Agents
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executing-plansjnmetacode当你要在当前会话里亲自担任实现者执行一份实现计划时使用——你的人类伙伴选择了内联执行，或者没有可用的子智能体工具mcp-builderjnmetacode在构建 MCP 服务器或 MCP 工具时使用 —— 系统化的生产级 MCP 构建方法论，覆盖工具设计、错误处理、传输层选择与测试，让 AI 助手连接外部能力subagent-driven-developmentjnmetacode当在当前会话中执行包含独立任务的实现计划时使用using-superpowersjnmetacode在开始任何对话时使用——确立如何查找和使用技能，要求在任何响应（包括澄清性问题）之前调用 Skill 工具workflow-runnerjnmetacode在 Claude Code / OpenClaw / Cursor 中直接运行 agency-orchestrator YAML 工作流——无需 API key，使用当前会话的 LLM 作为执行引擎。当用户提供 .yaml 工作流文件或要求多角色协作完成任务时触发。writing-skillsjnmetacode当创建新技能、编辑现有技能或在部署前验证技能是否有效时使用qiaomu-goal-meta-skilljoeseesunTurn vague or complex Codex tasks into strong `/goal` commands with outcome, verification, constraints, boundaries, iteration policy, completion evidence, and pause/block conditions. Use when the user asks for Codex goal instructions, Goal 指令, 目标指令, `/goal` prompts, 中文 Goal 模板, plan-to-goal interviews, success criteria, verification commands, or bounded agent work definitions.recoverjsmastery-proWhen something goes wrong during a build, diagnose what type of failure it is before deciding how to respond. Targeted fix, hard reset, or full rethink — the right response depends on the right diagnosis.rememberjsmastery-proSave what matters at the end of a session so the next session picks up exactly where you left off. Or restore context at the start of a new session so nothing is lost between them.context-retrospectivejwyniaAnalyze agent-user interaction transcripts to identify context network maintenance needs and guidance improvements. Use after significant agent interactions or to improve context networks.fact-checkjwyniaVerify claims in generated output against sources. Use as a separate pass AFTER content generation to catch hallucinations. Critical constraint - cannot be reliably combined with generation in a single pass.skill-builderjwyniaBuild new agent skills. Use when creating diagnostic frameworks, CLI tools, or data-driven generators that follow the established skill patterns.arbork-dense-aiAutonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bepi-agentk-dense-aiBuild with and use Pi, the minimal terminal coding harness. Use for installing Pi, configuring providers/models/settings/environment variables, creating Pi skills/extensions/packages/themes/prompt templates, embedding Pi through the SDK, integrating over RPC or JSON event streams, parsing sessions, running local models through the llama.cpp router, developing custom Pi providers and TUI components, or using ecosystem packages such as pi-subagents (delegation/orchestration), pi-mcp-adapter (MCP sleaderkkkkhazix把一句话的想法拆成 AI agent 能独立跑完的目标任务书。用户说「帮我给 agent 写个目标」「帮我详细拆一下这个目标」「写个任务书/brief 给 agent」「写个 goal 提示词」「让 agent 自己跑这个项目」「把活分给几个 agent 并行」时使用。先进代码库实测、必要时联网调研，再一次性提问（≤5 个），产出一份 ≤4000 字符、直接粘进 /goal 就能跑的任务书，含实测数字、白名单地界、防作弊验收和断点续跑。执行型与探索型（调研/选型/找方案）自动分流。self-learningkulaxyzCapture a hard-won "golden path" from the current session as a reusable Agent Skill, so future sessions start already knowing it. Use it (1) right after non-trivial debugging, after working out a multi-step operational workflow, or after rediscovering project facts you didn't know up front — e.g. how to reach the dev/prod database, where credentials and env vars live, how to deploy, run migrations, or verify a change live; and (2) whenever the user says "remember this", "save this as a skill", "langsmith-fetchlangchain-aiFetches LangSmith traces for debugging agent behavior. Use when troubleshooting agent issues, reviewing conversation history, or investigating tool calls.workflowleokemp223当需要串联多个 skill 完成编译+烧录+监控或编译+烧录+调试等流水线任务时使用。autobahnlilmgeniusCarve guardrail-adjacent items out of scope with safe alternatives before risk-adjacent work starts, then run the safe remainder at full strength in a fresh subagent that only ever sees the carved prompt, never the risky input. Use when a task includes stealth, scraping, privacy, IP, policy, licensing, security, or other safety-adjacent material that could be silently dropped, over-elaborated, or needlessly diluted. Fires on the impulse, not only the topic: the moment you notice yourself about tmacrothinklilmgeniusUser-invoked read-only pass for checking whether the current direction is tunnel-visioned: strip the session's bait, fan out 2 to 5 same-model fresh reads, and report