# Report Accuracy Auditor

> Fact-check and accuracy-audit a long AI-generated research, legal, tax, or compliance report against primary sources, then (on request) produce a cleaned-up corrected version and a personal action-item/deadline extraction. Use when the user uploads or points at a deep-research deliverable (often a long .md from a research agent or swarm) and asks to "analyze the report", "do an accuracy check", "fact-check this", "verify the claims", "break it down", "find errors", "is this right", or asks for a cleaned-up/corrected report after a review. Covers claim extraction, batched source verification, confirmed/error/unverifiable classification, citation-machinery checks, and the user's standing presentation preference: neutral tone, issues first, good points LAST.

- Skill: `kimiguel/report-accuracy-auditor` (Agent Skill)
- Install (CLI): `npx skillmds@latest add kimiguel/report-accuracy-auditor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kimiguel/report-accuracy-auditor/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: KiMiGuel (https://skillmd.com/u/kimiguel)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kimiguel/report-accuracy-auditor

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# Report Accuracy Auditor

Verify a long AI-generated report claim-by-claim before the user relies on it. The user commissions research from agents (legal/tax/compliance, OSINT, business) and audits the output before acting — accuracy is the whole job, not summary.

## Standing user preferences

- **Order: issues and errors FIRST, strengths/good points LAST.** Never open with praise.
- **Neutral tone.** No cheerleading, no alarmism. State what checks out and what does not.
- Follow-ups usually come in this sequence: audit → "discard what doesn't help / errors" → cleaned-up corrected report → "what does this mean for me / action items / deadlines". Offer the next step; do not start it unasked.

## Workflow

1. **Read the entire report before judging anything.** For long files: `wc -l`, `grep -n "^## "` to map chapters, then read in offset/limit chunks. Do not audit from a partial read.
2. **Extract the checkable claims.** Hard facts only: numbers, thresholds, dollar amounts, dates, statute/case identifiers, "in force / repealed / pending" statuses, penalty figures, named entities. Skip opinions, recommendations, and framing.
3. **Verify in batches.** Fire up to ~4 focused `web_search` queries per call, one per claim cluster. Prefer primary sources: statute/regulation text (eCFR, Cornell LII, official codes), agency pages (IRS, FTB, FinCEN...), court dockets/opinions. If an agency's own marketing page contradicts the current regulation text, trust the regulation text and note the discrepancy.
4. **Classify every claim** into exactly three buckets:
   - **Confirmed** — with the confirming source named.
   - **Error** — quote the report's claim, give the correct value, cite the source, and note how many times the error repeats in the document.
   - **Unverifiable** — say so plainly. Zero search results means unverifiable, never "wrong" and never laundered into "confirmed".
5. **Check the document machinery, not just facts:**
   - Citation architecture: in-text footnote markers must map to the reference list. Per-chapter marker numbering (`[^1^]` restarting each chapter) against one global reference list = broken traceability — flag it.
   - Internal consistency: the same fact stated differently across chapters.
   - Magnitude language: check words like "eight-figure" against the actual number (observed 2026-07-24: an $8.5M verdict repeatedly called "eight-figure" — it is seven-figure).
   - Reference captions: statute captions in the reference list must match what the in-text usage cites them for.
6. **Deliver the audit:** errors/issues first (each with quote → correction → source), then unverifiable items, then what is confirmed and genuinely strong, last. Close with a short concrete fix list.
7. **Cleaned-up version (only when asked):** regenerate the report with errors corrected, unverifiable claims removed or flagged, and citation machinery repaired — preserve the original structure and disclaimers. Then, when asked, extract the user's personal deadlines, obligations, and action items.

## Pitfalls

- Do not fact-check from memory. Thresholds, penalty amounts, and case status shift yearly — every hard claim gets a fresh search, even ones that "look right".
- Do not accept the report's own citations as proof. Reports cite secondary sources (law-firm blogs, vendors); confirm the underlying fact via primary or reputable coverage before marking Confirmed.
- Do not expand scope. Audit the report as delivered; new research directions are a separate task the user will ask for explicitly.
- Do not bury errors inside a balanced narrative. A material error repeated three times in the source is one finding, stated once, with its locations.
- Respect read-only framing: "analyze / accuracy check / break it down" means zero file writes until the user asks for the cleaned-up version.