audit
SolidUse when checking a thesis draft before submission for inconsistent numbers, terminology, cross-references, or citation problems.
AI & Automation 28 stars
3 forks Updated 4 days ago MIT
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Skill Content
# /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 helpe...
Details
- Author
- yha9806
- Repository
- yha9806/academic-writing-toolkit
- Created
- 4 months ago
- Last Updated
- 4 days ago
- Language
- Python
- License
- MIT
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