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自动化工厂，将 GitHub 仓库转换为标准化的 AI Skill。当用户提供 GitHub URL 并希望"打包"、"封装"或"创建 Skill"时使用此工具。支持自动获取仓库元数据、生成标准目录结构、注入生命周期管理所需的扩展元数据。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.
12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
Generate or edit images using AI models (FLUX, Nano Banana 2). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.
Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.
| name | skill-factory |
| description | 自动化工厂，将 GitHub 仓库转换为标准化的 AI Skill。当用户提供 GitHub URL 并希望"打包"、"封装"或"创建 Skill"时使用此工具。支持自动获取仓库元数据、生成标准目录结构、注入生命周期管理所需的扩展元数据。 |
将任意 GitHub 仓库一键转换为标准化的 Claude Skill。
git ls-remote 获取 commit hash，通过 HTTP 获取 README触发方式:
/skill-factory <github_url>运行 scripts/fetch_github_info.py 获取仓库元数据:
python scripts/fetch_github_info.py <github_url>
输出 JSON 格式:
{
"name": "repo-name",
"owner": "owner",
"url": "https://github.com/owner/repo",
"latest_hash": "abc123...",
"default_branch": "main",
"description": "仓库描述",
"readme": "README 内容..."
}
Agent 分析 README 内容，理解:
运行 scripts/create_skill.py 生成目录结构:
python scripts/create_skill.py <json_file_or_string> <output_dir>
生成的目录结构:
skill-name/
├── SKILL.md # 主 Skill 文件（含扩展元数据）
├── scripts/
│ └── wrapper.py # 占位脚本
├── references/
│ └── .gitkeep
└── assets/
└── .gitkeep
Agent 根据 README 分析结果:
确认:
每个由 skill-factory 创建的 Skill 必须包含以下扩展元数据:
---
name: skill-name
description: 详细描述，包含触发条件
# 生命周期管理字段（必需）
source_url: https://github.com/owner/repo
source_hash: abc123def456...
version: 0.1.0
created_at: 2026-01-23
updated_at: 2026-01-23
evolution_enabled: true
# 可选字段
entry_point: scripts/wrapper.py
dependencies:
- dependency1
- dependency2
---
| 脚本 | 功能 |
|---|---|
scripts/fetch_github_info.py | 获取 GitHub 仓库元数据（轻量级，无需 clone） |
scripts/create_skill.py | 创建标准化 Skill 目录结构 |
scripts/import_github_skill.py | 完整导入 GitHub 仓库为本地 Skill（支持并行下载） |
高效地从 GitHub 仓库下载所有文件并创建本地 Skill。
特点：
--no-source）用法：
# 基本用法
python scripts/import_github_skill.py <github_url> <output_dir>
# 自定义名称
python scripts/import_github_skill.py <github_url> <output_dir> --name my-skill
# 不保留源信息（作为全新本地 Skill）
python scripts/import_github_skill.py <github_url> <output_dir> --no-source
# 组合使用
python scripts/import_github_skill.py https://github.com/user/repo ./skills --name my-skill --no-source
示例：
# 导入并重命名
python scripts/import_github_skill.py https://github.com/PenglongHuang/chinese-novelist-skill ./skills --name chinese-novelist --no-source
source_hash 字段允许 skill-manager 后续检查更新用户: /skill-factory https://github.com/yt-dlp/yt-dlp
Agent:
1. 运行 fetch_github_info.py 获取仓库信息
2. 分析 README，了解 yt-dlp 是视频下载工具
3. 运行 create_skill.py 创建目录
4. 完善 SKILL.md，添加常用命令示例
5. 实现 wrapper.py，封装下载功能
用户: 把 https://github.com/kkroening/ffmpeg-python 打包成 Skill
Agent:
1. 获取仓库信息
2. 分析 README，了解这是 FFmpeg 的 Python 绑定
3. 创建 Skill 目录
4. 完善文档，添加常用转换示例
5. 实现 wrapper.py，封装常用操作