Scan a project for missing best-practice areas and implement the top recommendation for each gap. Currently covers linting and unit testing. Installs tools, writes configs, and adds CI steps.
Swap between local n-dx development build and published npm version
Local-first code graph builder with 5-signal hybrid search. Use when analyzing codebases, searching for code architecture, exploring dependencies, or building code graphs from source code and documents.
扫描项目技术栈，推荐并安装匹配的 agent skills 套装。Use when starting a new project, onboarding to a codebase, or when the user asks "what skills should I install", "recommend skills for this project", "auto setup skills".
Engineering methodology skill for all tasks. Core behaviors: clarify before assuming, investigate before asking, verify before delivering, review full dependency chains, match existing code style. Trigger on: any task start, code development, debugging, multi-step work. Do NOT skip just because the task looks easy.
Generates a smart daily briefing by analyzing Gmail and Google Calendar.
HERITAGE skill describing the donor bridge between audit trails and memory frames (memory_pack, memory_bundle, memory_verify, context_create, ams_session_resume). DO NOT invoke in Phase 0 — the memory_* family is deferred and there is no "bridge" between audit and memory in Phase 0. The ζ (Decision Trail) + η (Proof Store) half works via audit_session_start / thought_record / audit_verify_chain / merkle_finalize / merkle_root / audit_session_end (6 tools). For active Phase 0 proof-grade work, see the colibri-audit-proof skill and the Phase 0 proof-grade workflow in colibri-mcp-server/references/workflow.md §Workflow 3.
Use when attacking Active Directory environments, hunting Kerberoastable accounts, AS-REP roasting, DCSync, Pass-the-Hash, Pass-the-Ticket, BloodHound path analysis, LDAP enumeration, GPO abuse, ACL abuse, or full AD domain compromise chains. Also use when the user says "attack AD", "domain compromise", "Kerberoast", "DCSync", "BloodHound", or "lateral movement".
Set up and run an autonomous experiment loop for any optimization target. Gathers what to optimize, then starts the loop immediately. Use when asked to "run autoresearch", "optimize X in a loop", "set up autoresearch for X", or "start experiments".
>-
Analyze an Event Model DSL file and (re)write its `slice` declarations from scratch based on the edges currently in the file. Strips any existing slices first so re-running cleanly replaces stale slices instead of accumulating them. Slices follow the four canonical Event Modeling patterns — including the Automation Pattern, which bundles `Event(s) → View → Automation → Command → Event(s)` into a single slice rather than splitting at the automation.
ClawDesk API integration for managing agents, projects, and tasks. Use when interacting with ClawDesk backend at http://localhost:3777 for (1) Listing/creating/updating/deleting agents, (2) Managing projects and tasks, (3) Running tasks on agents, (4) Syncing agents from OpenClaw, (5) Viewing system stats and dashboard.
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:
