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Import Skills

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huangzesen huangzesen
from GitHub Tools & Productivity
  • 📄 SKILL.md
part how automation

lingtai-anatomy

Reference for how an agent's memory, filesystem, and runtime are organized. Part 1 explains the memory system — the durability hierarchy from conversation to shared library, plus auxiliary layers (soul, token ledger, time veil) and the network-topology layer that lives across stores. Part 2 is the filesystem reference — where manifests, system prompts, history, mailboxes, heartbeats, logs, signal files, and config live, with exact field-level schemas. Part 3 is the runtime anatomy — turn loop, state machine, signal consumption lifecycle, molt mechanics, and mail atomicity. Read Part 1 to understand how knowledge flows between layers; Part 2 when debugging or inspecting on-disk state; Part 3 when reasoning about *how an agent runs*.

⬇0 ❤43 18 days ago · Uploaded Detail →
DimaReverse DimaReverse
from GitHub Development & Coding
  • 📄 EXAMPLES.md
  • 📄 INSTALL.md
  • 📄 OPCODES.md
python nbc reconstruction

nuitka-nbc-rebuilder

Maximum-fidelity Python source reconstruction from Nuitka `.nbc` / `NBC/2` files produced by `nuitka_decompiler.py`. Use when the user pastes or references a `.nbc` file, an `AI_READY_NBC` bundle, sections such as `@MOD`, `@CONSTS`, `@RAW_CHUNK`, `@OPS`, `@ASM`, `@FORENSICS`, `module_code_*`, `mod_consts[N]`, or asks to rebuild Python source from a Nuitka C-compiled module. The goal is evidence-backed reconstruction, not guaranteed perfect 1:1 recovery; uncertain spans must be marked.

⬇0 ❤43 18 days ago · Uploaded Detail →
SamadhiFire SamadhiFire
from GitHub Daily Life
  • 📁 agents/
  • 📁 examples/
  • 📁 references/
  • 📄 .editorconfig
  • 📄 .gitattributes
  • 📄 .gitignore
再持续澄清到关键 团队治理等结构性 实践检验的方法分

maozedong-maoxuan-skill

当用户希望用《毛泽东选集》/教员的方法分析现实问题、把毛选中的方法论转成现代分析框架，或明确提出“用毛选帮我分析”“用教员的方法帮我分析 xxx”“用新青年帮我分析”“按新青年的方法拆一下”“按主要矛盾/阶段判断/统一战线/实践检验的方法分析”时使用。适用于工作推进、复杂协作、关系边界、学习成长、生活决策、团队治理等结构性问题；一旦触发，就默认按复杂结构题处理。澄清是所有任务的硬前提：先锁目标，再持续澄清到关键结构基本清楚，之后才进入分析。澄清题默认使用带 A/B/C/D 和“其他”的选项式追问。正式输出前再确认用户要文字版还是 HTML 版。

⬇0 ❤47 1 month ago · Uploaded Detail →
LukasNiessen LukasNiessen
from GitHub Testing & Security
  • 📁 .claude/
  • 📁 .claude-plugin/
  • 📁 .github/
  • 📄 .gitignore
  • 📄 CHANGELOG.md
  • 📄 CODE_OF_CONDUCT.md
when hallucinations blast-radius

terrashark

Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps. Use when generating, reviewing, refactoring, or migrating IaC and when building delivery/testing pipelines.

⬇0 ❤48 1 month ago · Uploaded Detail →
chengzhongwei chengzhongwei
from GitHub Development & Coding
  • 📁 .github/
  • 📁 docs/
  • 📁 eval/
  • 📄 .gitignore
  • 📄 CHANGELOG.md
  • 📄 CLAUDE.md
ai prompt automation

prompt-sensei

Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.

⬇0 ❤35 9 days ago · Uploaded Detail →
Terryc21 Terryc21
from GitHub Tools & Productivity
  • 📄 SKILL.md
automation security ai

plan

Epic decomposition into trackable, right-sized tasks. Three modes — audit-aware (codebase-audit reports), workflow-audit-aware (handoff.yaml with pre-rated findings), standalone (from scratch). Light convention scanning for projects without CLAUDE.md.

