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
Use this skill to teach Claude exactly which Kotlin and Compose changes hot-reload under Compose HotSwan and which trigger a full incremental rebuild fallback. Root cause is Android Runtime (ART) class schema immutability; only method bodies are mutable at runtime, so any change to fields, signatures, constructors, interfaces, inline functions, or new resource ids forces a rebuild. Covers the supported-changes table, the rebuild-forcing list, the diff-then-batch workflow that keeps a hot-reload session inside the fast path, and the inline-function and new-resource-id pitfalls. Trigger when the user asks "why did this rebuild?", "why isn't this hot reloading?", wants to learn HotSwan's boundaries before adopting it, or when reviewing a refactor that risks pushing a hot-reload session into a full rebuild.
Use this skill to keep Jetpack Compose state alive across HotSwan hot reloads by understanding the three escalating tiers Compose HotSwan uses (tier 1 targeted recomposition, tier 2 composition reset, tier 3 Activity.recreate) and choosing edits and state holders that stay inside tier 1 where scroll position, lazy items, dialog state, and per-composable remember values all survive. Explains which edits force escalation, which state holders survive each tier, and how to hoist transient UI state when the iteration loop must escalate. Use when the developer says "scroll jumped to top after a hot reload", "lost dialog state", "lazy column re-fetched", "tab selection reset", asks why HotSwan reload escalated to tier 2 or 3, plans a refactor and needs to know which scope it touches, or wants to know which state holders survive composition reset.
Use this skill to instrument a Jetpack Compose composable with `@TraceRecomposition` from `skydoves/compose-stability-analyzer` so per-recomposition diffs (which state or parameter changed, what value transition) print to logcat under the `Recomposition` tag. Works in release-with-debug-symbols builds where Android Studio Layout Inspector cannot reach, and feeds the IntelliJ / Android Studio plugin's live recomposition heatmap (green under 10, yellow 10–50, red 50+). Covers the Gradle plugin setup, the `ComposeStabilityAnalyzer.setEnabled(BuildConfig.DEBUG)` runtime gate that keeps the instrumentation out of production, and the handoff to debug-time Layout Inspector and CI `stabilityCheck`. Use when the user mentions `@TraceRecomposition`, "trace recomposition", "compose-stability-analyzer", "recomposition logcat", "recomposition heatmap", "release-mode recomposition counts", or needs to confirm a stability fix in a release-like build.
Create, maintain, or migrate functional skills; supports creating from a requirement brief and migrating an existing legacy skill directory into a functional skill structure.
>- This skill should be used when the user asks to "add a cask to homebrew", "create a homebrew cask", "add an app to homebrew", "new cask for", "homebrew cask for", "package an app for homebrew", "submit to homebrew-cask", "make a cask", "contribute a cask", or wants to package a macOS application for Homebrew.
>- This skill should be used when the user asks to "audit claude permissions", "audit permissions", "review local claude settings", "promote permissions to global", "clean up claude settings", "find permission patterns", or wants to identify project-local Claude Code permissions that should be added to global configuration.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Automatically fix broken OpenCLI adapters when commands fail. Load this skill when an opencli command fails — it guides you through collecting a trace artifact, patching the adapter, retrying, and filing an upstream GitHub issue after a verified fix. Works with any AI agent.
Use at the start of any OpenCLI session — this is the top-level map of what `opencli` can do, how to discover adapters, what flags and output formats are universal, and which specialized skill to load next. Point here when an agent asks "what can opencli do?" or "how do I find the right command?".
基于 opencli 命令的智能搜索路由器。当用户想要使用 OpenCLI、CLI 或 API 搜索、查询、查找或研究信息时，尤其是涉及指定网站、社交媒体、技术资料、新闻、购物、旅游、求职、金融或中文内容时，务必使用此 skill
Orchestrates implementation of a plan file by delegating work to subagents in parallel. Verifies git branch state, tracks progress, and ensures high-quality implementation. Invoke with a plan file path and optional model override: /implement plans/my-plan.md [--model sonnet]
Consolidated expert library for professional Godot 4.7+ game and application development. Orchestrates 92 Domain Skills through architectural workflows, anti-pattern catalogs, performance budgets, and Server API patterns. Use when: (1) starting a new Godot project, (2) designing game or app architecture, (3) building entity/component systems, (4) debugging performance or physics issues, (5) choosing between 2D/3D approaches, (6) implementing multiplayer, (7) optimizing draw calls or script time, (8) porting between platforms, (9) migrating from 4.6 to 4.7. Primary entry point for ALL Godot development tasks. Keywords: Godot 4.7, AreaLight3D, HDR, Asset Store, godot-master.
