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thesis-writer

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  • License — License: MIT
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  • Active repo — Last push 0 days ago
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SUMMARY

AI-proof thesis-writing skill for Claude Code: writes chapters, scans for 14 AI writing patterns (23-point framework — 14 to avoid + 9 to apply), chapter-aware scoring, and viva prep. Based on Wikipedia 'Signs of AI writing'.

README.md

thesis-writer

Write publication-quality MSc/PhD thesis chapters that pass examiner scrutiny and AI detection tools.

What it does

  • 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

Install

# Claude Code
claude skill add /path/to/thesis-writer

# Or clone and point to it
git clone https://github.com/futureweapons2022-crypto/thesis-writer.git ~/.claude/skills/thesis-writer

Usage

# In Claude Code
/thesis-writer write chapter 2 section 2.1

/thesis-writer scan my-chapter.docx

/thesis-writer prepare for viva

Standalone AI scanner

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

What's inside

thesis-writer/
├── SKILL.md                        # Main skill (3 modes: Write, Scan, Fix)
├── README.md
├── LICENSE
├── references/
│   ├── ai-patterns.md              # 14 patterns to avoid + 9 human-writing signals
│   ├── 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)

The 14 AI patterns it catches

# 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

Anti-AI detection approach

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.

Research basis

The patterns are grounded in published work — and deliberately updated as the models change:

  • Lexical tells (the banned-word list) come from Kobak et al., Delving into LLM-assisted writing in biomedical publications through excess vocabulary, Science Advances 2025 (arXiv:2406.07016) — a 14M-abstract study of which words surged after ChatGPT (delves ×25, showcasing ×9, underscores ×9).
  • These tells decay. "delve" peaked in 2023–24 and faded through 2025 as labs trained it out, so current-gen models lean on formulaic transitions instead — "In conclusion," "It is important to note," "Notably" — flagged separately (pattern 29).
  • The durable signals are structural, not lexical: sentence-length burstiness, clause-density variation, and punctuation entropy (pattern 28). Stylometric research (arXiv:2511.21744, arXiv:2510.00890) finds combined structural features reach F1 ≈ 0.94, well above perplexity alone — and unlike word lists, they don't expire.

Honest caveat: even the best stylometric detectors top out at ~80–95% accuracy and drop when text is edited. Treat this as a guide, not proof.

Credits

License

MIT

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