audit

name	audit
description	Use when checking a thesis draft before submission for inconsistent numbers, terminology, cross-references, or citation problems.
allowed-tools	Read, Glob, Grep, Bash

/audit — Thesis Consistency Audit Skill

Purpose

Scan all thesis chapters for internal data consistency issues: contradictory numbers, inconsistent terminology, broken cross-references, and arithmetic errors. This is a pre-submission quality check.

Trigger Words

This skill activates on: audit, consistency check, check numbers, /audit.

Workflow

1. Scan all chapter files in the chapters/ directory using Glob. Read each file to extract quantitative claims, terminology, and cross-references.

2. Check the following categories:

   A. Numerical consistency

   • The same statistic (e.g., accuracy, sample size, p-value) cited in multiple chapters must have the same value.
   • Percentages in a distribution must sum to 100% (with tolerance of +/-1% for rounding).
   • Counts (e.g., "42 models") must match between chapters.

   B. Terminological consistency

   • The same concept must use the same term throughout. Flag cases where synonyms are used inconsistently (e.g., "structured review" vs "systematic review" for the same concept).
   • Abbreviations must be defined on first use in each chapter.

   C. Cross-reference validity

   • References to other sections (e.g., "as discussed in Section 3.2") must point to sections that exist.
   • References to tables and figures must match actual table/figure numbers.
   • Forward references ("Chapter 6 will show...") must be fulfilled.

   D. Citation consistency

   Run python3 scripts/audit-citations.py --base-dir . --style $(grep -oP '(?<=Citation style: )\S+' CLAUDE.md) --json and parse the JSON output. The script implements four tiers:

   • Tier 0 — Source-line lint over literature/reading_notes/*_NOTES.md. Flags missing or malformed **Source**: lines. Severity medium (notes-source-missing) or medium (notes-source-malformed).
   • Tier 1 — Pairing. Every in-text citation must match a **Source**: entry; every Source must be cited at least once. Three modes:
     • Author-Year (Harvard, APA, Chicago Author-Date, GB/T 7714-2015): pair on (lastname, year). Phantom and unused → severity high.
     • Author-Page (MLA): pair on lastname only.
     • Numeric (IEEE, Vancouver): pair on count balance + integer-gap detection.
   • Tier 2 — Style mode detection across all in-text citations. Flags outliers when the manuscript drifts (e.g. mixed (Smith 2024) and (Smith, 2024)). Severity medium.
   • Tier 3 — Per-style format validation against the declared Citation style: in CLAUDE.md. Flags wrong-comma, et al. threshold violations, wrong multi-author connector. Severity low.

   The script's exit code is 0 (no issues), 1 (issues at any tier), or 2 (invalid arguments). Add the script's issues to the Issues table below as new rows; severity vocabulary maps directly (critical | high | medium | low | info).

   See docs/skills/06-audit.md and python3 scripts/audit-citations.py --help for the public citation-audit interface and supported styles.

3. Output the audit report using the format below.

Output Format

## Audit Report -- {YYYY-MM-DD}

### Summary

- **Critical**: {N} issues (contradictory data)
- **High**: {N} issues (broken references, missing definitions)
- **Medium**: {N} issues (terminology inconsistency, minor arithmetic)

### Issues

| # | Severity | Category | Location | Issue | Current | Expected |
|---|----------|----------|----------|-------|---------|----------|
| 1 | Critical | Numerical | Ch3 s3.2, Ch5 s5.4 | Sample size differs | 120 (Ch3) vs 125 (Ch5) | Should be consistent |
| 2 | High | Cross-ref | Ch4 s4.1 | Ref to "Section 3.7" | Section 3.7 | Section does not exist |

### Recommendations

{Grouped by severity, brief notes on how to resolve each issue.}

Severity Levels

• Critical: The same quantitative claim has different values in different chapters. This directly undermines thesis credibility.
• High: Broken cross-references, undefined abbreviations on first use, missing table/figure numbers.
• Medium: Inconsistent terminology that does not cause factual error, minor rounding discrepancies within tolerance.

Constraints

1. Never auto-fix. List all issues for the user to review and decide. The user may choose to fix selectively.
2. No emoji in output.
3. Report all instances, not just the first occurrence. If a statistic appears in 4 chapters with 2 different values, list all 4 locations.
4. Be specific about locations. Provide chapter number, section number, and surrounding context so the user can find the issue quickly.
5. Do not flag stylistic issues. This skill checks data consistency, not prose quality.