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Fact-check claims encountered during reading — dates, names, events, citations. Use when encountering historical facts or disputed claims.
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Baseado na classificação ocupacional SOC
Use when AI-assisted thesis edits risk claim drift, scope creep, loss of author control, or repeated revisions that fail to converge; provides spine cards, edit contracts, drift audits, revision escalation, and human gates.
Stop repeated failed writing, coding, manuscript, rebuttal, or restructuring revisions when the same issue has gone through 3+ unsatisfactory edits, vague feedback such as still wrong/weird/unclear/weak/越改越乱, version contamination, or possible gap/claim/evidence/venue-fit drift.
Build and audit the manuscript argument system: intent, gap-contribution alignment, hierarchical claims, evidence balance, limitations, and reviewer attack surfaces. Use when a paper, thesis chapter, review article, or rebuttal needs explicit intent registers, contribution chains, claim hierarchy, argument maps, evidence-fit checks, or reviewer-risk matrices before drafting, revision, peer review, or self-review.
Review another author's manuscript, paper, thesis chapter, proposal, or preprint as an external reviewer. Use when asked to evaluate novelty, significance, gap-contribution fit, claim-evidence adequacy, methods, evaluation, overclaim risks, structure, writing, required revisions, or recommendation without rewriting the manuscript or using private author context.
Review the user's own manuscript, paper, thesis chapter, rebuttal, or release packet with clean-room anti-contamination controls. Use when asked for self-review, internal review, pre-submission review, readiness check, reviewer simulation on own work, or claim-evidence self-audit where prior chat memory, unstated assumptions, model background knowledge, or unlisted local notes must not be treated as evidence.
Reframe academic manuscript drafts that read like engineering reports, module inventories, internal validation packets, or system notes into paper-form scientific arguments with clear gap, contribution chain, results narrative, figure/table roles, AI-assisted component boundaries, and submission-readiness blockers.
| name | verify |
| description | Fact-check claims encountered during reading — dates, names, events, citations. Use when encountering historical facts or disputed claims. |
| allowed-tools | WebSearch, WebFetch, Read, Edit |
Fact-check claims encountered during reading sessions. This covers dates, names, historical events, publication details, and attribution of ideas. Use when the user questions a claim or when a factual assertion seems uncertain.
This skill activates on: verify, check this, fact-check, is this correct, /verify.
(verified) or (corrected: {correct info}) tag next to the relevant entry in the notes file.## Verification
**Claim**: {the original claim as stated in the source}
**Verdict**: Confirmed | Needs correction | Incorrect | Unverifiable
| Item | Original | Verified |
|------|----------|----------|
| {specific fact} | {as claimed} | {as verified} |
**Source**: {single-line citation in the project's declared style — see `literature/reading_notes/_template_NOTES.md` for per-style examples; the active style is `Citation style:` in `CLAUDE.md`. Append the URL after the citation if the source is web-only}
**Confidence**: High | Medium | Low
**Notes**: {any additional context, e.g., "Date varies by edition" or "Multiple conflicting sources"}
When verifying several claims at once, produce one verification block per claim, numbered sequentially:
### 1. {Brief claim description}
{verification block}
### 2. {Brief claim description}
{verification block}
When the user says "annotate" or "mark in notes" after verification:
(verified 2026-03-30) or (corrected: {correct value}, verified 2026-03-30).