Claude Code Skills · page 128

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

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Expert guidance for Apache Arrow, the cross-language columnar memory format for analytics workloads. Helps developers use Arrow for high-performance data interchange between systems, zero-copy reads, and efficient columnar processing in Python (PyArrow) and JavaScript (Arrow JS).

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When the user wants to automate mobile app testing on iOS and Android using Appium. Also use when the user mentions "appium," "mobile automation," "iOS testing," "Android testing," "mobile WebDriver," "XCUITest," or "UiAutomator." For React Native-specific testing, see detox.

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Expert guidance for Arcjet, the developer-first security platform that provides rate limiting, bot protection, email validation, and attack detection as a code-first SDK. Helps developers add security layers to Next.js, Node.js, and other JavaScript/TypeScript applications without managing infrastructure.

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When the user wants to perform load testing or performance testing using Artillery with YAML-based scenario definitions. Also use when the user mentions "artillery," "YAML load test," "WebSocket testing," "Socket.io load test," "scenario-based load testing," or "artillery run." For JavaScript-based load testing, see k6.

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Build, modify, debug, and deploy agents with Agentforce Agent Script. TRIGGER when: user creates, modifies, or asks about .agent files or aiAuthoringBundle metadata; changes agent behavior, responses, or conversation logic; designs agent actions, tools, subagents, or flow control; writes or reviews an Agent Spec; previews, debugs, deploys, publishes, or tests agents; uses Agent Script CLI commands (sf agent generate/preview/publish/test). DO NOT TRIGGER when: Apex development, Flow building, Prompt Template authoring, Experience Cloud configuration, or general Salesforce CLI tasks unrelated to Agent Script.

Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use developing-agentforce); writes or runs test specs (use testing-agentforce); uses sf agent preview for local development iteration; deploys or publishes agents.

Run OWASP LLM Top 10 security assessments against live Agentforce agents. TRIGGER when: user asks for security testing, OWASP scan, red-teaming, penetration testing, security grade, vulnerability assessment, prompt injection test, data leakage test, excessive agency test, security posture check, or hardening recommendations. DO NOT TRIGGER when: user runs functional smoke tests or batch tests (use testing-agentforce); performs static safety review of .agent file content (use developing-agentforce Section 15); analyzes production session traces (use observing-agentforce); writes or modifies .agent files.

Write, run, and analyze structured test suites for Agentforce agents. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use developing-agentforce); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use observing-agentforce); requests OWASP, security, or red-team testing (use securing-agentforce).

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Fetches live AI crypto trading signals with entry price, stop-loss, take-profit, leverage, confidence scores, and automated verification. Covers 50+ coins including BTC, ETH, SOL. Use when the user asks for crypto signals, trade ideas, market direction, portfolio analysis, or wants to build a trading bot.

v2.1.4 · Dungeon Master assistant for running persistent D&D 5e campaigns. Handles campaign creation/loading, character management, combat tracking, NPC generation, dice rolling, and session state — all persisted across sessions. Invoke with /dm:dnd followed by a subcommand, or just speak naturally once a campaign is loaded.

Track Serenity / @aleabitoreddit on X and turn the feed into (1) a beginner-friendly Chinese iMessage digest with first-principles + Buffett-style judgement, (2) cumulative 60-day ticker mention analytics CSVs for a website, (3) a Xiaohongshu writing brief, and (4) a durable private research map. Trigger on requests like "follow aleabitoreddit / AleaBito / Serenity", "daily Chinese updates from that X account", "60-day mention analytics", "ticker mention count", "写小红书 aleabito", "aleabito 研究地图 / research map", or any request for Chinese commentary derived from @aleabitoreddit posts.

Apply @aleabitoreddit ("Serenity")'s distilled stock-analysis method to ANY ticker, sector, or situation — critical-chokepoint / supply-chain-OSINT idea discovery, first-principles value-chain decomposition, a Buffett-style quality gate (moat / profitability / customer-replacement risk, all default unverified), and narrative-vs-fundamentals hygiene. Produces a beginner-friendly Chinese analysis (她的观点 / 小白解释 / 第一性原理 / Buffett 直接判断 / 当前结论) that classifies an idea as 研究地图 vs 可投资结论. Trigger on "analyze like Serenity / 用 aleabito 的方法分析 / 用 Serenity 框架 / critical chokepoint 分析 / 第一性原理 + Buffett 判断这只股 / supply-chain bottleneck thesis". Never emits buy/sell calls.

Use @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

Claim the AI bounty on tDVV by delegating to the canonical repo SKILL and its branch docs; this public skill is AA/CA only and requires explicit write confirmation.

Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

Automate self-hosting of open-source apps on cloud infrastructure the user owns. Use when the user asks to "self-host", "deploy to my own cloud", "install X on AWS / Lightsail / EC2 / Azure / Hetzner / DigitalOcean / GCP / Oracle Cloud / Hostinger / Raspberry Pi / Kubernetes / Fly.io / Render / Railway / Northflank / exe.dev", "set up my own Ghost blog / Mastodon / WordPress / Nextcloud", wants to deploy the self-hosted personal AI agent **OpenClaw** (openclaw.ai — NOT the Captain Claw platformer game) or **Hermes-Agent** (Nous Research's self-improving AI agent at github.com/NousResearch/hermes-agent), wants to run **Ollama** (local-LLM inference server at ollama.com — pairs with every AI agent / chat UI as an OpenAI-compatible provider), wants to run **Open WebUI** (feature-rich self-hosted ChatGPT-like UI at github.com/open-webui/open-webui — pairs natively with Ollama and any OpenAI-compatible backend; adds RAG, web search, image gen, voice, multi-user), wants to run **Stable Diffusion WebUI** / **Automatic1111** / **A1111** (the most-popular open-source AI image generator at github.com/AUTOMATIC1111/stable-diffusion-webui — text-to-image, img2img, inpainting, ControlNet, LoRA; pairs with Open WebUI as an image-gen backend), wants to run **ComfyUI** (node-based AI image / video generation at github.com/comfyanonymous/ComfyUI — power-user alternative to A1111 with workflow graphs; same models, different UX; pairs with Open WebUI as image-gen backend), wants to deploy **Dify** (open-source LLMOps + AI app builder at github.com/langgenius/dify — visual workflow builder, RAG, multi-tenant; the "build a SaaS-grade AI app" platform, different category from chat UIs), wants to deploy **LibreChat** (multi-provider chat UI with deep enterprise plumbing at github.com/danny-avila/LibreChat — alternative to Open WebUI for teams; multi-user with social logins, per-user balance + transactions, agents + MCP, dedicated rag_api), wants to deploy **AnythingLLM** (RAG-focused workspace + agent platform at github.com/Mintplex-Labs/anything-llm — drop-in PDFs + URLs + GitHub repos, ask questions over them; built-in LanceDB; Desktop App + Docker + 8 cloud one-clicks), wants to install **Aider** (AI pair-programming CLI at github.com/Aider-AI/aider — runs in the terminal next to a git repo, edits files via diffs, auto-commits; pairs with any LLM provider including Ollama for local), wants to deploy **vLLM** (production-grade LLM inference server at github.com/vllm-project/vllm — high-throughput multi-tenant serving with PagedAttention + tensor parallelism + prefix caching; NVIDIA / AMD / Intel / CPU; Docker / Kubernetes / Helm / PaaS), wants to deploy **Langfuse** (open-source LLM engineering platform at github.com/langfuse/langfuse — observability, evals, prompt management, datasets, scoring; v3 six-service architecture with Postgres + ClickHouse + Redis + S3; Docker Compose, Kubernetes Helm chart, first-party Terraform modules for AWS / GCP / Azure, Railway one-click), or names any combination of an open-source app and a cloud provider. Walks the user through provisioning, DNS, TLS, outbound email (SMTP), and inbound email, in phases that are resumable across sessions via a state file at `~/.open-forge/deployments/<name>.yaml`. Supported today: Ghost on AWS Lightsail (Bitnami blueprint); OpenClaw via every upstream-blessed path documented at docs.openclaw.ai/install/* — AWS Lightsail blueprint, Docker Compose, Podman, Kubernetes (Kustomize), native installers (install.sh / install-cli.sh / install.ps1), ClawDock, Ansible, Nix, Bun, plus per-host adapters for AWS EC2 / Azure / Hetzner / DigitalOcean / GCP / Oracle Cloud / Hostinger / Raspberry Pi / macOS-VM (Lume) / BYO Linux server / localhost / Fly.io / Render / Railway / Northflank / exe.dev. More projects and infras added under `references/projects/` and `references/infra/`.

Use when executing implementation plans with independent tasks in the current session

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.

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Use when you have a spec or requirements for a multi-step task, before touching code

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Use when implementing any feature or bugfix, before writing implementation code

Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

Track development session events in a daily markdown changelog, including file changes, test results, and key decisions.

Code review specialist for quality, security, and best practices

Debugging specialist for root cause analysis and problem resolution

Documentation specialist for README, API docs, and code comments

Validate custom agent file format and structure

Generate custom agent from template

List all available agents (core + expert)

TDD-based autonomous development loop with checkpoint recovery and observability changelog

Verify development environment is ready

Validate CLAUDE.md structure and completeness

Generate CLAUDE.md template for current project

Self-Evolving Development Loop - Dynamic skill generation with learning and evolution

View Self-Evolving Loop session status, history, and memory metrics