divergence first without treating convergence as proof.mandelalilmgeniusAudit any eval, metric, experiment, or benchmark for leakage — does external ground-truth enter independently, or are the model, scorer, and designer just confirming a result no outside truth ever produced? Use before trusting any 'how we'll know it worked' — an A/B, a holdout, a score, a validation — and whenever a result feels too clean or self-confirming. Walks an 8-pattern leakage taxonomy and returns only the patterns that fire, each with an independence fix. Read-only.modelchklilmgeniusSize a task's run before spending it: the cheapest sufficient capability tier (fast, standard, frontier) and the reasoning effort within it, on a neutral scale that binds to whatever levels the model exposes. Use when work seems over- or under-powered, costly, ambiguous, or high-risk, or asks which model class and how much thinking is enough.re0-looplilmgeniusRun repeated build -> QA -> re0-memo -> re0-work cycles while preserving learning and letting code die. Use for long agentic projects where progress must be measured by quality-cleared templates, reusable modules, and eliminated anti-patterns rather than hours spent or features accumulated.readchklilmgeniusVerify the model's understanding of a user's instruction before spending non-trivial work. Use when a request is long, bundled, high-stakes, hard to undo, or has ambiguous scope or referents such as this, that, it, the other one, whatever is cleaner, or whichever order makes sense. Restate internally, cross-check against available context, proceed silently when resolved, and surface only a genuine surviving fork.siplilmgeniusAfter you create or change an artifact or skill, taste-test it with our own skills instead of trusting your in-session judgment — recursive self-improvement, made automatic. Use right after writing or editing anything, before calling it done, committing, or handing it off.conversational-flow-managementlouisblytheWhen the user wants to build or improve a sales bot's ability to keep exchanges natural while progressing toward outcomes. Also use when the user mentions "conversation design," "dialog flow," "bot conversations," "natural conversations," or "guided conversations.evaluation-rubricslyndonklDesigns structured scoring tools with explicit criteria, performance scales, and descriptors for consistent, transparent quality assessment. Use when need quality criteria and scoring scales to evaluate work consistently, compare alternatives objectively, set acceptance thresholds, reduce subjective bias, or when user mentions rubric, scoring criteria, quality standards, evaluation framework, inter-rater reliability, or grading/assessing work.langchain-orchestrationmanutejComprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestrationlinear-dev-acceleratormanutejAccelerate software development with Linear project management and MCP server integration. Master issue tracking, project workflows, and development automation for frontend, full-stack, and mobile applications. Includes comprehensive MCP tool usage, workflow patterns, and development best practices.mapbox-location-groundingmapboxCompose Mapbox MCP tools to produce grounded, cited location-aware responses from live data instead of training datamapbox-mcp-runtime-patternsmapboxIntegration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.lossless-clawmartian-engineeringConfigure, diagnose, and use lossless-claw effectively in OpenClaw, with emphasis on key settings, summary health, and recall-tool usage.agent-skills-creatormblodeCreates and improves portable Agent Skills with a validator, routing scenarios, and evidence-based keep, cut, merge, or retire decisions. Use when asked to "write a skill", "update all skills", "audit my SKILL.md", "remove redundant instructions", or fix skill triggering. For AGENTS.md or CLAUDE.md use agents-md.agents-mdmblodeAudits and edits agent instruction files, verifies repository commands, and migrates repositories to AGENTS.md as the single shared source. Use when asked to "improve my AGENTS.md", "migrate CLAUDE.md to AGENTS.md", or make instructions work across agents. For SKILL.md use agent-skills-creator.ax-auditmblodeAudits agentic products for tool parity, authority, approval payloads, recovery, and trust using 27 rules and a ship