L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Développement · SKILL.md

factcheck

factcheck

Fact-check any piece of written content — LinkedIn posts, emails, YouTube scripts, newsletters, or any draft text — by identifying factual claims and verifying them via web search. Returns a clear ✅/❌ claim-by-claim report with sources, suggested fixes, and a publishability rating. Trigger this skill whenever the user types /factcheck, pastes content and as…

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: factcheck description: > Fact-check any piece of written content — LinkedIn posts, emails, YouTube scripts, newsletters, or any draft text — by identifying factual claims and verifying them via web search. Returns a clear ✅/❌ claim-by-claim report with sources, suggested fixes, and a publishability rating. Trigger this skill whenever the user types /factcheck, pastes content and asks to fact-check it, asks to "verify", "check the facts", or "make sure this is accurate". Always present a clickable tier selector unless the user already specified a tier.

Fact Checker

Verify factual claims in written content and return a clear, sourced, actionable report.


Triggering

Use this skill when:

  • The user types /factcheck (with or without content following)
  • The user pastes a draft and says anything like "fact-check this", "check this", "is this accurate?", "verify this"

Step 1: Get the content and tier

If the user hasn't provided content yet, ask for it.

If the user hasn't specified a tier, use the ask_user_input_v0 tool to present clickable options — do NOT ask them to type it. Use this exact call:

ask_user_input_v0({
  questions: [{
    question: "Which level of fact-checking would you like?",
    type: "single_select",
    options: [
      "⚡ Quick — Top claims only, fast",
      "🔍 Standard — All claims, solid sourcing",
      "🧠 Deep — All claims, multiple sources, nuance flagged"
    ]
  }]
})

Step 2: Extract claims

Read the content carefully and identify all checkable factual claims — statements that are objectively true or false and can be verified. Ignore opinions, predictions, and subjective statements.

Examples of checkable claims:

  • Statistics and numbers ("X% of people…", "revenue grew by $Y")
  • Named events and dates ("X happened in 2021")
  • Attributions ("X said…", "X was founded by…")
  • Scientific or historical facts
  • Claims about specific people, companies, or products

Watch for viral/commonly-misquoted claims — statistics or quotes that circulate widely online but have a history of being misattributed, exaggerated, or fabricated (e.g. "85% of jobs in 2030 don't exist yet", "humans only use 10% of their brains"). Flag these proactively for extra scrutiny.

Label each claim with a short identifier (C1, C2, C3…).


Step 3: Research by tier

⚡ Quick

  • Pick the top 3–5 most important or risky claims (skip minor details)
  • Run one focused web search per claim
  • Prioritize speed over exhaustiveness
  • If a result looks surprising or contradicts the claim, run one follow-up search to confirm before marking as ❌

🔍 Standard

  • Check all identified claims
  • Run one to two web searches per claim
  • Prioritize authoritative sources (official sites, reputable news, academic sources)
  • For any ❌ or surprising ⚠️ finding: run a second search from a different angle to confirm before finalizing the verdict

🧠 Deep

  • Check all identified claims
  • Run two to four web searches per claim, cross-referencing sources
  • For any ❌ or surprising ⚠️ finding: mandatory second verification search from a different source or angle before finalizing — do not mark something as wrong based on a single source
  • If sources conflict, flag it explicitly
  • Note important context, nuance, or caveats even if the core claim is technically correct
  • Flag any claims that are misleading or missing important context, even if not outright false
  • Apply the 🚨 Viral/Misquoted flag to any claim that appears widely circulated but dubious

Step 4: Output the report

Return a clean, scannable report in this format:


🔎 Fact-Check Report — [Quick / Standard / Deep]

Content type: [LinkedIn post / Email / YouTube script / Newsletter / Other]

Publishability: 🟢 Good to go / 🟡 Fix before publishing / 🔴 Do not publish as-is


C1: [Short summary of the claim]

"[Exact quote or close paraphrase from the original text]"

✅ Verified / ❌ Incorrect / ⚠️ Partially correct / ❓ Unverifiable / 🚨 Viral claim — unreliable

[1–3 sentence explanation of what you found. If incorrect or partially correct, state what the accurate information is.]

🔗 Source: [Source name + URL]

💬 Suggested fix: "[Drop-in corrected sentence the user can paste straight into their draft — only include this for ❌ and ⚠️ claims, not for ✅]"


(Repeat for each claim)


Summary

  • ✅ Verified: X
  • ❌ Incorrect: X
  • ⚠️ Partially correct: X
  • ❓ Unverifiable: X
  • 🚨 Viral/unreliable: X

Overall assessment: [1–2 sentence summary of how accurate the content is.]


Status labels

LabelMeaning
✅ VerifiedConfirmed accurate by reliable sources
❌ IncorrectContradicted by reliable sources — provide the correct information + a suggested fix
⚠️ Partially correctCore claim is true but missing context, outdated, or overstated — provide a suggested fix
❓ UnverifiableCould not find reliable sources to confirm or deny
🚨 Viral/MisquotedWidely circulated stat or quote with no credible original source — treat as unreliable

Publishability rating

RatingMeaning
🟢 Good to goAll claims verified or only minor caveats — safe to publish
🟡 Fix before publishingOne or more claims are incorrect or partially correct — fix flagged items first
🔴 Do not publish as-isMultiple significant errors, or a core claim is wrong in a way that would embarrass the author

Tone guidelines

  • Be direct and useful — this is a tool, not an essay
  • When something is wrong, say what the correct information is
  • The suggested fix should be natural and ready to paste — not a note to the author, but actual replacement text
  • Don't hedge excessively — if the sources are clear, be clear
  • For Deep checks: flag nuance and context even when a claim is technically true
Skills enregistrées