Skill directory

Agent skills

Focused instruction packs that teach a coding agent one job well. Browse the catalog and use any of them inside a RiverX project.

18,175Skills

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18,175 skills

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Agent Developmentanthropics/claude-codeThis skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.07 Agent Developmentanthropics/claude-plugins-officialThis skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.07 Agent Developmentdavila7/claude-code-templatesThis skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.07 Agent Docs API Openapiruvnet/rufloAgent skill for docs-api-openapi - invoke with $agent-docs-api-openapi01 Agent Elements21st-dev/agent-elements|02 Agent Email CLIzaddy6/agent-email-skillOperate the agent-email CLI to create disposable inboxes, poll for new mail, retrieve full message details, and manage local mailbox profiles. Use when the user needs terminal-based email inbox access for LLM or agent automation workflows.03 Agent Email Inboxresend/resend-skillsUse when building any system where email content triggers actions — AI agent inboxes, automated support handlers, email-to-task pipelines, or any workflow processing untrusted inbound email. Always use this skill when the user wants to receive emails and act on them programmatically, even if they don't mention "agent" — the skill contains critical security patterns (sender allowlists, content filtering, sandboxed processing) that prevent untrusted email from controlling your system.04 Agent Email Patternsagentmail-to/agentmail-skillsArchitecture 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 whether an agent needs email, or choosing an email provider; do not use for AgentMail SDK method calls or basic send/receive implementation.05 Agent Evalaffaan-m/eccカスタムタスクでコーディングエージェント（Claude Code、Aider、Codex など）をヘッドツーヘッドで比較し、合格率、コスト、時間、一貫性のメトリクスを測定します01 Agent Evalcolbymchenry/codegraphBenchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.02 Agent Evaluationdavila7/claude-code-templatesTesting and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.01 Agent Evaluationmlflow/skillsUse this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. IMPORTANT - Always also load the instrumenting-with-mlflow-tracing skill before starting any work. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).018 Agent Evaluationneolabhq/context-engineering-kitEvaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.01 Agent Evaluationsickn33/agentic-awesome-skillsEvaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.02 Agent Experiencebrowserbase/skillsAudit the developer experience of a product, SDK, docs site, or SKILL.md by dropping multiple Claude subagents at it with only a tiny task prompt and real tools (WebFetch, Bash, Write). Agents must discover the docs themselves, install deps, ask for credentials if needed, and attempt real execution. The skill captures each agent's trace — tool calls, retries, wall time, errors — and scores on Setup Friction, Speed, Efficiency, Error Recovery, and Doc Quality, then emits an HTML report with an A–F grade and concrete fixes. Use when the user asks to audit agent experience, test a skill, audit docs for agents, check if a SDK is agent-friendly, validate a SKILL.md, measure agent DX, or benchmark how painful onboarding is for an AI agent. Triggers: 'audit agent experience', 'test this skill', 'audit docs for agents', 'is my SDK agent-friendly', 'run a DX audit', 'agent experience test', 'test my docs', 'how do agents do with my product'.06 Agent Flow Visualizationreason-machines/trending-skillsVS Code extension for real-time visualization of Claude Code agent orchestration as interactive node graphs01 Agent Fsdesplega-ai/agent-fs>-01 Agent Github Modesruvnet/rufloAgent skill for github-modes - invoke with $agent-github-modes01 Agent Github PR Managerruvnet/rufloAgent skill for github-pr-manager - invoke with $agent-github-pr-manager01 Agent Goal Plannerruvnet/rufloAgent skill for goal-planner - invoke with $agent-goal-planner01 Agent Gossip Coordinatorruvnet/rufloAgent skill for gossip-coordinator - invoke with $agent-gossip-coordinator01 Agent Governancegithub/awesome-copilot|01 Agent Graphslaunchdarkly/ai-toolingCreate and manage agent graphs — directed graphs of configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.01 Agent Hierarchical Coordinatorruvnet/rufloAgent skill for hierarchical-coordinator - invoke with $agent-hierarchical-coordinator01 Agent Hooksstarchild-ai-agent/official-skillsManage shell hooks — user scripts that run at agent lifecycle points to block, rewrite, or warn on actions, via the /hooks command.011 Agent Implementer Sparc Coderruvnet/rufloAgent skill for implementer-sparc-coder - invoke with $agent-implementer-sparc-coder01 Agent Installdatadog-labs/agent-skillsInstall the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically instruments applications for APM without code changes. Only use if no Datadog Agent is deployed on the cluster yet.01 Agent Integrationveris-ai/veris-skillsIntegrate a raw customer agent repo with Veris end to end. Installs or verifies veris-cli, logs in, creates or reuses a Veris environment, analyzes the repo, generates or updates `.veris/veris.yaml`, `.veris/Dockerfile.sandbox`, `.veris/.dockerignore`, configures runtime env vars, and can finish with `veris env push`. Use when a repo has no Veris setup yet, or when an existing `.veris/` integration is stale and needs to be refreshed.014 Agent Introspection Debuggingaffaan-m/eccStructured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.02 Agent Issue Trackerruvnet/rufloAgent skill for issue-tracker - invoke with $agent-issue-tracker01 Agent Load Balancerruvnet/rufloAgent skill for load-balancer - invoke with $agent-load-balancer01 Agent Manager Skilldavila7/claude-code-templatesManage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.01 Agent Manager Skillsickn33/agentic-awesome-skillsManage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.01 Agent Matrix Optimizerruvnet/rufloAgent skill for matrix-optimizer - invoke with $agent-matrix-optimizer01 Agent Md Refactordavila7/claude-code-templatesRefactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.02 Agent Md Refactorsoftaworks/agent-toolkitRefactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.02 Agent Memory Coordinatorruvnet/rufloAgent skill for memory-coordinator - invoke with $agent-memory-coordinator01 Agent Memory MCPdavila7/claude-code-templatesA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).01 Agent Memory MCPsickn33/agentic-awesome-skillsA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).01 Agent Memory Systemsdavila7/claude-code-templatesMemory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm01 Agent Memory Systemssickn33/agentic-awesome-skillsMemory is the cornerstone of intelligent agents. Without it, every02 Agent Mesh Coordinatorruvnet/rufloAgent skill for mesh-coordinator - invoke with $agent-mesh-coordinator01 Agent Migration Planruvnet/rufloAgent skill for migration-plan - invoke with $agent-migration-plan01 Agent Multi Repo Swarmruvnet/rufloAgent skill for multi-repo-swarm - invoke with $agent-multi-repo-swarm01 Agent Native Designagents365-ai/365-skillsUse when designing, reviewing, or refactoring a CLI that must serve AI agents alongside humans, or when converting an API or SDK into an agent-usable CLI interface.010 Agent Neural Networkruvnet/rufloAgent skill for neural-network - invoke with $agent-neural-network01 Agent Observability Eval Bootstrapdatadog-labs/agent-skillsBootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.01 Agent Observability Eval Pipelinedatadog-labs/agent-skillsEnd-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", "onboard me to datasets and experiments", "walk me through experiments", "I have an ml_app, now what", "Agent Observability onboarding", "guided experiment setup", "from traces to experiments", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. Stop early with `--stop-after <phase>` to short-circuit at evaluators or dataset, or resume mid-flow with `--start-at <phase>`.04

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