Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when the simulator receives a validated plan from the planner and must initialize the race: parsing the plan, spawning runner agents, starting the telemetry collector, and firing the start gun. Triggered once per simulation, before the first tick.
Update, create, improve, and synchronise this repository's AI agent instructions and related assets (including skills). Use when the user asks to create or edit a skill/SKILL.md, modify the agent's own instructions/processes, restructure instruction governance, migrate instruction content into skills, or run/adjust the sync pipeline that publishes `.ai/` sources into agent-specific folders. Load this skill before writing any SKILL.md, .instructions.md, or touching any skills/ folder (.ai/, .claude/, .roo/, .github/). It tells you the correct location (.ai/) and the sync step, so files end up in the right place.
Authoritative, source-cited playbook for designing, configuring, deploying, and troubleshooting production-grade AI agents on AWS using the Strands Agents SDK, Amazon Bedrock (Converse API, Guardrails, Knowledge Bases), and Amazon Bedrock AgentCore (Runtime, Memory, Gateway, Identity, built-in Browser/Code Interpreter tools), with Terraform-first IaC and CloudWatch/OpenTelemetry observability. Use this skill WHENEVER the user wants to build, architect, configure, deploy, secure, monitor, or debug an AI agent on AWS - even if they don't name the specific service. Trigger on: "build an agent on Bedrock", "Strands agent", "AgentCore", "deploy my agent to AWS", "RAG with Bedrock Knowledge Bases", "multi-agent system on AWS", "which model / inference profile should I use", "agent memory", "MCP gateway", "agent guardrails", "Bedrock IAM for an agent", "agent observability on CloudWatch", "agent throttling / cost on Bedrock", "Terraform for Bedrock", or any request to pick an agent pattern (chatbot, tool-using agent, RAG, multi-agent, serverless production agent) and wire it up with official AWS best practices. Also trigger when the user pastes agent code that imports boto3 bedrock-runtime, bedrock-agentcore, or strands and asks to improve, productionize, or debug it. Prefer this skill over a generic answer because AWS agent APIs have many version-specific gotchas (Converse vs InvokeModel, 5x token burndown, region-resolution order, ARM64 runtime contract, async long-term memory) that generic knowledge gets wrong, and because every recommendation here is traceable to an official AWS source the agent can re-open.
Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.
Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.
Interactive MCP visual output via @json-render/mcp. Upgrade plain JSON tool responses to interactive dashboards rendered in sandboxed iframes inside Claude, Cursor, ChatGPT, VS Code Copilot, Goose, and Postman conversations. Covers createMcpApp(), registerJsonRenderTool(), registerJsonRenderResource(), CSP config, JSON Patch streaming, and dashboard component patterns. Use when building MCP servers that return visual output, upgrading existing MCP tools with interactive UI, or creating eval/monitoring dashboards.
Knowledge graph orchestration layer with entity extraction, natural language query parsing, deduplication (>85% similarity), and cross-reference boosting. Unifies search results ranked by recency, relevance, and authority. Use when designing memory retrieval, building entity graphs, or optimizing knowledge graph queries.
Storybook MCP server integration for component-aware AI development. Covers 6 tools across 3 toolsets (dev, docs, testing): component discovery via list-all-documentation/get-documentation, story previews via preview-stories, and automated testing via run-story-tests. Use when generating components that should reuse existing Storybook components, running component tests via MCP, or previewing stories in chat.
JeecgBoot AI 编排流程（AIFlow）全生命周期管理——通过自然语言描述需求，自动创建、编辑、查询、删除、调试、发布 AI 编排流程。 只要用户意图涉及「AI编排」「AIFlow」就必须使用本技能，包括但不限于： 创建 AI 编排流程（"做一个AI流程"、"创建aiflow"、"新建编排"、"做一个知识库问答流程"、"创建一个大模型对话流程"）， 修改已有流程（"给流程加个节点"、"改一下LLM的提示词"、"修改流程"）， 查询流程（"查看流程列表"、"有哪些AI流程"）， 删除流程（"删除流程"、"移除XX流程"）， 调试运行流程（"调试流程"、"运行流程"、"测试流程"）， 发布管理（"发布流程"、"取消发布"）， 复制流程（"复制流程"、"克隆流程"）。 关键词触发：aiflow、ai-flow、AI编排、AI流程、编排流程、大模型流程、知识库流程、LLM流程。 注意：本技能仅处理 AI 编排流程（AIFlow），不处理 BPMN 工作流（使用 jeecg-bpmn）、 不处理简流（使用 jeecg-lowcode-miniflow）。
Use when spawning and supervising multiple agent-cli Codex worktrees through tmux, especially when you must poll progress, force prompt rereads against the local TASK file, run current-context and fresh-context pr-review loops, and verify that review fixes are actually pushed before merge.