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data-fidelity

Research
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Fact-checking and source verification workflow for research documents. Launches parallel fact-checkers, aggregates findings, applies corrections systematically.

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How to use this skill

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/analysis/data-fidelity/SKILL.md

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Data Fidelity

Activates when research documents need verification, fact-checking, or data updates.

When to Use

  • After initial research documents are written (quality gate before publish)
  • When refreshing documents with current data (market prices, company stats)
  • When a user says "check for errors", "verify claims", "fact check", "add fidelity"

Workflow

Phase 1: Fact-Check Fleet

Launch 2-3 parallel fact-checker agents, each covering a different document range:

  • Agent A: docs 01-12
  • Agent B: docs 13-24
  • Agent C: forward-looking/speculative docs (if any)

Phase 2: Aggregate Findings

Collect reports. Categorize by severity:

  • ERROR — factually wrong, must fix
  • QUESTIONABLE — might be wrong, needs verification
  • INCONSISTENCY — contradicts another document

Phase 3: Data Refresh

Launch market-researcher agents for current pricing, company data, and industry stats.

Phase 4: Apply Corrections

Systematic edit pass:

  1. Fix all ERRORs first
  2. Resolve INCONSISTENCYs (pick the correct value, update all occurrences)
  3. Update data with fresh market research
  4. Flag QUESTIONABLE items that couldn't be resolved

Phase 5: Rebuild

If documents have HTML/PDF output, rebuild after corrections:

bash build.sh

Quality Standards

  • Every numerical claim should have a source or explicit reasoning
  • Cross-document consistency: same number must be the same everywhere
  • Dates should be specific (not "recently" — say "March 2026")
  • Company names should be current (post-merger names, current HQ)
  • Legal citations should include statute number (USC, CFR, RCW)