Expert blueprint for dedicated / headless multiplayer hosts: ENet/DTLS, authority validation, safe packet decode, matchmaker handoff, and health telemetry. Use when building authoritative servers, --headless hosts, or hardening host networking. Keywords: dedicated server, headless, ENet, DTLS, authority, safe_packet_decoder, multiplayer host, WebSocketMultiplayerPeer.
> Install, share, sync, and create AI agent skills across coding tools (Claude Code, Cursor, Codex, OpenCode, Copilot, Pi) using the agr CLI. Use skill ("install the pdf skill", "agr add ..."), sync agent resources across tools, share skills with their team, scaffold a new SKILL.md, run a skill ephemerally (agrx), set up a repo to manage AI agent dependencies, or configure tools/sources/instruction-syncing. Also use whenever an agr.toml or agr.lock is present in the project and the user is doing resource-management work.
> Debrief an AI agent skill (SKILL.md) after using it — capture session feedback or a retrospective and fold it back into the skill. Use whenever X", "feedback on X skill", "improve the X skill", "update the X skill", "let's revise X based on what we just did", "the X skill should also handle Y", "X didn't trigger when it should have", or otherwise wants to capture lessons from a session back into the skill that drove it. Handles in-repo skills (under skills/) by editing the source, committing, and re-installing via `agr upgrade`. Handles remote/upstream skills by offering to fork them in-repo or to file a GitHub issue via `gh`. Do NOT use for greenfield skill authoring (a separate concern — see `anthropics/skills/skill-creator`) or for installing / syncing / removing skills (use the `agr` CLI directly).
> Map, measure and improve business processes — SIPOC and swimlane capture, cycle-time and bottleneck analysis, handoff diagnosis, and a payback-ranked improvement backlog. Use when a process is slow, error-prone, or crosses too many teams.
> Test and evaluation harness for AI agents — scenario suites, deterministic replay, regression diffing, cost and latency budgets. Use when agent quality is vibe-checked, before shipping a prompt or model change, or when evals drift.
> Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
> agent messaging. Use when building multi-agent systems, defining tool interfaces, or implementing agent-to-agent communication.
> Design multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management. Use when building pipelines of specialized agents, designing fan-out/fan-in patterns, or implementing fault-tolerant workflows.
> Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.
> This skill should be used when the user asks to "evaluate LLM output quality", "set up LLM-as-judge", "build an eval rubric", "compare model outputs pairwise", or "measure agent quality".
> This skill should be used when the user asks to "batch LLM requests", "should I use the batch API", "estimate batch vs realtime cost", "design a bulk LLM job", or "process thousands of prompts cheaply".
> OpenAI Codex CLI and cross-platform skill authoring. Use when setting up Codex CLI, converting or syncing skills between Claude Code and Codex, configuring agents/openai.yaml, or validating cross-platform skill compatibility.
> Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.
> This skill should be used when the user asks to "build a computer-use agent", "automate a GUI with an AI agent", "when to use computer use vs an API", "make browser automation reliable", or "design screenshot-driven agent actions".
> This skill should be used when the user asks to "decide reasoning effort", "set a thinking budget", "when to use extended thinking", "tune reasoning vs cost", or "should this task use a reasoning model".
> This skill should be used when the user asks to "estimate LLM costs", "count tokens in prompts", "optimize prompt token usage", "compare model pricing", or "reduce LLM API costs".
> Build MCP (Model Context Protocol) servers with tool definitions, resource providers, prompt templates, and transports. Use when exposing APIs to AI agents, building tool servers, converting OpenAPI to MCP, or creating MCP integrations.
> This skill should be used when the user asks to "audit prompts for safety", "check prompts for injection vulnerabilities", "manage a prompt catalog", "version control prompts", or "review prompt quality and compliance".
> Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.
> Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.
> Security audit and vulnerability scanning for AI agent skills before install. Detects prompt injection, dangerous code, exfiltration, credential harvesting, and supply chain risks. Use when evaluating untrusted skills or gating installs.
> Author, lint, and publish skill packages that satisfy the library authoring standard. Use when creating a new skill, reviewing a skill PR, or fixing one that never activates.
| Programmatic web search with context isolation. Use this skill for any research task where you need to search the web, filter results, and extract specific information — without polluting your context window with raw HTML and boilerplate. This is the default skill for web research. Triggered by "search for", "look up", "find", "research", "what's the latest on", or any query that requires current web information. Also use when asked to "search and filter", "find the important parts", or "extract the key details" — any case where the user wants curated, noise-free content.
