Agent Workflow Claude Skills
4 121 agent workflow skills from 665 authors. They configure the agents themselves: memory, prompts, context and other skills. Half of them fit into 1 845 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 541 copies of these same skills sitting in other people's repositories — counted once here, not 541 times.
4 121 unique 665 authors 2 767 updated this month 506 from vendors
> Configures Elastic Cloud authentication and environment defaults. Use when setting up EC_API_KEY, configuring Cloud API access, or when another cloud skill requires credentials.
> Create and manage Agent Builder agents and custom tools in Kibana. Use when asked to create, update, delete, test, or inspect agents or tools in Agent Builder.
Elastic ML anomaly detection skill — investigation/RCA, score explanation, job operations (create, datafeed, start/stop, results), and troubleshooting (missing docs, memory limits, datafeed health, lifecycle). Operates against Kibana Agent Builder MCP tools (`ad_*`) on `.ml-anomalies-*`, `.ml-config`, `.ml-notifications-*`, `.ml-annotations-*`. Use when answering "what broke?"/"which entity?"/RCA, "why is score high/low?"/renormalization, "datafeed stopped"/"memory limit", or any request to set up or configure an ML anomaly detection job.
Maximize context window efficiency, reduce latency, and prevent lost-in-middle issues through strategic masking and compaction. Use when token budgets are tight, tool outputs overflow the context, conversations drift from intent, or latency spikes from cache misses.
Append a learning entry to AGENTS_LEARNING.md when an AI agent makes a mistake. Auto-activates after a pre-write audit auto-fix, a retrospective correction loop, or a mid-session user correction. Use when: mistake, wrong, correction, my bad, agent error, learning log.
Analyze conversation corrections to detect skill gaps and prepare targeted skill-library maintenance tasks. Use after any session with user corrections, rework, or retrospective requests. After finding correction loops, also load +common/common-learning-log to persist mistake entries to AGENTS_LEARNING.md.
Standardizes the creation and evaluation of high-density Agent Skills (Claude, Cursor, Windsurf). Ensures skills achieve high Activation (specificity/completeness) and Implementation (conciseness/actionability) scores. Use when: writing or auditing SKILL.md, improving trigger accuracy, or refactoring skills to reduce redundancy and maximize token ROI.
Enforce session-cost telemetry and execution-metadata reporting. Use when explicitly invoking get_session_cost, reporting token/cost usage, applying telemetry or cost guidance during a workflow handoff, or writing artifacts/session-cost.md at a workflow terminal state.
Workflow skill for evals run.
Convert delivery findings into skill, eval, workflow, and documentation improvements.
Capture delivery evidence, commands, changed files, blockers, and standards feedback after a work session.
Standards for creating new High-Density Agent Skills with optimal token economy.
Claude Code plugin for Butterbase — 30+ guided skills and auto-configured MCP for the AI-native backend-as-a-service.
Use when designing, deploying, or debugging a Butterbase Agent (declarative LLM/tool graph), registering an MCP server for tool use, or wiring access controls and rate limits. Agents are first-class app resources defined by a `graph_spec` and invoked over `/v1/<app_id>/agents/<name>/runs`.
Use when building stateful per-key actors — chat rooms, multiplayer rooms, rate limiters, long-running agents, leaderboards — that need persistent in-memory + storage state across requests
Use as the agents build stage of the Butterbase journey. Implements the Agents section of 02-plan.md by delegating to the `agents` skill for each agent. Registers any required MCP servers, validates each graph_spec, creates the agent, and smokes it via invoke_agent. Skipped if the plan has no agents.
Use before any Butterbase platform-touching action (or when journey-preflight is invoked directly) to verify the user has a Butterbase account, the MCP server is connected, BUTTERBASE_API_KEY is set, and an app_id exists for this project. Re-run automatically if docs/butterbase/03-preflight.md is older than 24h or any required value is null.
Use when the user says "build an app", "let's start", "help me build", "I have an idea for", "ship it", or otherwise signals they want to go from idea to deployed Butterbase app. Orchestrates the full guided journey (idea → plan → preflight → build → deploy → optional hackathon submit) by reading docs/butterbase/00-state.md and dispatching to the next stage skill.
Use when the user wants to read/write their Butterbase substrate — the per-user agent-memory backend that holds entities, business state, institutional memory, and an append-only action ledger. Use for: founder copilots, AI agents that need memory across sessions, anything that proposes actions on the user's behalf.
Use when you need to install the embedded robot agents into either .cursor/agents or .claude/agents, selecting the destination interactively and copying the embedded agent definitions from project assets. This should trigger for requests such as Install embedded agents; Bootstrap .cursor/agents; Bootstrap .claude/agents; Copy robot agents. Part of cursor-rules-java project
Create your OpenAI Agents SDK skill in one prompt, then learn to improve it throughout the chapter
Generated by Gremlin Forge Meta-Agent 🍆👾⚡
Use when you need to generate an AGENTS.md file for a Java repository — covering project conventions, tech stack, file structure, commands, Git workflow, and contributor boundaries — through a modular, step-based interactive process that adapts to your specific project needs. This should trigger for requests such as Create AGENTS.md; Update AGENTS.md file; Add agent instructions. Part of cursor-rules-java project
Create your Google Agent Development Kit skill in one prompt, then learn to improve it throughout the chapter
Create your Claude Agent SDK skill in one prompt, then learn to improve it throughout the chapter
Create a skill that orchestrates the write-execute-analyze loop to autonomously process data. Learn to implement error recovery, iterate toward robust solutions, and test your skill across diverse input scenarios. This is where specification-driven development meets real problem-solving.
Build a specification-first Digital FTE that orchestrates accumulated intelligence from Lessons 1-7. Learn to compose execution skills into production-ready agents, validate against specifications, and position for monetization.
Create your agent-integration skill from OpenAI SDK and LiteLLM documentation before learning framework integration
Create your LiveKit Agents skill from official documentation, then learn to improve it throughout the chapter
Main orchestrator for autonomous coding operations. Use when running autonomous sessions, coordinating components, managing the full lifecycle, or orchestrating implementations.
Create your Pipecat skill from official documentation, then learn to improve it throughout the chapter
Create handoff packages for session transitions. Use when ending sessions, preparing for continuation, saving session state, or creating resumable context.
Add new Agent templates to the meta-agent-skills framework.
| Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Route plain-language requests for Pi, Claude Code, Codex, OpenCode, Gemini CLI, or ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions ("telephone game" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation.
a set of guidelines to build with Botpress's Agent Development Kit (ADK) - use these whenever you're tasked with building a feature using the ADK
Agent Farm CLI quick reference. Use when running af commands to check correct syntax, subcommands, and flags. Prevents guessing at command names.
Agent skill for agentic-payments - invoke with $agent-agentic-payments
Agent skill for agent - invoke with $agent-agent
Agent skill for app-store - invoke with $agent-app-store
Agent skill for arch-system-design - invoke with $agent-arch-system-design
Use when designing an autonomous agent, planning agent architecture, building a scheduled automation, or creating a Claude Code agent workflow. Triggers: 'design an agent', 'build an automation', 'agent architecture', 'automate this workflow', 'create a scheduled agent', 'shell script agent'.
Agent skill for architecture - invoke with $agent-architecture
| Agent assistance skill that provides stuck detection, memory management, and session learning capabilities for AI agents
Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.
Agent skill for authentication - invoke with $agent-authentication
Validates agent configurations for model selection, tool permissions, focus areas, and approach quality. Use when reviewing, auditing, improving agents, or learning agent best practices.