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Found 6,698 Skills
Plan a huge chunk of work — more than one agent session can hold — as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.
interview the user to design an agentic control loop (sensor, controller, actuator under disturbances) tailored to their codebase, then build it as locally-runnable components plus a scheduled coding-agent workflow
Analyzes observability signals from customer GenAI applications with DQL. Reads OpenTelemetry GenAI spans and LLM evaluation bizevents. Use for: golden signals (traffic, errors, latency, saturation); LLM signals (model, provider, tokens); cost/token analytics, usage attribution, and prompt caching; agent signals (tool calls, steps, failures, loop detection, Smartscape topology); conversation/session analytics; guardrails (blocked/truncated responses); and evaluation signals (quality, pass/fail). Trigger: "LLM latency", "token usage by model", "cost by model and provider", "cost per conversation", "who is driving token spend", "do I have prompt caching", "failing agent tool calls", "find runaway agents", "responses truncated or blocked", "failed evaluations", "am I hitting rate limits", "token throughput / TPM", "provider throttling or 429s". Do NOT use for: Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI tracing (dt-obs-tracing).
在新机器或新项目上落地「高智商领导 + 便宜执行」分层子代理：Codex Sol 领导 + Luna 工人， 可选 Claude Code 项目级 agents 与 Pi/pi-flow 跨工具编排。用于： (1) 从零安装并配置 Codex / Claude Code / Pi (2) 写入项目级 .codex/agents、AGENTS.md、.claude/agents (3) 修复 Sol 无法 spawn Luna 的 multi-agent catalog 问题 (4) 跑 Sol/Luna/多代理冒烟验证 触发：新机器设置、Sol-Luna、分层子代理、multi-agent 配置、codex agents 初始化
Use when building an internal admin-facing AI agent in a Medusa project. These agents are operated by merchants and store operators — not customers. Covers data models, module service, agent runtime (tools, system prompt, streamText), streaming API routes (NDJSON), and admin UI chat extensions. Load for any internal agent type: store operations assistant, product audit, cohort analysis, customer service tooling for support staff, etc. Do NOT use for customer-facing agents (storefront chatbots, buyer-side assistants).
AE/TE/ThinkingEngine/ThinkingAI ae-cli manual for AI Agent Team tasks: managing teams (list, create, update, delete, AI-generate, templates) and executing TeamRuns (start, chat, cancel, reply, result, artifacts). Use when the user asks to find a team, run a team task, check run status, retrieve results or artifacts, or set up multi-agent workflows. Must use ae-cli, read the matching references/<command>.md before composing commands, and never guess team IDs, run IDs, config structures, or parameter formats.
Delegate a coding task to the Warp Agent CLI (`oz`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Warp - phrasings like "have Warp implement X", "delegate this to the Warp CLI", "run it through Warp", "use oz to implement/fix/refactor" - or wants to run a queue of coding tasks through Warp while staying the reviewer. DO NOT USE for tasks small enough to do inline, when the user wants the code written directly without delegating, or for the interactive `warp` TUI (this skill drives the headless `oz agent run`, not the terminal app).
Create or update a reusable profile of a summer camp (name, type, location and jurisdiction, programs, ages, voice, accreditation, and positioning) saved to .agents/camp-profile.md so every other camp skill can use it instead of re-asking. Use when setting up the camp skills for the first time, when another skill needs camp context and no profile exists, or when the camp's details change.
Use this skill to design an OpenAPI spec from scratch, assess an existing spec for AI agent readiness, security, or design quality, or fix issues found in a spec. Trigger when the user describes an API they want to build, asks to "design", "create", "draft", or "scaffold" an OpenAPI spec, or mentions building a REST API for a service or domain. Trigger when the user says things like "I want to expose endpoints for X", "help me design an API for Y", or "I need an OpenAPI spec for Z" — even without saying "OpenAPI" explicitly. Trigger when the user asks to evaluate, review, check, or assess an OpenAPI spec for agent compatibility, API quality, security, OWASP compliance, WSO2 guidelines, or REST best practices — or when they share a .yaml/.json OpenAPI file and ask how good it is. Trigger when the user asks to fix, correct, remediate, or apply fixes to issues in an OpenAPI spec — including "fix issue spec-001", "fix all HIGH severity issues", "apply autoFixable fixes", or "fix the spec issues from this report".
You MUST use this when auditing, improving, restructuring, or maintaining agent instruction files - AGENTS.md, CLAUDE.md and its variants, .claude/rules/, SKILL.md files, or docs linked from them - including reducing always-loaded context cost, finding stale, duplicated, or conflicting instructions, and keeping Claude Code or Codex project memory aligned with the codebase. Not for documentation written for human readers.
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.