Claude Code Skill 目錄

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

Create, set up, or update the personal context portfolio: structured markdown files describing who you are, how you work, your teams, and your tool/ADO configuration. Runs the interview workflow for first-time setup and targeted edits for updates. Trigger this skill when the user asks to: set up their context, create or update their context portfolio, "create my IQ", "set up my IQ", edit their profile, add/remove a stakeholder, update ADO config, change team info, update pillars, or set up any plugin configuration. Trigger when another skill fails to find context (missing files or TODO markers) and needs context populated. Also trigger when the user mentions a context change in passing (e.g., "my manager changed", "we added someone to the team") to offer a context file update. Do NOT trigger for read-only questions like "who's on my team?" or "what's my ADO config?". Those are answered directly from the context files referenced in the loaded custom instructions; no skill is needed.

Foundation skill for Power Automate via FlowStudio MCP — auth setup, the reusable MCP helper (Python + Node.js), tool discovery via `list_skills` / `tool_search`, and oversized-response handling. Load this skill first when connecting an agent to Power Automate. For specialized workflows, load `flowstudio-power-automate-build`, `flowstudio-power-automate-debug`, `flowstudio-power-automate-monitoring` (Pro+), or `flowstudio-power-automate-governance` (Pro+) — each contains the workflow narrative, this skill provides the plumbing they all rely on. Requires a FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app

Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

Converts draft customer-story Markdown into Langfuse website MDX (Fumadocs), collects missing metadata and assets, wires meta.json and authors. Use when adding or converting a customer case study, user story, or /users page, or when the user mentions customer story setup, cresta/canva-style posts, or CustomerStoryCTA/BlogHeader for customers.

Use when setting up, auditing, or slimming project agent instructions; integrating Teamwork artifacts, MCP/CodeGraph policy, AGENTS/CODEX/CURSOR/CLAUDE rules, install readiness, or reusable workflow-rule migration.

Use when updating Teamwork package version, refreshing installed skills/agents/global policy, checking install freshness, or release metadata.

Use when Teamwork is explicitly requested, work must cross Teamwork stages, or the correct next stage is unclear; route to native/research/debug/plan/execute/review/goal/init/update.

Create a new Langfuse integration page in the langfuse-docs repo. Use this skill whenever the user wants to add, create, draft, or scaffold an integration page, cookbook, or docs page for a new tool/framework/model-provider/gateway in Langfuse — triggers include "new integration", "integration page", "docs page for <X>", "cookbook for <X>", "add <X> to langfuse docs", or any request that results in a new `cookbook/integration_*.ipynb`. Also use when the user pastes working integration code, a link to a partner's docs, or rough notes and wants them turned into the standard Langfuse integration notebook. The skill produces a correctly formatted Jupyter notebook, updates `cookbook/_routes.json`, and tries to fetch the partner logo into `public/images/integrations/`.

Resume session from CONTEXT-llm.md. Use when resuming work, loading saved context, continuing a previous session. Triggers include "load context", "resume session", "continue where I left off".

Recommend optimal Claude model (haiku/sonnet/opus) for a task. Use when user asks "which model", "pick model", "model for", or before starting costly/complex tasks. Covers tech and non-tech tasks.

Save session to CONTEXT-llm.md with conversation summary. Use when saving work, checkpointing progress, preserving session state. Triggers include "save context", "save session", "checkpoint", "save my progress".

Comprehensive toolkit for validating, optimizing, and understanding Prometheus Query Language (PromQL) queries. Use this skill when working with PromQL queries, prometheus queries, metrics queries, alerting rules, recording rules, or grafana dashboards to check syntax, detect anti-patterns, identify optimization opportunities, and interactively plan queries with users.

Challenge, push back, play devil's advocate on AI output. Use when: challenge this, are you sure, push back, prove it, what if you're wrong, devil's advocate, stress test, poke holes, second opinion, sanity check, too confident, really?, question this decision. Subcommands: anchor (committed too fast), verify (facts wrong?), framing (wrong problem?), deep (full devil's advocate in separate context).

Sense-making before action. Classify problem using Cynefin triangulation (3 tests + decomposition) to route to the right skill chain. Use when: frame, what approach, how should I start, which skill, where to begin, unsure what to do. NOT for known tasks — just do them.

Safe-to-fail experiment for Complex domain problems where cause-effect is only visible in retrospect. Two-phase: foreground qualify → background probe → sense result. Use when: probe, safe-to-fail, test hypothesis, experiment with hypothesis, Complex domain with hypothesis. NOT for brainstorming (use brainstorm) or known cause-effect (use investigate).

Use when user reports an error, bug, or something not working. Search-first troubleshooting with diagnostic phase. Triggers: debug, error, broken, not working, failing, crash, exception.

Query and display available GitHub Copilot AI models with their capabilities, context limits, and features. Use when: "what models are available", "show copilot models", "list github models", "check model capabilities", "switch models". Examples: - user: "What models can I use with GitHub Copilot?" → fetch and display available models - user: "Show me models with vision support" → filter models by capability - user: "Which model has the largest context window?" → compare model specifications - user: "List all GPT-5 models" → filter by model family

Analyze collaboration patterns (HOW) and compute metrics from captured sessions. Use when reviewing collaboration quality, analyzing human-AI interaction, computing session metrics. Triggers include "retrospect collab", "collaboration analysis", "session patterns", "how am I collaborating".

Analyze domain insights (WHAT/WHY learned) from captured sessions. Use when reviewing learnings, extracting patterns, analyzing decisions. Triggers include "retrospect domain", "domain analysis", "what did I learn", "session insights".

Comprehensive toolkit for validating, linting, and testing GitHub Actions workflow files, custom local actions, and public actions. Use this skill when working with GitHub Actions YAML files (.github/workflows/*.yml), validating workflow syntax, testing workflow execution with act, or debugging workflow issues.

Query Google NotebookLM notebooks directly for source-grounded, citation-backed answers from your uploaded documents. Includes browser automation, library management, and persistent authentication.

用一面交易哲學鏡片(預設「存活紀律派」,可換),把你的真實交易復盤成一張卡——一個最大的洞 + 一條下次要守的規矩 + 一句鏡片原則。先用機械算抓出最大的行為漏洞(假分散 / 梭哈 / 攤平 / 賣太早 / 把beta當alpha),再用鏡片的思路問出每筆交易背後的「動機」(焦慮還是判斷、看好還是不想認賠)。用戶說 /fomo-kernel、復盤我的交易、看我的交易紀錄、幫我 review 這份對帳單、trade review 時使用。不用於個股研究、選股建議、大盤預測或財經新聞問答——那些不是復盤,不要觸發。資料全程留在用戶本機,不外傳。

Audit your Claude Code setup against prompt caching best practices. Checks ordering, tool stability, dynamic content handling, and hook injection patterns. Returns a scored report with specific fixes.