Agent Workflow Claude Skills
4 122 agent workflow skills from 665 authors. They configure the agents themselves: memory, prompts, context and other skills. Half of them fit into 1 844 tokens or less — that is what one costs your context window when the agent loads it. 778 ship runnable scripts rather than instructions alone. 5 of them cannot work without an MCP server, most often task. We also found 589 copies of these same skills sitting in other people's repositories — counted once here, not 589 times.
4 122 unique 665 authors 4 updated this month 506 from vendors
Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.
Plan and build MCP servers with agent-friendly tools, schemas, error handling, and evaluation. Use when creating or refactoring MCP integrations.
Create or update agent skills with concise SKILL.md instructions, bundled resources, agent metadata, validation, and packaging. Use when building or refactoring skills.
Operating-instructions dispatcher for the openclaw-coder autonomous-programmer workspace. Routes every user message by surface — thread → working session, channel/DM → channel handling — and carries the global rules. The workspace AGENTS.md loads this skill first on every user message.
Proactively decompose and coordinate substantial development, research, analysis, planning, document, data, and content work with the smallest useful parallel set of custom subagents. Use when independent execution, parallelism, context isolation, or fresh review can improve speed or quality. Keep unresolved decisions and final acceptance in the main thread. Do not use for casual or simple tasks.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
Build an evidence-backed ecc install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ecc should be trimmed to what a project actually needs instead of loading the full bundle.
Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across OpenAI Codex, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.
Build MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.
Build MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
Build an evidence-backed ecc install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ecc should be trimmed to what a project actually needs instead of loading the full bundle.
Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across OpenAI Codex, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.
日本語翻訳：このファイルは mcp-server-patterns 用の日本語翻訳が必要です
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
回答する前に、どれだけの回答深度を消費するかについてユーザーに情報に基づいた選択を提供する。ユーザーが回答の長さ、深さ、またはトークンバジェットを明示的に制御したい場合にこのスキルを使用する。トリガー条件："token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas"、またはユーザーが回答のサイズや深さの制御を明示的に求めるその他の明確なバリエーション。トリガーしない条件：ユーザーが現在のセッションでレベルを指定済み（そのレベルを維持）、リクエストが明らかに一言の回答、または「token」が認証/セッション/支払いトークンを指している。origin: community
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1; route continuous learning, session learning, and pattern extraction requests to continuous-learning-v2.
Pattern for progressively refining context retrieval to solve the subagent context problem
Hook'lar aracılığıyla oturumları gözlemleyen, güven skorlaması ile atomik instinct'ler oluşturan ve bunları skill/command/agent'lara evriltiren instinct tabanlı öğrenme sistemi. v2.1 çapraz proje kontaminasyonunu önlemek için proje kapsamlı instinct'ler ekler.
Head-to-head comparison of coding agents (OpenAI Codex, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
Evidence-first automation inventory and overlap audit workflow for ecc. Use when the user wants to know which jobs, hooks, connectors, MCP servers, or wrappers are live, broken, redundant, or missing before fixing anything.
Transform OpenAI Codex into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging OpenAI Codex's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
>- Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. complex multi-PR task, or describes work that needs multiple sessions. than 3 tool calls, or user says "just do it".
Audits OpenAI Codex context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.
Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.
使用Node/TypeScript SDK构建MCP服务器——工具、资源、提示、Zod验证、stdio与可流式HTTP对比。使用Context7或官方MCP文档获取最新API信息。
>- Analyze raw prompts, identify intent and gaps, match ecc components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.
Use this skill to prevent destructive operations when working on production systems or running agents autonomously.
Scan your OpenAI Codex configuration (.codex/ directory) for security vulnerabilities, misconfigurations, and injection risks using AgentShield. Checks CODEX.md, settings.json, MCP servers, hooks, and agent definitions.
Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines
Use when auditing Codex skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.
>- Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
Audit the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommend the highest-value ecc-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up OpenAI Codex or understanding what capabilities are actually available in their environment.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
Automatically extract reusable patterns from OpenAI Codex sessions and save them as learned skills for future use.
Pattern for progressively refining context retrieval to solve the subagent context problem
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.
Build persistent multi-agent operating systems on OpenAI Codex. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.
Track and report OpenAI Codex token usage, spending, and budgets from a local cost-tracking database. Use when the user asks about costs, spending, usage, tokens, budgets, or cost breakdowns by project, tool, session, or date.
Guide users through ecc's current agents, skills, commands, hooks, rules, install profiles, and project onboarding by reading the live repository surface before answering.
Read a plan document, decompose it into steps, design a per-step agent chain from the ecc catalogue, and emit ready-to-paste /orchestrate custom prompts. Generative only — never invokes /orchestrate itself. Use when the user has a multi-step plan and wants to drive it through orchestrate without composing chains by hand.
Search existing local, marketplace, GitHub, and web skill sources before creating a new skill. Use when the user wants to create, build, fork, or find a skill for a workflow.
CLI-first serial port debugging and macro automation with serial-mcp-server for Codex and Claude Code. Use when working with UART or USB-serial devices, listing serial ports, probing STM32/Arduino/ESP32 boards, writing or reading serial data, running JSON serial macros, controlling RTS/DTR, or using serial-mcp-server through MCP tools.
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
Coordinate small teams of specialized Codex agents with bounded parallelism, explicit ownership, native messaging, and evidence-based synthesis. Use when two or more independent investigations, reviews, or implementation streams can run concurrently without sharing writable files, or when the user asks for swarm, team, parallel agent, or multi-reviewer execution.