maestro-help
Maestro Flow 命令帮助系统。搜索命令、浏览技能、工作流推荐、新手引导。Triggers on "maestro-help", "帮助", "命令", "怎么用", "skill", "workflow", "maestro 怎么用".
Remove AI writing patterns from academic prose. Detects and fixes inflated symbolism, promotional language, superficial analyses, vague attributions, AI vocabulary, and formulaic structures. Supports English and Chinese. Triggers on "remove AI patterns", "humanize text",
$ npx -y skills add catlog22/maestro-flow --skill scholar-anti-ai-writing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/scholar-anti-ai-writingContext preview
The summary Claude sees to decide when to auto-load this skill.
Remove AI writing patterns from academic prose. Detects and fixes inflated symbolism, promotional language, superficial analyses, vague attributions, AI vocabulary, and formulaic structures. Supports English and Chinese. Triggers on "remove AI patterns", "humanize text",
name: scholar-anti-ai-writing disable-model-invocation: true description: Remove AI writing patterns from academic prose. Detects and fixes inflated symbolism, promotional language, superficial analyses, vague attributions, AI vocabulary, and formulaic structures. Supports English and Chinese. Triggers on "remove AI patterns", "humanize text", "anti-AI polish", "去除AI写作痕迹", "人性化处理". allowed-tools: Read, Write, Edit, Bash, Glob, Grep, AskUserQuestion, TodoWrite session-mode: none
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.
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
User: "Remove AI patterns from my paper"
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┌──────────────────────────────────────────────────────────────┐
│ SKILL.md (Orchestrator) │
│ Collect preferences → Dispatch phases → Track progress │
└──────────┬───────────────────────────────────────────────────┘
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┌───────┼───────────┬──────────────┐
v v v v
┌──────┐┌──────────┐┌──────────────┐
│ P1 ││ P2 ││ P3 │
│Detect││ Rewrite ││ Validate │
│ & ││ & Polish ││ & Score │
│Score ││ ││ │
└──┬───┘└────┬─────┘└──────┬───────┘
│ │ │
v v v
pattern polished final score
report prose + diff report1. **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.
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.
When `workflowPreferences.autoYes === true`:
> **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`.
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 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](phases/01-detect-score.md) | Scan patterns, score text | TodoWrite driven | | 2 | [phases/02-rewrite-polish.md](phases/02-rewrite-polish.md) | Rewrite and humanize | TodoWrite driven + sentinel | | 3 | [phases/03-validate-score.md](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**
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.
| Dimension | Question | Target | |-----------|----------|--------| | **Directness** | Direct statements or announcements? | >= 7 | | **Rhythm** | Varied or metronomic? | >= 7 | | **Trust** | Respects reader i
Intent-driven workflow orchestration for multi-agent AI development — adaptive lifecycle engine, self-reinforcing knowledge graph, and visual dashboard for Claude Code, Gemini, Codex & more
Repo: catlog22/maestro-flow
Maestro Flow 命令帮助系统。搜索命令、浏览技能、工作流推荐、新手引导。Triggers on "maestro-help", "帮助", "命令", "怎么用", "skill", "workflow", "maestro 怎么用".
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