(colcon/ament), edits launch files (.launch.py), configures QoS or DDS, writes URDF/xacro,
State-machine driven iterative planning and execution for complex coding tasks.
Email inboxes for AI agents - create addresses, send and receive email, manage threads
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.
Summarizes B2B account health by analyzing usage patterns, engagement trends, risk signals, and expansion opportunities. Use for customer success reviews, renewal preparation, QBRs, or account prioritization.
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.
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.
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.
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Bootstrap and iterate TypeScript/TSX JupyterLab plugins in Plugin Playground for plugin development, using command-driven workflows and extension references.
Nightly pipeline for integrating newly captured external article notes into Brain knowledge surfaces. Use when: 文章整合, article notes integration, nightly article sync, update article relations, topic index update, article knowledge graph, 前一天文章整理, 或 run the 02:00 article pipeline. --- # Article Notes Integration 把前一天新增或待整合的 Article Notes，转成可检索、可关联、可继续提炼的 Brain 知识输入层。 ## Purpose 这个技能负责 **文章 ingestion 之后的 nightly integration**，而不是原始外部文章采集本身。 它处理的是： 1. 扫描昨天新增或尚未 integrated 的 article notes 2. 校验并补足结构 / frontmatter / relation 状态 3. **交叉引用更新**（见下方 Cross-Reference Protocol，每次 ingest 后执行） 4. 更新 topic / domain / project 相关的轻量图谱入口 5. 生成 open questions / pattern candidates / article-derived graph signals 6. 输出高价值 article candidates，供后续 flywheel amplification 使用 ## Primary Inputs - Brain root: `{{BRAIN_ROOT}}` - Source notes: `03-KNOWLEDGE/02-WORKING/01-ARTICLE-NOTES/` - Candidate set: - 前一天新增 article notes - 或 `integration_status != integrated` 的 article notes - Read-only context: - related domain notes - `03-KNOWLEDGE/99-SYSTEM/01-INDEXES/` 下已有 topic / topic-map / open-question surfaces - `05-PROJECTS/` 下 project briefs（若能稳定识别项目） ## Required Outputs
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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:
