Creates implementation plans for ALL work scenarios. MANDATORY entry point for the PLAN phase. 8-step workflow: Intent -> Discovery -> Scenario -> Context -> Template -> Approach -> Session -> Approval 2 scenarios: AGENTING (ecosystem work), DOCUMENTATION (context creation & refinement)
Provides local documentation for the Claude Agent SDK (formerly Claude Code SDK).
Canonical reconciliation runsheet for AUD artefacts. Create or update the audit, disposition every finding, reconcile specs/contracts, and hand off to closure only when audit state supports it.
First-time ttal setup. Installs ttal, then runs ttal onboard for daemon, hooks, and config. Run this after cloning a ttal workspace template.
分析备忘录（Analytical Memo）生成工具。研究者在编码过程中或编码后，直接 说出脑子里的想法——一个编码、一段资料、一个困惑、一个"这里有什么"的感觉—— skill 自动生成结构化的分析备忘录并保存为 Markdown 文件到本地。 适用于主题分析（TA）、扎根理论（GT）及一切质性研究方法。 与 memo-coach 的区别：analytic-memo 由 AI 代写分析内容； memo-coach 由研究者自己写，AI 只负责追问（专用于程序化扎根理论）。 当用户提到"写备忘录""记录分析思路""写 memo""分析笔记""帮我记下这个想法" "这个编码有点意思""这里好像有什么""这个值得记录" "这个受访者说的很奇怪"，或在编码/主题分析过程中表达任何需要捕捉的分析直觉时触发。 --- # 分析备忘录（Analytical Memo） 分析备忘录是质性研究中捕捉分析动能的核心工具。Charmaz（2014）将备忘录定义为 研究者与数据之间持续进行的智识对话，而非填写分类表格的形式操作。 此 skill 的设计原则：**研究者只管说出想法，工具负责追问和结构化**。 ## 启动：获取必要信息 触发后，只需收集两项信息（其余由 skill 自动判断）： 1. **触发内容**：用户输入的编码片段、类属名称、原始资料段落、初步想法或困惑 （直接使用用户的原始表述，不要要求用户重新整理或分类） 2. **保存路径**（可选）：若未提供，默认保存到 `~/Documents/research-memos/` 若用户在之前对话中已提供研究背景（研究主题、研究问题），直接沿用，不重复询问。 --- ## 内部识别逻辑（对用户不可见） 根据用户输入，自动判断分析方向，**不向用户暴露这个判断过程**： **→ 概念深化**（输入是单个编码或类属，附带描述或疑问） 追问：这个概念的核心含义和边界是什么？在哪些条件下更显著或消退？ 与已有理论概念有何联系或张力？它暗示了什么理论主张？ **→ 关系假设**（输入涉及两个或以上概念，且包含关系词：关系、影响、导致、联系、之间） 追问：这个关系的性质是什么（因果、条件、并行、对立）？ 数据中有哪些直接证据？在什么情境下成立或不成立（边界条件）？ **→ 负面案例**（输入包含反差信号：但是、例外、不符合、反而、奇怪、矛盾、和别人不一样） 追问：这是真正的反例，还是揭示了边界条件？ 是否需要修订现有类属或理论假设？修订方向是什么？ **→ 反身性**（输入包含研究者自我指涉：我觉得、我担心、我是否、我的立场、我注意到自己） 追问：研究者的哪种预设或情绪可能影响了这段分析？ 这个反思对理论抽样或研究设计有什么启示？ **→ 综合展开**（输入混合多种信号，或信号不明确） 先用一句话锚定这段想法的核心，再沿最主要的分析方向展开。 --- ## 发展阶段判断（参考 Birks, Chapman & Francis, 2008） 根据用户描述的研究进展，在文件 frontmatter 中自动标注阶段： - `preliminary`：研究者处于开放编码早期，想法贴近数据、印象式 - `interim`：开始跨类属思考，建立概念间联系 - `advanced`：涉及核心类属、理论命题或整体理论框架 判断依据： - "刚开始编码"/"第一份访谈" → preliminary - 提到多个类属的关系/"开始看到模式" → interim - 提到核心类属/"理论框架"/"饱和" → advanced - 无法判断 → 留空，不强行填写 --- ## 备忘录生成 按以下结构生成分析内容（对话中展示，同时写入文件）： ### 文件 frontmatter ```yaml ---
Assesses whether an existing Python, bash, or hybrid pipeline is a good fit for Seamless (content-addressed caching, reproducible execution, local-to-cluster scaling). Triggers when wrapping scripts or functions without rewriting them, avoiding recomputation, comparing workflow frameworks (vs Snakemake, Nextflow, CWL, Airflow, Prefect), migrating a pipeline, or setting up remote/HPC execution. Covers direct/delayed decorators, seamless-run CLI, nesting, module inclusion, scratch/witness patterns, deep checksums, and execution backends (local, jobserver, daskserver). Provides safe guidance on remote execution and determinism — avoids naive "copy code to server" suggestions.
Run comprehensive pre-press preflight checks on Adobe Illustrator documents using illustrator-mcp tools. Detects print-critical issues (RGB in CMYK, broken links, low-res images, white overprint, text not outlined), text consistency problems (dummy text, notation variations), and PDF/X compliance. Use when user asks to check a document before printing, submission, or handoff — or mentions "preflight", "pre-press check", "print check", "submission check".
Use when user wants to develop on the SecondMe platform (second.me, develop.second.me). Triggers: building SecondMe third-party apps (第三方应用/外部应用), SecondMe OAuth login integration (Client ID/Secret, token exchange), MCP integration for SecondMe, Agent Memory API, Act stream API, app scaffolding, review submission, or hackathon/黑客松 projects targeting SecondMe. Covers the full developer lifecycle from app creation and credentials to release. NOT for casual SecondMe usage like browsing profiles, adding friends, or social features — only for building and integrating with SecondMe as a developer platform.
NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
Use when you need to create an academic Beamer presentation with original theme and multi-agent review.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Manage code snippets in ByteStash snippet storage service. This skill should be used when the user asks to "save a snippet", "search snippets", "find code", "share snippet", "organize snippets", "list my snippets", "create snippet", "delete snippet", or mentions ByteStash, code storage, snippet management, or code archival.
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:
