Launch the Brain Monitor TUI, a real-time terminal dashboard for watching Claudia's memory system. Triggers on "brain monitor", "show dashboard", "memory dashboard", "terminal brain".
Detects code smells, anti-patterns, and readability issues. Use when implementing features, reviewing code, or refactoring.
Use when creating a new AgentSH security policy, making a policy for an agent sandbox, CI pipeline, or development environment, or asking for a new policy YAML file
Configure CCA (Continuous Clearing Auction) smart contract parameters through an interactive bulk form flow. Use when user says "configure auction", "cca auction", "setup token auction", "auction configuration", "continuous auction", or mentions CCA contracts.
Creates Java + Spring Boot projects: Web applications, full-stack apps with Vue.js or Angular or React or vanilla JS, PostgreSQL, REST APIs, and Docker. Use when creating Spring Boot projects, setting up Java microservices, or building enterprise applications with the Spring Framework.
CLI reference for agents — how to claim tasks, log progress, submit for review
Anti-detect browser automation CLI for AI agents. Use when the user needs to interact with websites with bot detection, CAPTCHAs, or anti-bot blocks, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task that requires bypassing fingerprint checks.
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
This plugin is a thin adapter between OpenClaw and LycheeMem. It does not replace `memory-core`, does not claim `plugins.slots.memory`, and does not duplicate LycheeMem algorithms.
Ensure cross-platform compatibility across macOS (Intel/ARM), Ubuntu, and Fedora for this dotfiles repository. Detects and auto-fixes hardcoded paths, platform-specific assumptions, package availability issues, and test coverage gaps. Use when adding features, updating configs, bumping Nix flake, or investigating platform-specific bugs. Keywords: cross-platform, compatibility, macOS, Linux, Ubuntu, Fedora, platform, portability, Nix flake, Docker test, CI
🇺🇸 AI Product Global Launch Playbook — Battle-tested strategies from viral products like Manus & Devin. Complete Product Hunt launch SOP, KOL outreach, UGC growth tactics. 🇨🇳 AI产品全球发布行动指南 — 基于 Manus、Devin 等现象级产品的实战复盘。包含完整的 Product Hunt 发布 SOP、KOL 合作、UGC 增长策略。 🇯🇵 AIプロダクトグローバルローンチガイド — Manus、Devinなどのバイラル製品の実戦経験に基づく。Product Hunt発売SOP、KOLアウトリーチ、UGC成長戦略を含む。 🇰🇷 AI 제품 글로벌 런칭 플레이북 — Manus, Devin 등 바이럴 제품의 실전 경험 기반. Product Hunt 런칭 SOP, KOL 협업, UGC 성장 전략 포함.
/aleph - External memory workflow for large local data.
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:
