# Fact Checker > Extract and verify every factual claim in a draft before it gets published, using web search against primary sources, and return a claim-by-claim verdict report plus a corrected version. Use this skill whenever the user asks to fact-check, verify, "make sure this is accurate", or review a piece before publishing or sending. ALSO use it proactively before delivering any content containing statistics, dates, prices, quotes, names, job titles, rankings, or superlatives (first, largest, only), including blog posts, newsletters, LinkedIn posts, reports, press releases, and presentations. Do NOT use for pure opinion pieces, fiction, or internal brainstorms the user isn't publishing. - Skill: `kshyam/fact-checker` (Agent Skill) - Install (CLI): `npx skillmds@latest add kshyam/fact-checker` - Raw SKILL.md: https://api.skillmd.com/api/skills/kshyam/fact-checker/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: kshyam (https://skillmd.com/u/kshyam) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kshyam/fact-checker --- # Fact Checker One wrong number in a published piece costs more than a hundred right ones earn. Readers who catch a single error discount everything else, corrections travel slower than the original mistake, and in professional contexts a bad stat can mean legal or reputational damage. This skill exists because the failure mode is silent: wrong claims read exactly like right ones. The only defense is checking each one on purpose. A second reason this skill exists: text drafted from memory (yours or the user's) inherits stale facts. A claim that was true in 2023 can be false today. Treat "I remember this being true" as a hypothesis, never as a verification. ## Workflow ### 1. Extract claims Read the draft and pull out every checkable factual assertion. A claim is checkable if a source could prove it right or wrong. Number each one and keep the exact wording from the draft, because precision matters: "the largest producer" and "one of the largest producers" are different claims with different truth values. Not claims (skip these): opinions, predictions, the user's personal experiences, hedged generalities ("many people prefer..."), and genuinely common knowledge (water boils at 100°C at sea level). When in doubt whether something is common knowledge, treat it as a claim. ### 2. Triage by risk Verify in this order, because these categories fail most often and cost most when wrong: 1. **Numbers**: statistics, prices, percentages, dates, counts 2. **Quotes**: attributed statements (check wording AND attribution; misattributed real quotes are the most common quote failure) 3. **People and titles**: names, spellings, current roles. Job titles go stale fast; "CEO of X" claims are wrong surprisingly often. 4. **Superlatives and absolutes**: first, only, largest, never, always. These are falsified by a single counterexample, so they fail at the highest rate of any category. 5. **Medical, legal, and financial claims**: highest stakes; hold these to primary-source standard only. 6. **Names of products, studies, laws, organizations**: easy to garble, easy to check. If the draft has more than ~25 claims, tell the user, verify the high-risk tiers fully, and list the low-risk remainder as unchecked rather than silently skipping them. ### 3. Verify against sources Search for each claim. Source quality rules: - **Primary beats secondary.** The company's filing, the study itself, the government dataset, the transcript. An aggregator citing a source is a pointer, not a verification; follow the pointer. - **Check the date.** A 2021 source can only verify what was true in 2021. For anything that changes (prices, populations, market shares, job titles, records), require a recent source and note the as-of date. - **Two independent sources for surprising claims.** If a claim is counterintuitive or damaging to someone, one source is not enough, and make sure the two sources aren't both citing the same origin. - **A claim repeated everywhere is not therefore true.** Viral stats (the "we swallow 8 spiders a year" class) have thousands of citations and zero primary sources. If you cannot find the origin, that is a finding: mark it unverifiable. ### 4. Render verdicts Use exactly these five verdicts, because the differences drive different fixes: - **CONFIRMED**: matches a reliable source. Cite it. - **NEEDS UPDATE**: was true, isn't current. Provide the current figure and its as-of date. - **IMPRECISE**: directionally right, wrong as written (wrong year, rounded too far, overstated superlative). Provide corrected wording. - **UNVERIFIABLE**: no adequate source found either way. Do not treat as false; do recommend cutting or hedging it, since the user cannot defend it if challenged. - **FALSE**: contradicted by reliable sources. Show what the source actually says. ### 5. Report, then repair Return the report in this format: ``` ## Fact-check report Checked N claims: X confirmed, Y need changes, Z unverifiable. 1. "exact claim text" — CONFIRMED Source: [name + link], as of [date] 2. "exact claim text" — FALSE Source says: [what it actually says] Suggested fix: [replacement wording] ... ``` Then offer to apply the fixes and return a corrected draft. When applying fixes, change only what the verdicts require; do not rewrite voice or style (that's a different job, and mixing the two makes the diff unreviewable). ## Judgment rules **Never rubber-stamp.** If every claim in a stat-heavy piece comes back CONFIRMED on the first pass, re-examine the two or three most surprising ones. A fact-check that finds nothing should earn that result. **"I couldn't verify it" is a respectable answer.** The failure mode to avoid at all costs is asserting a verdict without a source. Every CONFIRMED and every FALSE must carry a citation the user can click. **Check what the draft says, not what it meant.** If the draft says "studies show" (plural) and you found one study, that's IMPRECISE. If it says "proven" and the source says "associated with", that's IMPRECISE. Overclaiming is a factual error, not a style choice. **If web search is unavailable**, do not simulate verification from memory. Extract and triage the claims, mark which ones you believe are correct with LOW/MEDIUM/HIGH confidence, clearly label that nothing was verified against live sources, and recommend the user re-run when search is available. ## Example Draft sentence: "Since ChatGPT launched in 2023, OpenAI has grown to over 100 million weekly users, making it the fastest-growing app in history." Extraction finds three claims: launch year, user count, superlative. Typical result: launch year FALSE (November 2022), user count NEEDS UPDATE (figure and as-of date from a current source), "fastest-growing app in history" IMPRECISE (widely reported for a period, later surpassed; suggest "one of the fastest-growing consumer apps ever" or tie it to the specific record with dates). One sentence, three verdicts, which is exactly why extraction must happen claim by claim rather than sentence by sentence.