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**

   Resolve the bundled helper at `scripts/audit-citations.py` relative to this `SKILL.md`, then run it from the project root:

   `python3 {skill_dir}/scripts/audit-citations.py --base-dir . --style $(grep -oP '(?<=Citation style: )\S+' CLAUDE.md) --json`

   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`).

   Use `python3 {skill_dir}/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.