⬇0 ❤48 1 month ago · Uploaded Detail →
cl0nazepamm cl0nazepamm
from GitHub Tools & Productivity
  • 📄 maxscript-3dsmax-objects.md
  • 📄 maxscript-animation-controllers.md
  • 📄 maxscript-common-patterns.md
automation introspection organization

3dsmax-mcp-dev

Rules, tool choices, and workflow patterns for AI agents working with 3ds Max via MCP. Covers the native C++ bridge, plugin introspection, scene organization, material workflows, and MAXScript pitfalls.

⬇0 ❤48 1 month ago · Uploaded Detail →
Gingiris Gingiris
from GitHub Business & Operations
  • 📁 .github/
  • 📁 assets/
  • 📁 references/
  • 📄 CODE_OF_CONDUCT.md
  • 📄 CONTRIBUTING.md
  • 📄 LICENSE
reddit kol sop

gingiris-opensource

🇺🇸 Open Source Launch Marketing Playbook — Complete SOP from strategy to execution. GitHub star growth tactics, KOL partnership lists, Reddit marketing, community distribution across global channels. 🇨🇳 开源项目发布整合营销手册 — 从战略到执行的完整 SOP。GitHub Star 增长策略、KOL 合作清单、Reddit 运营、海外群组分发。 🇯🇵 オープンソースローンチマーケティングガイド — 戦略から実行までの完全SOP。GitHub Star成長戦略、KOLパートナーシップ、Redditマーケティング、グローバルコミュニティ配信。 🇰🇷 오픈소스 런칭 마케팅 플레이북 — 전략부터 실행까지 완벽한 SOP. GitHub 스타 성장 전략, KOL 파트너십, Reddit 마케팅, 글로벌 커뮤니티 배포.

⬇0 ❤48 1 month ago · Uploaded Detail →
moyupeng0422 moyupeng0422
from GitHub Tools & Productivity
  • 📁 assets/
  • 📁 data/
  • 📁 examples/
  • 📄 LICENSE
  • 📄 README.md
  • 📄 SKILL.md
python references automation

法律文件脱敏处理

法律文件脱敏/还原工具 - 将法律文档中的敏感信息进行智能替换和脱敏处理，或将脱敏稿还原为原文 <examples> - 帮我把这份合同脱敏处理 - 我需要脱敏这个法律文件 - 生成脱敏版本的合同文档 - 将这份法律文书中的敏感信息替换掉 - 创建合同的脱敏版本 - 帮我把脱敏稿还原成原文 - 使用比对词还原审核稿 </examples> --- # 法律文件脱敏处理 将法律文档中的敏感信息进行智能替换和脱敏处理，生成可对外分享的脱敏版本。支持将脱敏稿交由外部审核后，使用比对词还原为原文。 ## 核心功能 ### 脱敏功能 - **多种脱敏类型**：名称、日期、价格、文件名、项目名、银行账号、案号等 - **自定义脱敏类型**：创建自定义类型（如"合同名称"、"产品型号"），批量输入精准匹配内容 - **批量模式**：多文件上传自动进入批量模式，统一编号确保跨文件一致性 - **规则设置**：可自主开启/关闭16种内置脱敏类别，灵活控制识别范围 - **智能替换**：根据上下文识别角色（买方/卖方公司） - **实时预览**：黄色高亮显示脱敏内容 - **格式保留**：完整保留原文格式（段落、表格、字体） - **白名单/黑名单管理**：精确控制特定内容的脱敏行为；黑名单支持记录项目类型 - **优先级机制**：黑名单 > 白名单 > 脱敏类别（内置+自定义） - **冲突检测**：添加到列表时自动检测是否已存在于其他列表 - **调试模式**：详细日志输出，便于排查问题 ### 还原功能 - **自动化还原**：根据比对词自动将【X】标记还原为原文 - **批量还原**：支持多文件同时还原，自动匹配文件配对，ZIP打包下载 - **保留审核痕迹**：还原时保留文档中的修订、批注等审核痕迹 - **runs级别替换**：精确替换，不影响其他内容的格式 ## 使用方式 ### HTML离线工具（推荐） #### 脱敏模式 **单文件脱敏：** 1. 打开 `assets/index.html`，选择"脱敏模式" 2. 拖拽或选择单个 docx 文件上传 3. 自动识别并预览脱敏效果 4. 手动编辑脱敏项 5. 导出脱敏文件和比对.md文档 **批量脱敏：** 1. 上传多个 docx 文件，自动进入批量模式 2. 统一识别：相同内容使用相同替换文本 3. 文件切换：通过列表栏切换查看各文件 4. 同步编辑：删除/添加脱敏项会同步到所有文件 5. 导出结果：每个文件生成独立的 `{文件名}_比对.md` #### 还原模式 **单文件还原：** 1. 打开 `assets/index.html`，选择"还原模式" 2. 上传脱敏稿（带审核痕迹的docx） 3. 上传对应的比对.md文件 4. 点击"执行还原"，自动下载还原后的文件 **批量还原（4步流程）：** 1. **上传文件**：上传多个脱敏稿 + 多个比对.md文件 2. **确认配对**：系统自动匹配文件名，支持手动调整 3. **执行还原**：批量处理，显示进度条 4. **下载结果**：ZIP打包下载 ### Python脚本 ```bash # 安装依赖 pip install python-docx # 执行脱敏 python scripts/redact.py input.docx data/rules.json -o output.docx # 执行还原（保留修订、批注） python scripts/restore.py redacted.docx mapping.md -o restored.docx ``` ## 详细文档 - **工作流程**: [references/workflow.md](references/workflow.md) - **规则模式库**: [references/patterns.md](references/patterns.md) - **数据格式**: [references/data-formats.md](references/data-formats.md) - **脚本使用**: [scripts/README.md](scripts/README.md) - **HTML使用**: [assets/README.md](assets/README.md) ## 版本历史 - **v1.5.0（2026-03-29）右键菜单集成 + Python/HTML识别统一**： - **Windows 右键菜单**：右键 .docx 文件可直接"用脱敏工具打开"、"一键脱敏"或"一键还原"，通过注册表集成，无需管理员权限 - **macOS 右键菜单**：通过 Automator Quick Action 实现，Fi

