Scholar Anti-AI Writing

Detect and eliminate AI writing patterns from academic prose. Based on Wikipedia's "Signs of AI writing" guide. Supports both English and Chinese text.

Core insight: LLMs predict the most statistically likely outcome — creating detectable patterns. This skill identifies and rewrites those patterns while preserving meaning and adding authentic voice.

Pre-load (before execution)

1. Codebase docs: If .workflow/codebase/ARCHITECTURE.md exists, read for project context
2. Specs: maestro load --type spec --category coding — load coding conventions
3. Wiki knowledge: maestro search "academic writing research paper" --json — top 5 entries as prior context
4. All optional — proceed without if unavailable

Architecture Overview

User: "Remove AI patterns from my paper"
         |
         v
┌──────────────────────────────────────────────────────────────┐
│  SKILL.md (Orchestrator)                                      │
│  Collect preferences → Dispatch phases → Track progress       │
└──────────┬───────────────────────────────────────────────────┘
           |
   ┌───────┼───────────┬──────────────┐
   v       v           v              v
┌──────┐┌──────────┐┌──────────────┐
│ P1   ││ P2       ││ P3           │
│Detect││ Rewrite  ││ Validate     │
│ &    ││ & Polish ││ & Score      │
│Score ││          ││              │
└──┬───┘└────┬─────┘└──────┬───────┘
   │         │             │
   v         v             v
 pattern   polished     final score
 report    prose        + diff report

Key Design Principles

1. Pattern detection + soul injection: Removing AI patterns is only half the job — add authentic voice.
2. Preserve meaning: Core content and technical claims must remain intact.
3. Language-aware: English and Chinese have different AI pattern signatures.
4. Scoring-driven: Quantitative 5-dimension scoring (50-point scale) guides revision.
5. Academic tone: Maintain scholarly register — humanize without becoming informal.

Interactive Preference Collection

Before dispatching to any phase, collect these preferences:

Questions to ask the user:

1. Input Source
   "What text should I process? (file path, directory, or paste text)"
   → inputSource

2. Language
   Options: English | Chinese | Bilingual (auto-detect per section)
   → language

3. Writing Context
   Options: Academic Paper | Technical Report | Blog/Article | General Prose
   → writingContext

4. Aggressiveness
   Options: Conservative (minimal changes) | Balanced (Recommended) | Aggressive (heavy rewrite)
   → aggressiveness

Store responses as antiAiPreferences context for all phases.

Auto Mode Defaults

When workflowPreferences.autoYes === true:

• Process all .tex files in cwd
• Auto-detect language
• Academic Paper context, Balanced aggressiveness
• Skip confirmation prompts

Execution Flow

COMPACT DIRECTIVE: Context compression MUST check TodoWrite phase status. The phase currently marked in_progress is the active execution phase — preserve its FULL content. Only compress phases marked completed or pending.

TodoWrite Setup

Anti-AI Writing:
- [ ] Phase 1: Detect & Score — scan for AI patterns, generate initial score
- [ ] Phase 2: Rewrite & Polish — rewrite flagged sections, add voice
- [ ] Phase 3: Validate & Score — re-score, verify quality threshold

Phase Sequence

Phase 1: Detect & Score
   └─ Ref: phases/01-detect-score.md
      ├─ Input: inputSource, language
      └─ Output: patternReport (flagged passages + initial scores)

Phase 2: Rewrite & Polish
   └─ Ref: phases/02-rewrite-polish.md
      ├─ Input: patternReport, writingContext, aggressiveness
      └─ Output: polishedText (rewritten content)

Phase 3: Validate & Score
   └─ Ref: phases/03-validate-score.md
      ├─ Input: polishedText, original text
      └─ Output: finalReport (before/after scores, diff)

Phase Reference Documents (read on-demand when phase executes):

Phase	Document	Purpose	Compact
1	phases/01-detect-score.md	Scan patterns, score text	TodoWrite driven
2	phases/02-rewrite-polish.md	Rewrite and humanize	TodoWrite driven + sentinel
3	phases/03-validate-score.md	Re-score, generate report	TodoWrite driven

Compact Rules:

1. TodoWrite in_progress → preserve full content, do not compress
2. TodoWrite completed → may compress to summary
3. sentinel fallback → phases marked with sentinel contain compact sentinel; if only sentinel remains, must immediately Read() to recover

Core Rules

1. Never lose meaning: Technical claims, data, and specific facts must survive rewriting.
2. Score before and after: Every processed text gets a 5-dimension score (50-point scale).
3. Flag, don't force: In Conservative mode, highlight issues but let the user decide.
4. Academic register: For papers, maintain formal-but-natural tone. No slang or casual language.
5. Minimum threshold: Target score >= 35/50 for academic papers, >= 40/50 for submission-ready.

Quick Scoring System (5 dimensions, 10 points each)

Dimension	Question	Target
Directness	Direct statements or announcements?	>= 7
Rhythm	Varied or metronomic?	>= 7
Trust	Respects reader intelligence?	>= 7
Authenticity	Sounds human?	>= 7
Density	Anything cuttable?	>= 7

Data Flow

Phase 1 ──patternReport──→ Phase 2
Phase 2 ──polishedText──→ Phase 3
Phase 1 ──originalText──→ Phase 3 (for comparison)

Error Handling

Error	Action
File not found	Ask user for correct path
Mixed languages in one file	Process each section in detected language
Score below threshold after rewrite	Flag for manual review, suggest specific areas
LaTeX commands broken during rewrite	Preserve LaTeX structure, only modify prose content
User disagrees with changes	Offer side-by-side diff, allow per-section accept/reject

Related Skills

• scholar-writing: Phase 5 (Anti-AI Polish) uses this skill's patterns
• scholar-review: Uses scoring to assess writing quality