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by InhouseSEO.aiinhouseseo/superseo-skills341 stars
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Use when writing a complete SEO article. Includes the full anti-AI-slop ruleset (banned vocabulary, banned phrases, banned structural patterns) and voice rules. The agent researches the SERP itself if needed — no keyword data exports required.

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Use this Skill: https://skilld.dev/gh/inhouseseo/superseo-skills/write-content

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referencesfact-checking.md

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Technique 08: Fact-Checking & Verification

What It Is

A systematic process to verify every factual claim, statistic, quote, and attribution in AI-assisted content — because AI models confidently fabricate data, invent sources, and present plausible-sounding but incorrect information.

Why It Works

One fabricated statistic destroys an article's credibility. Google's EEAT framework heavily weights Trustworthiness, and factual accuracy is its foundation. More practically: if a reader fact-checks ONE claim and it's wrong, they dismiss the entire article — and bounce back to search results (NavBoost penalty).

AI hallucination rates for factual claims vary by model and domain, but even the best models fabricate 5-15% of specific statistics and quotes. For an article with 20 factual claims, that's 1-3 fabricated "facts" by default.

Step-by-Step Process

Step 1: Claim Extraction

  1. Read the draft and highlight every factual claim:
    • Statistics ("63% of marketers...")
    • Quotes ("According to [name]...")
    • Named studies or reports
    • Historical facts ("Founded in 2019...")
    • Tool capabilities ("Supports up to 100 users...")
    • Comparisons ("3x faster than...")
  2. Create a verification checklist with each claim

Step 2: Source Verification

  1. For each statistic:
    • Can you find the ORIGINAL source (not another blog citing it)?
    • Is the source reputable (academic, government, major research firm)?
    • Is the year current? (Statistics from 2020 may not apply in 2026)
    • Does the exact number match? (AI often rounds or inverts percentages)
  2. For each quote:
    • Did this person actually say this?
    • Is it attributed to the right person?
    • Is it in context? (AI often paraphrases and presents as direct quotes)
  3. For each named study:
    • Does the study exist?
    • Does it say what the article claims it says?
    • Is the author/organization correct?

Step 3: Claim Classification

  1. Classify each claim:
    • Verified: Source found, claim matches
    • Partially correct: Source exists but numbers/context differ
    • Fabricated: No source found, likely hallucinated
    • Unverifiable: First-party data that only the author can confirm
  2. Fix or remove all fabricated claims
  3. Correct all partially correct claims to match actual sources
  4. Flag unverifiable claims for human verification

Step 4: Attribution Standards

  1. Replace all vague attributions:
    • "Studies show..." -> "[Organization]'s [year] study of [N subjects] found..."
    • "Experts agree..." -> "[Name], [title] at [organization], states..."
    • "Research suggests..." -> "A [year] paper in [journal] by [author] found..."
    • "According to industry data..." -> "According to [specific report name]..."
  2. Every statistic must have: source name, year, and ideally a link

Step 5: Currency Check

  1. Flag statistics older than 2 years for review
  2. Note when data was last updated
  3. Add "as of [date]" qualifiers for time-sensitive data
  4. Check if more recent data is available

Tips

  • The "Google Scholar" test: If you can't find a study on Google Scholar, it probably doesn't exist. AI loves citing plausible-sounding studies that were never published.
  • Reverse-search quotes: Paste any direct quote into Google with quotation marks. If it only appears in AI-generated content, it's fabricated.
  • Watch for "statistic laundering": AI often cites a real organization but invents the specific number. "According to HubSpot, 78% of..." — HubSpot exists, but they never published that 78% figure.
  • Common AI fabrication patterns:
    • Round numbers (AI loves 67%, 83%, 91% — exact round numbers)
    • Recent years (AI often cites "[current year - 1]" studies that don't exist)
    • Well-known organizations (AI cites McKinsey, HubSpot, Forrester with invented numbers)

Common Mistakes

  1. Trusting AI citations: Even when AI provides a URL, the page often doesn't exist or doesn't contain the cited information
  2. Skipping first-party data verification: When the human provides data, still verify it makes sense (basic sanity checks)
  3. Verifying against other AI content: If your "source" is another AI-generated article that cites the same fake statistic, you've verified nothing
  4. Removing instead of fixing: Sometimes the claim is directionally correct but the specific number is wrong. Find the real number rather than cutting valuable data points

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    The skill provides a detailed multi-phase framework for researching and generating SEO-optimized articles while minimizing common AI writing patterns. It ingests and processes external data from search engine results and competitor web pages, creating a standard indirect prompt injection surface area. No malicious behaviors, obfuscation, or unauthorized access patterns were detected.

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  • Snyk16d

    Risk: LOW · No issues

Provenance

Signed by skilld at bdb4922. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 weeks ago.

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