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Use when checking a thesis draft before submission for inconsistent numbers, terminology, cross-references, or citation problems.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| 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 |
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.
This skill activates on: audit, consistency check, check numbers, /audit.
Scan all chapter files in the chapters/ directory using Glob. Read each file to extract quantitative claims, terminology, and cross-references.
Check the following categories:
A. Numerical consistency
B. Terminological consistency
C. Cross-reference validity
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:
literature/reading_notes/*_NOTES.md. Flags missing or malformed **Source**: lines. Severity medium (notes-source-missing) or medium (notes-source-malformed).**Source**: entry; every Source must be cited at least once. Three modes:
(lastname, year). Phantom and unused → severity high.lastname only.(Smith 2024) and (Smith, 2024)). Severity medium.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.
Output the audit report using the format below.
## 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.}
Use when AI-assisted thesis edits risk claim drift, scope creep, loss of author control, or repeated revisions that fail to converge; provides spine cards, edit contracts, drift audits, revision escalation, and human gates.
Stop repeated failed writing, coding, manuscript, rebuttal, or restructuring revisions when the same issue has gone through 3+ unsatisfactory edits, vague feedback such as still wrong/weird/unclear/weak/越改越乱, version contamination, or possible gap/claim/evidence/venue-fit drift.
Build and audit the manuscript argument system: intent, gap-contribution alignment, hierarchical claims, evidence balance, limitations, and reviewer attack surfaces. Use when a paper, thesis chapter, review article, or rebuttal needs explicit intent registers, contribution chains, claim hierarchy, argument maps, evidence-fit checks, or reviewer-risk matrices before drafting, revision, peer review, or self-review.
Review another author's manuscript, paper, thesis chapter, proposal, or preprint as an external reviewer. Use when asked to evaluate novelty, significance, gap-contribution fit, claim-evidence adequacy, methods, evaluation, overclaim risks, structure, writing, required revisions, or recommendation without rewriting the manuscript or using private author context.
Review the user's own manuscript, paper, thesis chapter, rebuttal, or release packet with clean-room anti-contamination controls. Use when asked for self-review, internal review, pre-submission review, readiness check, reviewer simulation on own work, or claim-evidence self-audit where prior chat memory, unstated assumptions, model background knowledge, or unlisted local notes must not be treated as evidence.
Reframe academic manuscript drafts that read like engineering reports, module inventories, internal validation packets, or system notes into paper-form scientific arguments with clear gap, contribution chain, results narrative, figure/table roles, AI-assisted component boundaries, and submission-readiness blockers.