When you want to set up an agent loop, cron-scheduled task, or recurring workflow that runs autonomously in Claude Code. Judgment layer on top of ScheduleWakeup, CronCreate, and the /loop skill — decides whether to use dynamic pacing (self-scheduling wake-ups), cron scheduling (fixed intervals), or a one-shot loop; tunes delay to avoid the 5-minute cache-miss cliff; designs idempotent loop bodies; sets bail-out conditions so loops don't run forever. Examples of loops to loopify — weekly review pulse, daily brief generation, hourly monitoring of a metric, periodic vault compilation, upstream-check for an adapted skill, sponsorship-pipeline refresh, YouTube-transcript-batch-download, morning startup routine. Triggers on "/loopify," "set up a loop," "schedule this task," "run this daily," "run this weekly," "cron this," "make this recurring," "automate this on a schedule," "keep this running until X." Part of the -ify trifecta (skillify / toolify / loopify) for extending Claude Code. NOT for authoring a new skill — that's skillify. NOT for adding a tool/integration — that's toolify.
When you want to create, adapt, or update a Claude Code skill in one of your sibling repos (list your own repos in ~/.config/makerskills/skillify/repos.yaml; defaults to makerskills). Routes to the right mode automatically. Modes — CREATE (from-chat / from-video / from-dump / from-scratch) turns a workflow, brief, recording, or fresh idea into a new skill. ADAPT ports an external skill (GitHub URL, agentskills.io, local disk) into your namespace with three-bucket classification (keep/adapt/add) + license check + attribution. UPDATE improves existing skills from learnings with cross-skill propagation, memory-vs-skill triage, and semver discipline. Defers to Anthropic's guidance (compound-engineering:create-agent-skill, compound-engineering:skill-creator, compound-engineering:heal-skill) for schema and best-practice depth. Triggers on "/skillify," "create a skill," "make this a skill," "skill from this chat," "extract a skill from what we've been doing," "adapt this skill," "port this skill," "fork this skill," "borrow this skill," "update X skill," "apply this to the relevant skills," "propagate this learning," "improve [skill]," "fix [skill]," "iterate on [skill]." Part of the -ify trifecta (skillify / toolify / loopify) for extending Claude Code.
When a solution seems impossible and the path forward is blocked — the antidote to any roadblock or wall. Refuses to take no for an answer: classifies what kind of \"no\" you actually hit, then runs targeted lateral-thinking techniques (assumption autopsy, inversion, first principles, constraint toggling, analogical transfer, and more from a 10-technique inventory) until the wall cracks or is proven load-bearing. Generates a minimum of 10 angles before evaluating any. AGENTS: use this on yourself mid-task — when an API doesn't support what you need, an approach has failed twice, or a \"that's not possible\" / \"the only option is\" sentence is forming in your response, run the fast path BEFORE reporting a dead end; every dead-end report should arrive with tried-angles receipts. Archives every wall to ~/.config/makerskills/unstuck/archive/ so you learn which techniques crack YOUR walls. Sits upstream of decide: turns \"impossible\" into 2–3 viable angles, then decide picks one. Triggers on \"/unstuck,\" \"I'm stuck,\" \"this seems impossible,\" \"we hit a wall,\" \"there's no way to,\" \"they said no,\" \"dead end,\" \"out of options,\" \"I've tried everything,\" \"work around this,\" \"think outside the box.\
Core principle that the main agent is a coordinator, not an implementer. All work must be delegated to subagents.
Core Smart Ralph skill defining common arguments, execution modes, and shared behaviors across all Ralph plugins.
This skill should be used only when the user explicitly asks to use `$ralph-specum-design`, or explicitly asks Ralph Specum in Codex to run the design phase.
This skill should be used only when the user explicitly asks to use `$ralph-specum-index`, or explicitly asks Ralph Specum in Codex to generate or refresh index artifacts.
This skill should be used only when the user explicitly asks to use `$ralph-specum-requirements`, or explicitly asks Ralph Specum in Codex to run the requirements phase.
This skill should be used only when the user explicitly asks to use `$ralph-specum-tasks`, or explicitly asks Ralph Specum in Codex to run the tasks phase.
This skill should be used only when the user explicitly asks to use `$ralph-specum-triage`, or explicitly asks Ralph Specum in Codex to triage a large effort into multiple specs.
This skill should be used when running an interactive interview before a spec phase, gathering requirements through dialogue, asking the user clarifying questions before delegating to a subagent, or when any Ralph phase command (research, requirements, design, tasks) needs adaptive brainstorming dialogue. Covers the 3-phase algorithm (Understand, Propose Approaches, Confirm and Store).
This skill should be used when generating spec artifacts (research.md, requirements.md, design.md, tasks.md), formatting agent output, structuring phase results, or when any Ralph agent needs guidance on concise, scannable output formatting. Applies to all Ralph spec phase agents.