| name | data-fidelity |
| description | Fact-checking and source verification workflow for research documents. Launches parallel fact-checkers, aggregates findings, applies corrections systematically. |
| version | 1.0.0 |
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:
- Fix all ERRORs first
- Resolve INCONSISTENCYs (pick the correct value, update all occurrences)
- Update data with fresh market research
- 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)
