the anti-hallucination rules
scripts/fleet.py	Standalone parallel multi-model fleet runner with reasoning-model-safe defaults (no secrets in code)
scripts/redaction_gate.py	Fail-closed sensitive-data gate for outbound prompts (findings carry offsets, never the secret)
scripts/novelty_log.py	Persistent high-signal source-domain log
scripts/route_integration.py	Wrapper over a query-routing classifier with a safe-default degrade; optionally calls the sonar-router companion — degrades gracefully if absent
references/anti-hallucination.md	The R-1..R-5 rules + the case study
references/output-templates/	Standardized doc shapes for each phase
tests/	python -m pytest tests/ -q

Use

As a Claude Code skill: place the folder under your skills directory (e.g. symlink/junction it to ~/.claude/skills/polycentric-labcoat) and invoke /polycentric-labcoat or ask for "rigorous research" / "investigate X with the fleet."

The fleet runner standalone:

pip install -r requirements.txt        # httpx
export OPENROUTER_API_KEY=...           # env only — never hardcode, never log
python -m pytest tests/ -q

fleet.run_fleet(...) fans a prompt across models in parallel and returns one structured result per model; the API key never appears in any result. See SKILL.md for the call shapes.

The fleet runs against the OpenRouter API via fleet.py. Optional: you can instead drive it interactively through the third-party openrouter-multimodal MCP — it's not required and bundles none of labcoat.

Companion projects

• sonar-router — optional query-routing companion. route_integration.py wraps its classifier; if sonar-router is absent the wrapper degrades gracefully to a safe default.

Anti-hallucination (the spine)

• R-1 — never trust an opinion-model proper noun until web-confirmed (even at full fleet consensus).
• R-2 — primary-source check for every "X exists / is novel / says Y."
• R-3 — a dedicated fabrication-purge pass; expect fabrication.
• R-4 — hard-skeptic posture: try to kill each finding, not confirm it.
• R-5 — whatever renders the truth (fetch, then a headless browser for JS-heavy pages).

Tripwire: a proper noun that no verification record confirms is blocked from the synthesis. Full detail in references/anti-hallucination.md.

License

MIT — see LICENSE.

AI assistance

This project was developed alongside AI platforms.

Models used: Claude Opus 4.6, Claude Opus 4.7, Sonar Deep Research