Agents
1,401 skills.
Browse
dmux-workflowsaffaan-mMulti-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.dynamic-workflow-modeaffaan-mDesign task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses. Use when building a task-local harness, adding eval gates, or extracting a reusable skill from ad-hoc work.ecc-guideaffaan-mECC の現在のエージェント、スキル、コマンド、フック、ルール、インストールプロファイル、およびプロジェクトオンボーディングをガイドしています。ライブリポジトリサーフェスを読んでから回答するようユーザーをガイドします。ecc-tools-cost-auditaffaan-mECC ツール、エージェント、スキル、および実装のコスト監査を実施します。プロンプト入力トークンを分析して、計算効率を定量化します。enterprise-agent-opsaffaan-mオブザーバビリティ、セキュリティ境界、およびライフサイクル管理を備えた長寿命エージェントワークロードを運用します。eval-harnessaffaan-mFormal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles. Use when a Claude Code workflow needs a formal eval before it is trusted or changed.healthcare-cdss-patternsaffaan-m臨床意思決定支援システム（CDSS）パターン、医学的推論、およびエビデンスベースの実装。healthcare-eval-harnessaffaan-mヘルスケアAIモデル評価ハーネス、臨床メトリクス、およびレギュレーション遵守の検証。hookify-rulesaffaan-m自動フック実装、イベントドリブン実行、およびルール駆動ワークフロー。iterative-retrievalaffaan-mサブエージェントのコンテキスト問題を解決するために、コンテキスト取得を段階的に洗練するパターンknowledge-opsaffaan-m複数のストレージレイヤー（ローカルファイル、MCP メモリ、ベクターストア、Git リポジトリ）にわたるナレッジベースの管理、取り込み、同期、検索。ユーザーが知識システム全体で保存・整理・同期・重複排除・検索を行いたい場合に使用します。loop-design-checkaffaan-mDesign a goal-oriented agent loop or review one for failure modes: spinning, Goodhart-gaming the verifier, or running a wrong answer to completion. Covers machine-decidable goals, loop types, plan/build/judge skeletons, and runaway prevention; mechanism wiring lives in autonomous-loops. Use when designing, writing, or checking an agent loop. 中文触发：写 loop、设计 loop、做一个 loop、检查 loop 对不对、loop 体检、loop 会不会跑飞、可判定目标、五个崩法、plan build judge。mcp-server-patternsaffaan-mBuild MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API. Use when building or debugging an MCP server — tools, resources, prompts, validation, or transport choice.openclaw-persona-forgeaffaan-m为 OpenClaw AI Agent 锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡， 输出身份定位、灵魂描述(SOUL.md)、角色化底线规则、名字和头像生图提示词。 如当前环境提供已审核的生图 skill，可自动生成统一风格头像图片。 当用户需要创建、设计或定制 OpenClaw 龙虾灵魂时使用。 不适用于：微调已有 SOUL.md、非 OpenClaw 平台的角色设计、纯工具型无性格 Agent。 触发词：龙虾灵魂、虾魂、OpenClaw 灵魂、养虾灵魂、龙虾角色、龙虾定位、 龙虾剧本杀角色、龙虾游戏角色、龙虾 NPC、龙虾性格、龙虾背景故事、 lobster soul、lobster character、抽卡、随机龙虾、龙虾 SOUL、gacha。operator-approval-loopaffaan-mOperator approval contract with internal filing notices for agent-drafted outbound messages, hashed drafts, epoch-keyed decisions, durable delivery claims and receipts, and a pre-draft baseline gate. Use when an agent drafts messages to external counterparties and a human operator must approve, reject, or steer each send before it leaves.orch-add-featureaffaan-mOrchestrate building a brand-new feature end to end — research, plan, TDD implementation, review, and gated commit — by delegating each phase to the matching ECC agent. Use when adding a capability that does not exist yet.orch-build-mvpaffaan-mOrchestrate bootstrapping a working MVP from a design or spec document — ingest the SDD/PRD, plan thin vertical slices, scaffold the first end-to-end slice, then drive a generator-evaluator build loop with review and gated feat commits. Use when a design or spec document must become a running MVP through planned vertical slices.orch-change-featureaffaan-mOrchestrate altering an existing, working feature to new desired behavior — update its tests to the new spec, change the implementation to match, review, and gated commit. Use when behavior is not broken but should be different.orch-fix-defectaffaan-mOrchestrate fixing a bug — reproduce it as a failing regression test, fix to green, review, and gated commit — by delegating each phase to the matching ECC agent. Use when existing behavior is broken or wrong.orch-pipelineaffaan-mShared orchestration engine behind the orch-* skill family — the gated Research-Plan-TDD-Review-Commit pipeline, size classifier, agent and command map, and two human gates (plan approval, commit confirmation) that orch-* operation skills delegate to. Use indirectly via orch-add-feature, orch-fix-defect, orch-change-feature, orch-refine-code, or orch-build-mvp; read directly only when adding an orch operation or tuning shared phases.parallel-execution-optimizeraffaan-m当用户希望通过并行工作、并发 agents、批量工具调用、隔离 worktree 或多条独立验证通道来大幅加速任务、同时不损失正确性时使用。plan-orchestrateaffaan-m日本語翻訳：このファイルは plan-orchestrate 用の日本語翻訳が必要ですprompt-optimizeraffaan-m日本語翻訳：このファイルは prompt-optimizer 用の日本語翻訳が必要ですralphinho-rfc-pipelineaffaan-mRFC駆動の複数エージェントDAG実行パターン、品質ゲート、マージキュー、ワークユニットオーケストレーション。recursive-decision-ledgeraffaan-mRun repeated rollouts ("Prime Gauss" style recursive prompting) while keeping an append-only decision ledger of trials, marks, coherence checks, and promotion gates, so recursive confidence never auto-approves live trading, deploy, or destructive actions. Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.rules-distillaffaan-mスキルをスキャンしてドメイン横断的な原則を抽出し、ルールに蒸留する——既存のルールファイルへの追記、修正、または新規作成santa-methodaffaan-m収束ループを持つマルチエージェント敵対的検証。2つの独立したレビューエージェントが両方合格して初めて出力を出荷できます。skill-complyaffaan-mスキル、ルール、エージェント定義が実際に遵守されているかを可視化する——3種類のプロンプト厳格度レベルのシナリオを自動生成し、エージェントを実行し、動作シーケンスを分類し、完全なツール呼び出しタイムラインの遵守率をレポートするstrategic-compactaffaan-mSuggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction. Use when a session is approaching a context limit and a task phase is a natural place to compact.team-agent-orchestrationaffaan-mRun team-based orchestration for agent squads: work items with owners and scope, agent Kanban state, branch isolation, control pane visibility, and merge gates. Use when coordinating multiple agents in parallel across branches or worktrees — multi-agent fan-out, agent Kanban, squad coordination, or merging agent output into one product.team-builderaffaan-m並列チームを構成して派遣するためのインタラクティブなエージェント選択ツールunified-memoryaffaan-mShare durable, inspectable context and handoffs between Claude, Codex, Hermes, Cursor, OpenCode, and other agents through the local ECC Memory Vault. Use when an agent must save work state, transfer context, resume another agent's task, or search shared project knowledge.workspace-surface-auditaffaan-mアクティブなリポジトリ、MCPサーバー、プラグイン、コネクター、環境サーフェス、ツールのセットアップを監査し、最も価値の高いECCネイティブスキル、フック、エージェント、オペレーターワークフローを推奨する。ユーザーがClaude Codeのセットアップを支援してほしい場合や、環境で実際に何が使えるかを理解したい場合に使用する。best-mindsagentchengfeng模拟器思维：不问"你怎么看"，而是问"世界上谁最懂这个？TA 会怎么说？"。触发词：最强大脑、顶级专家、世界级、best minds、谁最懂这个agent-email-patternsagentmail-toArchitecture patterns for AI agents that communicate over email -- why agents need dedicated inboxes rather than human email accounts, infrastructure/provider tradeoffs, one-inbox-per-agent, two-way conversation loops, human-in-the-loop drafts, WebSocket vs webhook event design, multi-agent topologies, OTP flows, and the threat model (prompt injection, webhook spoofing, credential exposure, data leakage). Use when designing how agents send, receive, and manage email conversations, evaluating wheagentmail-mcpagentmail-toConfigure or troubleshoot the hosted AgentMail MCP server for Codex, Claude Code, Cursor, or another Streamable HTTP MCP client. Use for installation, OAuth, API-key headers, connection failures, or MCP tool discovery. Do not use when the connection already works and the user just wants to send, check, or manage mail — use the sibling action skills for that.agentmail-toolkitagentmail-toAdd AgentMail tools to agent frameworks with the TypeScript or Python AgentMail Toolkit. Use for Vercel AI SDK, LangChain, OpenAI Agents SDK, LiveKit Agents, or MCP adapters; do not use for direct mailbox operations, raw SDK implementation, CLI usage, or MCP client setup.email-for-ai-agentsagentmail-toDeprecated alias of the agent-email-patterns skill, kept so existing installs and pinned URLs keep resolving. Prefer installing agent-email-patterns; this is an identical generated copy covering agent email architecture, security, and provider tradeoffs.build-review-interfaceai-evals-courseBuild a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.error-discoveryai-evals-courseRun error analysis on a dataset. Build a review UI, select diverse samples, monitor annotations, and organize failure modes.eval-auditai-evals-courseAudit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-discovery, write-judge-prompt, or validate-evaluator instead).evals-startai-evals-courseEntry point for evals. Use when the user asks for help with evals, does not know where to begin, or asks for something no other skill in this plugin matches. Do NOT use when a more specific skill in this plugin already matches; load that skill directly.evaluate-ragai-evals-courseGuides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.generate-synthetic-dataai-evals-courseCreate diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.validate-evaluatorai-evals-courseCalibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per `write-code-eval`).write-judge-promptai-evals-courseDesign LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.skill-creatoraiskillstoreGuide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.llm-councilaiwithremyRun any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'valiskill-finderaktsmmSearch, install, and manage Agent Skills locally and from GitHub, then help decide whether the task really needs a skill or another customization primitive. Use when looking for skills, installing skills, managing a skill collection, or choosing between a skill, prompt, instruction, or agent.huashu-agent-swarmalchaincyf多Agent蜂群并行协作，纯git自组织，适合大型项目开发。当用户提到"蜂群模式"、"多agent"、"并行开发"、"agent swarm"时使用。