Write publication-quality MSc/PhD thesis chapters that pass examiner scrutiny and AI detection tools.
- Writes thesis chapters following academic conventions, with proper hedging, citations, and chapter structure
- Scans existing text for 14 AI writing patterns (based on Wikipedia: Signs of AI writing)
- Fixes flagged patterns while preserving meaning and citations
- Prepares for viva with 35+ categorised examiner questions
# Claude Code
claude skill add /path/to/thesis-writer
# Or clone and point to it
git clone https://github.com/YOUR_USERNAME/thesis-writer.git ~/.claude/skills/thesis-writer# In Claude Code
/thesis-writer write chapter 2 section 2.1
/thesis-writer scan my-chapter.docx
/thesis-writer prepare for viva
python scripts/ai_scan.py thesis_draft.docx
python scripts/ai_scan.py --text "paste your text here"Output:
============================================================
THESIS AI SCAN REPORT
============================================================
Score: 72/100
Words: 2515 | Sentences: 89 | Paragraphs: 29
Violations: 2 critical, 3 medium, 1 minor
VIOLATIONS:
❌ [CRITICAL] Pattern 1: Banned vocabulary
Found: "comprehensive" (2x)
Fix: Replace with a plain alternative
⚠️ [MEDIUM] Pattern 2: However chain
Detail: 5 transition starters in 2515 words (2.0 per 1000)
Fix: Use subordinate clauses or plain conjunctions
thesis-writer/
├── SKILL.md # Main skill (3 modes: Write, Scan, Fix)
├── README.md
├── LICENSE
├── references/
│ ├── ai-patterns.md # 14 AI patterns with before/after examples
│ ├── chapter-rules.md # Chapter-by-chapter writing rules
│ └── viva-questions.md # 35+ examiner questions by difficulty
└── scripts/
└── ai_scan.py # Standalone AI pattern scanner (stdlib only)
| # | Pattern | Severity | Example tell |
|---|---|---|---|
| 1 | Banned vocabulary | Critical | "delve," "comprehensive," "leverage" |
| 2 | However chain | Critical | However, / Moreover, / Furthermore, back-to-back |
| 3 | Uniform sentence length | Critical | Every sentence 20-25 words |
| 4 | Copula avoidance | Medium | "serves as" instead of "is" |
| 5 | Superficial -ing endings | Medium | "highlighting the importance of..." |
| 6 | Rule of three | Minor | "X, Y, and Z" in every paragraph |
| 7 | Synonym cycling | Medium | Same concept, different words each time |
| 8 | Negative parallelisms | Minor | "Not just X, but also Y" |
| 9 | False ranges | Minor | "From X to Y, from A to B" |
| 10 | Em dash overuse | Minor | More than 2 per page |
| 11 | Filler phrases | Minor | "It is worth noting that..." |
| 12 | Formulaic endings | Medium | "These findings highlight the importance..." |
| 13 | Generic attribution | Medium | "Several studies have shown..." |
| 14 | Descriptive lit review | Critical | Listing studies without comparing them |
Based on how detectors work:
- Perplexity: AI text is too predictable. The skill forces less obvious word choices.
- Burstiness: AI sentences are too uniform. The skill enforces dramatic length variation (5-word and 40-word sentences in the same paragraph).
Non-native English speakers face 61% false positive rates in AI detectors. This skill is especially useful for international students.
- AI patterns based on Wikipedia: Signs of AI writing by WikiProject AI Cleanup
- Viva questions compiled from University of Calgary, Oxford, and Edinburgh examiner guides
- Skill structure follows the claude-skills authoring standard
MIT