- 📄 SKILL.md
analyse
Analyze a GitHub issue and generate an implementation plan. Usage: /analyse [#N]
Analyze a GitHub issue and generate an implementation plan. Usage: /analyse [#N]
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Split ADRs, PRDs, and implementation plans into strict TDD micro-tasks with explicit execution order for autonomous coding agents
Turn planning outputs into a milestone-based implementation plan that sequences features, maps them to available packages or modules, and hands off to a repo-local build skill.
Analyze collected requirements from interviews, detect ambiguities, generate structured specifications, and suggest implementation approaches
Generate phased implementation roadmaps from Architecture Decision Records
Token-saving execution layer for OpenClaw v2.0. Runs skill commands in sandboxed subprocesses where only compact summaries enter the context window. Provides session continuity via SQLite event tracking that survives conversation compaction. Supports intent-driven filtering, batched multi-skill execution, and progressive memory loading. Includes automated installer that wires context-saver into AGENTS.md, TOOLS.md, and cron jobs with a single command. Use this skill to wrap any data-heavy operation to reduce token consumption by 70-98%.
Use after completing implementation to find unknown failure modes. Reads implementation diff and writes up to 5 tests designed to make it break. Triggers on 'break it', 'adversarial test', 'stress test implementation', 'find weaknesses', or any task seeking to expose unknown failure modes.
Turn ideas into designs through collaborative dialogue. Use when user wants to brainstorm, design features, explore approaches, or think through implementation before coding.
Use this skill when the user wants Codex to hand off a task to GitHub Copilot CLI LongRun for unattended execution, prompt generation, status checks, or resuming previous long-running missions. Trigger on requests mentioning longrun, Copilot CLI long tasks, resumable missions, or asking Codex to launch Copilot as the execution backend.
Use this skill when the user wants Codex to hand off a task to GitHub Copilot CLI LongRun for unattended execution, prompt generation, status checks, or resuming previous long-running missions. Trigger on requests mentioning longrun, Copilot CLI long tasks, resumable missions, or asking Codex to launch Copilot as the execution backend.
Lightweight packaged frontend implementation guide for AI Dev Hub. Use for UI-heavy subtasks such as pages, dashboards, forms, landing sections, component composition, responsive layout, interaction polish, and frontend integration work that should stay aligned with the local project codebase.
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
├─ ⭐ 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
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.
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.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
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.
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.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused 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.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills: