Use ONLY for abstract DataChain SDK questions — API usage, method signatures, or code patterns — when no specific dataset or bucket is referenced. If the request mentions creating, saving, listing, exploring datasets or buckets, use datachain-knowledge instead.
Reverse-engineer and clone one or more websites in one shot — extracts assets, CSS, and content section-by-section and proactively dispatches parallel builder agents in worktrees as it goes. Use this whenever the user wants to clone, replicate, rebuild, reverse-engineer, or copy any website. Also triggers on phrases like "make a copy of this site", "rebuild this page", "pixel-perfect clone". Provide one or more target URLs as arguments.
指引如何为 puerts 项目编写 Unity 和 Unreal 的 changelog
Official skill for the XcodeBuildMCP CLI. Use when doing iOS/macOS/watchOS/tvOS/visionOS work (build, test, run, debug, log, UI automation).
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission. Codex designs ablations from a reviewer's perspective, CC reviews feasibility and implements.
Unity CLI Tool. Execute compile, asset search, gameobject manipulation, transform operations, component inspection, scene/prefab management, screenshot capture, and GIF recording. Supports multi-command execution and runtime extension.
Search, read, and query research papers via the `alpha` CLI (alphaXiv-backed). Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.
Given a GitHub issue number, plan and implement the work described in the issue. Operates iteratively - creates an implementation plan, responds to feedback, and only builds when the 'state:agent-ready' label is applied. Includes tests, documentation updates, and PR creation. Trigger keywords - build from issue, implement issue, work on issue, build issue, start issue.
飞书多维表格（Bitable）的创建、查询、编辑和管理工具。包含 27 种字段类型支持、高级筛选、批量操作和视图管理。 **当以下情况时使用此 Skill**： (1) 需要创建或管理飞书多维表格 App (2) 需要在多维表格中新增、查询、修改、删除记录（行数据） (3) 需要管理字段（列）、视图、数据表 (4) 用户提到"多维表格"、"bitable"、"数据表"、"记录"、"字段" (5) 需要批量导入数据或批量更新多维表格 --- # Feishu Bitable (多维表格) SKILL ## 🚨 执行前必读 - ✅ **创建数据表**：支持两种模式 — ① 明确需求时，在 `create` 时通过 `table.fields` 一次性定义字段（减少 API 调用）；② 探索式场景时，使用默认表 + 逐步修改字段（更稳定，易调整） - ⚠️ **默认表的空行坑**：`app.create` 自带的默认表中会有空记录（空行）！插入数据前建议先调用 `feishu_bitable_app_table_record.list` + `batch_delete` 删除空行，避免数据污染 - ✅ **写记录前**：先调用 `feishu_bitable_app_table_field.list` 获取字段 type/ui_type - ✅ **人员字段**：默认 open_id（ou_...），值必须是 `[{id:"ou_xxx"}]`（数组对象） - ✅ **日期字段**：毫秒时间戳（例如 `1674206443000`），不是秒 - ✅ **单选字段**：字符串（例如 `"选项1"`），不是数组 - ✅ **多选字段**：字符串数组（例如 `["选项1", "选项2"]`） - ✅ **附件字段**：必须先上传到当前多维表格，使用返回的 file_token - ✅ **批量上限**：单次 ≤ 500 条，超过需分批（批量操作是原子性的） - ✅ **并发限制**：同一数据表不支持并发写，需串行调用 + 延迟 0.5-1 秒 --- ## 📋 快速索引：意图 → 工具 → 必填参数 | 用户意图 | 工具 | action | 必填参数 | 常用可选 | |---------|------|--------|---------|---------| | 查表有哪些字段 | feishu_bitable_app_table_field | list | app_token, table_id | - | | 查记录 | feishu_bitable_app_table_record | list | app_token, table_id | filter, sort, field_names | | 新增一行 | feishu_bitable_app_table_record | create | app_token, table_id, fields | - | | 批量导入 | feishu_bitable_app_table_record | batch_create | app_token, table_id, records (≤500) | - | | 更新一行 | feishu_bitable_app_table_record | update | app_token, table_id, record_id, fields | - | | 批量更新 | feishu_bitable_app_table_record | batch_update | app_token, table_id, records (≤500) | - | | 创建多维表格 | feishu_bitable_app | create | name | folder_token | | 创建数据表 | feishu_bitable_app_table | create | app_token, name | fields | | 创建字段 | feishu_bitable_app_table_field | create | app_token, table_id, field_name, type | property | | 创建视图 | feishu_bitable_app_table_view | create | app_token, table_id, view_name, view_type | - | --- ## 🎯 核心约束（Schema 未透露的知识） ### 📚 详细参考文档 **当遇到字段配置、记录值格式问题或需要完整示例时，查阅以下文档**： - **[字段 Property 配置详解](referenc
Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
You MUST use this for gathering contexts before any work. This is a Knowledge management for AI agents. Use `brv` to store and retrieve project patterns, decisions, and architectural rules in .brv/context-tree. Uses a configured LLM provider (default: ByteRover, no API key needed) for query and curate operations.
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:
