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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 8 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 9 days ago · Uploaded Detail →
yogirk yogirk
from GitHub Tools & Productivity
  • 📁 agent-council-nudge/
  • 📁 council-list/
  • 📁 council-nudge/
  • 📄 SKILL.md
ai automation question

council

Convene a panel of CLI-based AI agents (Codex, Gemini) to deliberate on a question. Each agent answers independently, then you synthesize the council's verdict as chairman. Use for architecture decisions, code review, debugging hypotheses, or any question where diverse perspectives add value.

⬇0 ❤56 27 days ago · Uploaded Detail →
RConsortium RConsortium
from GitHub Research & Analysis
  • 📄 SKILL.md
auto-discover pharma-skills performance

benchmark-runner

Auto-discover all skills with evals in RConsortium/pharma-skills, benchmark each with vs. without skill using matched isolated sessions, and post scored results to the linked GitHub issue. Use whenever someone says "run benchmarks", "compare skill performance", "eval the skills", or wants to measure whether a skill improves output quality.

⬇0 ❤47 11 days ago · Uploaded Detail →
frap129 frap129
from GitHub Tools & Productivity
  • 📄 SKILL.md
automation files rules

crafting-rules

Use when creating or modifying OpenCode rules (.md/.mdc files) that customize agent behavior. Trigger when user wants to create a rule, codify repeated instructions, persist guidance across sessions, or scope rules to specific files, prompts, environments, or workflows.

⬇0 ❤57 1 month ago · Uploaded Detail →
MitchellkellerLG MitchellkellerLG
from GitHub Tools & Productivity
  • 📁 baselines/
  • 📁 ground-truth/
  • 📁 knowledge/
  • 📄 .gitignore
  • 📄 LICENSE
  • 📄 README.md
automation research web

research-process-builder

Build validated web research processes through self-annealing loops. Takes a research goal, generates search steps, tests against sample companies, scores accuracy, and iterates until 90%+. Use when creating new research workflows, building claygent/agent prompts, or systematizing any web research task.

⬇0 ❤57 1 month ago · Uploaded Detail →
fiyo fiyo
from GitHub Databases & Storage
  • 📁 i18n/
  • 📁 rag/
  • 📁 skill/
  • 📄 .env.example
  • 📄 .gitattributes
  • 📄 .gitignore
database backend data

dbcheck

执行 MySQL、PostgreSQL、Oracle、SQL Server、DM8、TiDB 数据库健康巡检，内置 130+ 条增强风险分析规则 + 慢查询深度分析引擎 + 本地 Ollama AI 大模型诊断建议，一键生成专业 Word 巡检报告。适用于 DBA 和运维人员快速掌握数据库运行状况、排查风险。项目地址：https://github.com/fiyo/DBCheck.git

⬇0 ❤23 3 days ago · Uploaded Detail →
stephenleo stephenleo
from GitHub Tools & Productivity
  • 📁 assets/
  • 📁 references/
  • 📁 scripts/
  • 📄 SKILL.md
sprint bad run

bad

BMad Autonomous Development — orchestrates parallel story implementation pipelines. Builds a dependency graph, updates PR status from GitHub, picks stories from the backlog, and runs each through create → dev → review → PR in parallel — each story isolated in its own git worktree — using dedicated subagents with fresh context windows. Loops through the entire sprint plan in batches, with optional epic retrospective. Use when the user says "run BAD", "start autonomous development", "automate the sprint", "run the pipeline", "kick off the sprint", or "start the dev pipeline". Run /bad setup or /bad configure to install and configure the module.

⬇0 ❤54 22 days 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