verdict. Use when asked for an "AX audit", to review an agent approval flow, or whether an agent can operate the product. For human-facing API ergonomics use dx-audit; for ordinary UI use ui-design.initmcollinaCreates, updates, or optimizes an AGENTS.md file for a repository with minimal, high-signal instructions covering non-discoverable coding conventions, tooling quirks, workflow preferences, and project-specific rules that agents cannot infer from reading the codebase. Use when setting up agent instructions or Claude configuration for a new repository, when an existing AGENTS.md is too long, generic, or stale, when agents repeatedly make avoidable mistakes, or when repository workflows have changemcp-buildermcp-useBuild Model Context Protocol (MCP) servers with mcp-use framework. Use when creating MCP servers, defining tools/resources/prompts, working with mcp-use, bootstrapping MCP projects, deploying MCP servers, or when user mentions MCP development, MCP tools, MCP resources, or MCP prompts.creating-agents-in-medusamedusajsUse 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, bmem0-climem0aiMem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0 config, mem0 import). Also triggers when query includes CLI flags like --user-id, --output, --json, --agent, or describes bash/zsh/terminal/shell usage. DO NOT TRIGGER when: user asks abmem0-vercel-ai-sdkmem0aiMem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also triggers for Next.js apps needing memory-augmented AI. DO NOT TRIGGER when: user asks about direct Python/TS SDK calls without Vercel (use mem0 skill), or CLI terminal commands (use mempalace-recallmempalaceRecall protocol for MemPalace — search the palace before answering about past work, people, projects, or prior decisions. Apply when the user asks what was decided, what happened before, who someone is, what was discussed last time, or anything that may already be filed in their memory palace; or when mempalace-recall is invoked. Complements the mempalace setup skill and requires the mempalace-mcp server.mempalace-taskmempalaceCreate, hand off, claim, execute, and close agent tasks through the MemPalace logstream. Use when the user wants to delegate work, prepare a ready-to-paste task for another agent, receive a MemPalace task id, or explicitly launch a supported headless agent in controlled mode.skills-votememtensorFind the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first.article-prompts-to-skillsmengtoConvert an article, tutorial, or prompt pack into focused reusable AgentSkills, one independent capability per skill, with portable instructions, example prompts, working demos, preview screenshots, validation, gallery updates, and a narrow commit. Use when the user asks to turn an article's prompts, tutorial sections, design patterns, interactions, or workflow ideas into complete skills rather than leaving them as prose.iterate-until-verifiedmengtoApply a prompt-agnostic execution and verification loop to any substantial task while preserving the original request. Use when the user asks to fan out work, use subagents or independent reviewers, loop until done, benchmark against references, apply a harsh critic, compare candidates blind, improve an existing prompt with verification, or continue until explicit quality gates pass.poteto-modemichael-denyerpoteto's agent style for concise, detailed responses, deliberate subagents, unslopped prose, simple code, and verified work. Use for poteto, /poteto-mode, or requests to work in this style.microsoft-skill-creatormicrosoftdocsCreate agent skills for Microsoft technologies using official documentation. Use whenever the user wants to build, generate, or scaffold a skill for any Microsoft technology (Azure, .NET, M365, VS Code, Bicep, etc.)—even phrased casually like "make a skill for Cosmos DB." Investigates the topic via official docs, then generates a hybrid skill with essential knowledge stored locally and dynamic lookups for depth.autogen-developmentmindrallyExpert guidance for Microsoft AutoGen multi-agent framework development including agent creation, conversations, tool integration, and orchestration patterns.langchain-developmentmindrallyExpert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.llamaindex-developmentmindrallyExpert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.