⬇0 ❤48 1 month ago · Uploaded Detail →

Skill File Structure Sample (Reference)

skill-sample/
├─ SKILL.md              ⭐ Required: skill entry doc (purpose / usage / examples / deps)
├─ manifest.sample.json  ⭐ Recommended: machine-readable metadata (index / validation / autofill)
├─ LICENSE.sample        ⭐ Recommended: license & scope (open source / restriction / commercial)
├─ scripts/
│  └─ example-run.py     ✅ Runnable example script for quick verification
├─ assets/
│  ├─ example-formatting-guide.md  🧩 Output conventions: layout / structure / style
│  └─ example-template.tex         🧩 Templates: quickly generate standardized output
└─ references/           🧩 Knowledge base: methods / guides / best practices
   ├─ example-ref-structure.md     🧩 Structure reference
   ├─ example-ref-analysis.md      🧩 Analysis reference
   └─ example-ref-visuals.md       🧩 Visual reference

More Agent Skills specs Anthropic docs: https://agentskills.io/home

SKILL.md Requirements

├─ ⭐ Required: YAML Frontmatter (must be at top)
│  ├─ ⭐ name                 : unique skill name, follow naming convention
│  └─ ⭐ description          : include trigger keywords for matching
│
├─ ✅ Optional: Frontmatter extension fields
│  ├─ ✅ license              : license identifier
│  ├─ ✅ compatibility        : runtime constraints when needed
│  ├─ ✅ metadata             : key-value fields (author/version/source_url...)
│  └─ 🧩 allowed-tools        : tool whitelist (experimental)
│
└─ ✅ Recommended: Markdown body (progressive disclosure)
   ├─ ✅ Overview / Purpose
   ├─ ✅ When to use
   ├─ ✅ Step-by-step
   ├─ ✅ Inputs / Outputs
   ├─ ✅ Examples
   ├─ 🧩 Files & References
   ├─ 🧩 Edge cases
   ├─ 🧩 Troubleshooting
   └─ 🧩 Safety notes

Why SkillWink?

Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.

We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.

Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

Quick Start:

Import/download skills (.zip/.skill), then place locally:

~/.claude/skills/ (Claude Code)

~/.codex/skills/ (Codex CLI)

One SKILL.md can be reused across tools.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.

Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.

2. How do Skills work?

Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.

This keeps agents lightweight while preserving enough context for complex tasks.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared across tools?

Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.

Example: retrieval + writing + automation scripts as one workflow.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up