How do I install the Verify Claims skill?

Run npx skillmds@latest add emmanuelrtm/verify-claims in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Who published Verify Claims?

EmmanuelRTM (@emmanuelrtm) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Verify Claims by emmanuelrtm — Agent Skill

Name: Verify Claims
Author: EmmanuelRTM

Verify Claims

Run (or re-run) Stage 4 only — the Claim Verifier: extract every claim from the draft, verify each against its declared evidence source (numerical ±5%, citation entailment, method-code alignment), refine failures, finalize the paper, and run the Chain-of-Evidence audit. Requires paper/draft.md.

EmmanuelRTM 500dd86 2.6 KB Updated Sep 17, 2026

File contents

/verify-claims — Stage 4: Extract → Verify → Refine → Finalize

Arguments: $ARGUMENTS

Current state: !python3 .claude/scripts/ledger.py status

Preconditions: resolve the run (--run or ACTIVE_RUN); paper/draft.md must exist — otherwise stop and point to /write-paper.

Extract: python3 .claude/scripts/extract_claims.py <run> → paper/claims.jsonl.
Verify: launch the claim-verifier subagent (run dir). It runs verify_claims.py, judges the PENDING_LLM claims (citation entailment vs literature/ notes; method-code alignment vs best/solution.py) with a verbatim supporting quote per PASS/PARTIAL, and writes paper/verification-report.md. A stop-gate hook prevents it from finishing with unresolved or quote-ungrounded claims.

Best-of-N votes (if run-config.json#verifier_votes is N ≥ 2): the LLM judgments are sampled N times and disagreements resolved conservatively. SEQUENTIALLY, for k = 1..N-1: launch a claim-verifier, then python3 .claude/scripts/verdict_votes.py snapshot <run> <k> (this archives vote k under paper/votes/ and resets the LLM-judged claims so the next verifier judges blind — votes must NOT run in parallel, they share paper/claims.jsonl). Launch the final claim-verifier, then python3 .claude/scripts/verdict_votes.py merge <run> — on any disagreement the weakest verdict wins (FAIL < PARTIAL < PASS). Append the merge summary to paper/verification-report.md under a ## Vote reconciliation header, and re-run python3 .claude/scripts/verify_claims.py <run> for the post-merge tally.
Refine loop (max 2 rounds): if any claim is FAIL, launch refiner (run dir + the failure list), then re-run python3 .claude/scripts/verify_claims.py <run>. Remaining FAILs after round 2 are reported honestly, not hidden.
Finalize: copy paper/draft.md → final/paper.md.
Chain-of-Evidence audit: python3 .claude/scripts/chain_of_evidence.py <run> — Score Verification, Reference Verification, Specification Violation, Method-Code Alignment.
Ledger: python3 .claude/scripts/ledger.py append '{"event":"stage_verify","detail":"<tally>"}'

Report: claim tally (pass/partial/fail), the chain-of-evidence table, and the final paper path. Offer /paper-to-latex.

---name: verify-claimsdescription: Run (or re-run) Stage 4 only — the Claim Verifier: extract every claim from the draft, verify each against its declared evidence source (numerical ±5%, citation entailment, method-code alignment), refine failures, finalize the paper, and run the Chain-of-Evidence audit. Requires paper/draft.md.---# /verify-claims — Stage 4: Extract → Verify → Refine → FinalizeArguments: `$ARGUMENTS`Current state: !`python3 .claude/scripts/ledger.py status`Preconditions: resolve the run (`--run` or ACTIVE_RUN); `paper/draft.md` mustexist — otherwise stop and point to `/write-paper`.1. **Extract**: `python3 .claude/scripts/extract_claims.py <run>` → `paper/claims.jsonl`.2. **Verify**: launch the `claim-verifier` subagent (run dir). It runs `verify_claims.py`, judges the PENDING_LLM claims (citation entailment vs `literature/` notes; method-code alignment vs `best/solution.py`) with a verbatim supporting quote per PASS/PARTIAL, and writes `paper/verification-report.md`. A stop-gate hook prevents it from finishing with unresolved or quote-ungrounded claims. **Best-of-N votes** (if `run-config.json#verifier_votes` is N ≥ 2): the LLM judgments are sampled N times and disagreements resolved conservatively. SEQUENTIALLY, for k = 1..N-1: launch a `claim-verifier`, then `python3 .claude/scripts/verdict_votes.py snapshot <run> <k>` (this archives vote k under `paper/votes/` and resets the LLM-judged claims so the next verifier judges blind — votes must NOT run in parallel, they share `paper/claims.jsonl`). Launch the final claim-verifier, then `python3 .claude/scripts/verdict_votes.py merge <run>` — on any disagreement the weakest verdict wins (FAIL < PARTIAL < PASS). Append the merge summary to `paper/verification-report.md` under a `## Vote reconciliation` header, and re-run `python3 .claude/scripts/verify_claims.py <run>` for the post-merge tally.3. **Refine loop** (max 2 rounds): if any claim is FAIL, launch `refiner` (run dir + the failure list), then re-run `python3 .claude/scripts/verify_claims.py <run>`. Remaining FAILs after round 2 are reported honestly, not hidden.4. **Finalize**: copy `paper/draft.md` → `final/paper.md`.5. **Chain-of-Evidence audit**: `python3 .claude/scripts/chain_of_evidence.py <run>` — Score Verification, Reference Verification, Specification Violation, Method-Code Alignment.6. Ledger: `python3 .claude/scripts/ledger.py append '{"event":"stage_verify","detail":"<tally>"}'`Report: claim tally (pass/partial/fail), the chain-of-evidence table, and thefinal paper path. Offer `/paper-to-latex`.

Frequently asked questions