Manage X/Twitter bookmarks locally with Field Theory CLI. Actions: sync, search, list, classify, stats, viz, l2-label, review queue. Keywords: x bookmarks, twitter bookmarks, fieldtheory, ft.
Reference guide covering decision heuristics for building agents on the Claude API, including tool surface design, context management, caching strategies, and composing tool calls
Minimize token waste in all bash, file, and data processing operations. ALWAYS apply these principles whenever executing commands, reading files, or processing output. This skill governs how an LLM agent interacts with the system — it applies to every task involving tool use, file reading, data processing, or shell commands. TRIGGER on any computer use whatsoever. --- # Token-Efficient Computer Use Every character of stdout returned from a tool call gets tokenized and billed. A single careless `cat` on a 2000-line JSON file costs as much as a thoughtful conversation turn. The goal isn't to memorize specific commands — it's to internalize a simple cost model: **each byte of tool output is money and context window spent**, and each tool call has fixed round-trip overhead on top of that. Two questions should precede every tool call: 1. **Can I avoid this call entirely?** If the information is already in context from a previous read or from the user, use it. 2. **If I must call, how do I minimize the bytes returned?** Filter, project, truncate, or count at the source rather than dumping raw output.
E2E 除錯說明書——用 agent-browser CLI 在 DDD 工作流中系統性地除錯前端問題。 Use when E2E tests fail, when you need to visually verify UI behavior, debug form submissions, inspect page state, or trace frontend issues during "/DDD.work". Also use when the user says "debug E2E", "check the page", "why is the test failing", "open the browser", "take a screenshot", "inspect the DOM", or invokes "/DDD.agent-browser". --- # DDD:AgentBrowser — E2E 除錯說明書 在 DDD 工作流的開發階段（`/DDD.work`），當 Playwright E2E 測試失敗或需要視覺驗證時， 用 `agent-browser` CLI 直接操作瀏覽器來定位問題。 這份說明書不是瀏覽器自動化教學，而是**除錯流程指南**——幫你從「測試掛了」走到「找到根因」。 ## 核心除錯循環 ``` 測試失敗 → 重現場景 → 觀察狀態 → 定位根因 → 修正 → 驗證 ``` 每一步都有對應的 agent-browser 指令，按順序走就對了。 ## Step 1：重現場景 先把瀏覽器帶到測試失敗的那個畫面。 ```bash # 開啟目標頁面
[UDS] 分析 AC 與測試的追蹤關係及覆蓋率
教用户如何使用 taro-shadcn CLI 初始化项目和添加组件。当用户询问 CLI 用法或如何创建组件时调用。
エージェント・コマンド・フックの品質を監査しレポート生成。プラグインの整合性、定義の漏れ、設定の不備を検出する。「監査して」「プラグインの状態を確認」「整合性チェック」で発動。
Activate Multi-Agent Advisor Mode via AI-Connect (Salomo Principle). User calls /advisor to enter polling loop.
生成定制化的构建项目 Skill（含 SKILL.md + references/），为用户的构建过程提供从规划到结项的 持续引导。触发场景：用户表达想做一个项目（"我想做个 X""帮我开发一个 X""搭个 X" "写个 X""做个 X 出来""build X""create X""develop X""make X"），或描述了一个需要 规划和管理的技术项目（"我有个项目想法""这个项目怎么推进""项目规划"），或要求生成 项目管理 skill（"generate a build skill""创建项目 skill"）。涵盖软件开发、Skill 开发、 技术方案落地、系统搭建、工具制作等构建类项目。安装本 skill 即表明用户倾向于系统化 项目管理——遇到构建意图时优先触发，而非直接进入即时编码。
初始化专用技能。仅在用户显式输入 `$bingo-spec-coding-max-skill` 时使用。将项目初始化为 Spec 驱动结构，创建 AGENTS.md、spec 目录及模板，并把 doc 目录下的提示词与示例注入到 spec/prompts 与 spec/usage。支持中文或英文 spec 环境，默认中文。支持 Windows 与 macOS，默认 dry-run，使用 --apply 才落地。
Vehicle control with LQR lateral and PID/MRAC longitudinal controllers. Read trajectory/chassis/location, output control commands and tune parameters.
This skill should be used when the user asks to "create a hedera plugin", "build a hedera agent kit plugin", "extend hedera agent kit", "create custom hedera tools", "add hedera functionality", "write a hedera tool", "implement hedera tool", or needs guidance on Hedera Agent Kit plugin architecture, tool definitions, mutation tools, query tools, or parameter schemas using Zod.
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:
