본문으로 건너뛰기
cccskills
무료GitHub에 공개

fact-check

Verifies claims in backlog items, skill documentation, or plugin content against primary sources using web lookups. Spawns parallel verification agents that must use WebFetch/WebSearch/gh — training data recall is explicitly rejected as evidence. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Use when items are marked UNVERIFIED or when verifying tool API claims, CLI flags, or documented software behavior.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md6.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Fact Check — Primary Source Claim Verification

Verify factual claims against primary sources using web lookups. Training data recall is NOT evidence.


When to Use

  • Backlog items marked UNVERIFIED
  • Plugin documentation with uncited claims about tools, APIs, CLI flags
  • Skills that reference specific software behavior without citation
  • After code review agents flag potential fabrication

When NOT to Use

  • Structural findings (broken links, missing files, malformed YAML) — those are filesystem checks, not fact checks
  • Code logic bugs — use /dh:root-cause-tracing-process instead
  • Research on new tools — use /research-curator instead

Evidence Rules

<evidence_rules>

Adapted from the root-cause-tracing-process evidence chain protocol.

Valid evidence (one of these MUST support each verdict):

  1. WebFetch output — content retrieved from an official documentation URL
  2. WebSearch result — search results linking to authoritative sources
  3. Command output — npx <tool> --help, gh api, or similar CLI output
  4. Repository source code — file content from the tool's GitHub repo via gh or clone
  5. MCP tool output — mcp__Ref, mcp__exa results with URLs

NOT valid evidence (these MUST NOT support verdicts):

  • Training data recall ("I know this from my training")
  • Inference from absence ("the docs don't mention it, so it doesn't exist")
  • Reasoning from analogy ("tool X works this way, so tool Y does too")
  • Documentation that describes intent without observed behavior
  • Another AI's claim without primary source backing

</evidence_rules>


Claim Extraction

Parse the input to extract discrete, falsifiable claims:

flowchart TD
    Start([Parse input]) --> Q{Input type?}
    Q -->|Backlog item title| ReadBacklog[Run backlog list --format json, find matching item]
    Q -->|Plugin path| ScanFiles[Scan SKILL.md and references for factual claims]
    Q -->|--all-unverified| FindAll[Run backlog list --format json, filter UNVERIFIED items]
    ReadBacklog --> Extract[Extract falsifiable claims]
    ScanFiles --> Extract
    FindAll --> Extract
    Extract --> Classify[Classify each claim]
    Classify --> Spawn[Spawn verification agents]

Claim Classification

For each claim, determine:

  1. Claim text — the specific assertion
  2. Primary source — where to check (official docs URL, GitHub repo, CLI help)
  3. Verification method — WebFetch URL, run CLI command, search GitHub issues
  4. Falsifiability — what would disprove this claim?

Verification Agent Spawning

Spawn @fact-checker agents in parallel waves of 5.

Each agent receives:

CLAIM: {exact claim text}
SOURCE_FILE: {file containing the claim, with line numbers}
PRIMARY_SOURCE: {URL or command to check against}
VERIFICATION_METHOD: {WebFetch | WebSearch | CLI command | gh API}
FALSIFICATION_CRITERIA: {what would disprove this}

Wave Execution

flowchart TD
    Start([Claims extracted]) --> Count{How many claims?}
    Count -->|1-5| Wave1[Wave 1 — all in parallel]
    Count -->|6-10| Split2[Wave 1 — first 5, Wave 2 — remaining]
    Count -->|11+| SplitN[Waves of 5, sequential]
    Wave1 --> Collect[Collect verdicts]
    Split2 --> SeqWaves[Execute waves sequentially]
    SeqWaves --> Collect
    SplitN --> SeqWaves
    Collect --> Report[Generate report]

Verdict Format

Each agent returns a structured verdict:

CLAIM: {the claim being checked}
VERDICT: VERIFIED | REFUTED | INCONCLUSIVE
EVIDENCE:
  - Source: {URL or command}
  - Retrieved: {date}
  - Content: {relevant excerpt from primary source}
EXPLANATION: {how the evidence supports the verdict}
CITATION: {formatted citation for backlog/docs update}

Verdict Criteria

  • VERIFIED — primary source confirms the claim. Evidence excerpt matches.
  • REFUTED — primary source contradicts the claim. Evidence excerpt shows the discrepancy.
  • INCONCLUSIVE — primary source could not be reached, does not address the claim, or is ambiguous. State what additional step would resolve it.

Chain of Verification (CoVe) Requirement

Each agent MUST apply CoVe before finalizing:

  1. Initial lookup — fetch primary source, form initial verdict
  2. Verification questions — generate 2-3 questions that could falsify the initial verdict
  3. Independent check — answer each verification question using a different source or method
  4. Final verdict — confirm or revise based on cross-checking

This prevents the agent from confirming its own bias in a single lookup.


Report Format

After all waves complete:

# Fact Check Report

**Date**: {YYYY-MM-DD}
**Scope**: {backlog item title | plugin path | all-unverified}
**Claims checked**: {N}

## Summary

| Verdict | Count |
|---------|-------|
| VERIFIED | {N} |
| REFUTED | {N} |
| INCONCLUSIVE | {N} |

## Verdicts

### Claim 1: {claim text}

**Verdict**: {VERIFIED|REFUTED|INCONCLUSIVE}
**Source**: {primary source URL}
**Evidence**: {excerpt}
**Citation**: {formatted citation}

### Claim 2: ...

## Backlog Updates

{For each REFUTED or VERIFIED claim, the specific edit to make to the per-item file}

Post-Actions

  1. Update backlog — for VERIFIED/REFUTED claims, update the backlog item's Status field in the per-item file
  2. Lint — uv run prek run --files .claude/backlog/
  3. Commit — git add .claude/backlog/ && git commit -m "docs(backlog): fact-check {N} claims ({date})"

For INCONCLUSIVE claims, add a note to the backlog item describing what additional verification is needed.


References

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jamie-BitFlight/claude_skills672026년 10월 20일 업데이트

SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jamie-BitFlight/claude_skills672026년 10월 20일 업데이트

Browser automation for AI agents using the agent-browser CLI and Playwright. Use when navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, logging into sites, or automating any browser task. Triggers on "open a website", "fill out a form", "click a button", "scrape data", "test this web app", "automate browser actions", or any programmatic web interaction request.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jamie-BitFlight/claude_skills672026년 10월 20일 업데이트

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jamie-BitFlight/claude_skills672026년 10월 20일 업데이트

Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jamie-BitFlight/claude_skills672026년 10월 20일 업데이트

Create or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jamie-BitFlight/claude_skills672026년 10월 20일 업데이트

Jamie-BitFlight의 스킬 모두 보기

이 스킬의 문제 신고