Agent Skills
Instruction packs that give your AI agent know-how — some work anywhere, some only with the tool they came with.
✦ Standalone skills3,373
Self-contained. Install one into any project and it works on its own — no other software needed.
🧰 Tool add-ons952
Come bundled with a specific tool and only work together with it — they teach your agent how to operate that tool.
AI & Machine Learning
78 standalone skillstwilio-voice-outbound-calls
✓★ 4,081by openai
Make outbound phone calls via Twilio's Programmable Voice REST API. Covers the full voice platform: calls.create(), answering machine detection (AMD), conference-based agent bridging, call recording, status tracking, and SIP Trunking. Use this skill for outbound calls, sales dialers, or when asking what voice APIs are available.
research-router-skill
✓★ 4,081by openai
Route broad or ambiguous life-sciences research requests to the right skills, normalize core entities, optionally parallelize independent evidence gathering with subagents when available, and synthesize a concise evidence-backed answer. Use when a user asks a general life-sciences question that could span multiple sources or analysis types.
figma-use-slides
✓★ 4,081by openai
This skill helps agents use Figma's use_figma MCP tool in the Slides context. Can be used alongside figma-use which has foundational context for using the use_figma tool.
nemoclaw-user-get-started
✓★ 4,081by openai
Installs NemoClaw, launches a sandbox, and runs the first agent prompt. Use when onboarding, installing, or launching a NemoClaw sandbox for the first time. Trigger keywords - nemoclaw quickstart, install nemoclaw openclaw sandbox, nemohermes quickstart, hermes agent nemoclaw, run hermes openshell sandbox, nemoclaw prerequisites, nemoclaw supported platforms, nemoclaw hardware software, nemoclaw windows wsl2 setup, nemoclaw install windows docker desktop.
twilio-agent-augmentation-architect
✓★ 4,081by openai
Planning skill for augmenting human agents with real-time AI intelligence. Qualifies the developer's use case across coaching, compliance, QA, and routing to recommend the right Conversation Intelligence + Conversation Memory + TaskRouter architecture. Handles both "I want to add AI coaching to my call center" and "configure Conversation Intelligence operators for script adherence."
build-zoom-virtual-agent
✓★ 4,081by openai
Use when using Virtual Agent.
twilio-enterprise-knowledge
✓★ 4,081by openai
Add knowledge retrieval to AI agents using Twilio's Enterprise Knowledge product. Enterprise Knowledge is a centralized, searchable repository of your organization's documents, websites, and content — FAQs, support policies, warranty terms, product catalogs. Current models don't have access to how you run your business today. Enterprise Knowledge gives agents a way to query this repository during a conversation and ground their responses in your actual approved source material. This skill covers
twilio-ai-agent-architect
✓★ 4,081by openai
Planning skill for AI-powered conversational agents. Qualifies the developer's use case across outcome sophistication, entry point, and customer profile to recommend the right Twilio Conversations architecture and implementation skills. Handles both high-level requests ("build me a voice AI assistant") and specific ones ("integrate ConversationRelay with my OpenAI backend").
ai-generation-persistence
✓★ 4,081by openai
AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation
vercel-sandbox
✓★ 4,081by openai
Vercel Sandbox guidance — ephemeral Firecracker microVMs for running untrusted code safely. Supports AI agents, code generation, and experimentation. Use when executing user-generated or AI-generated code in isolation.
maintainer-review
✓★ 3,334by openai
Review a GitHub issue or pull request URL as an openai-agents-js maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing issue validity or severity, deciding whether an issue should be prioritized or closed, determining whether a requested feature represents an unmet need rather than a discoverability o
final-release-review
✓★ 3,334by openai
Perform a release-readiness review by locating the previous release tag from remote tags and auditing the diff (e.g., v1.2.3...<commit>) for breaking changes, regressions, improvement opportunities, and risks before releasing openai-agents-js.
m365-agents-dotnet
✓★ 2,676by microsoft
Microsoft 365 Agents SDK for .NET. Build multichannel agents for Teams/M365/Copilot Studio with ASP.NET Core hosting, AgentApplication routing, and MSAL-based auth. Triggers: "Microsoft 365 Agents SDK", "Microsoft.Agents", "AddAgentApplicationOptions", "AgentApplication", "AddAgentAspNetAuthentication", "Copilot Studio client", "IAgentHttpAdapter".
m365-agents-py
✓★ 2,676by microsoft
Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth. Triggers: "Microsoft 365 Agents SDK", "microsoft_agents", "AgentApplication", "start_agent_process", "TurnContext", "Copilot Studio client", "CloudAdapter".
m365-agents-ts
✓★ 2,676by microsoft
Microsoft 365 Agents SDK for TypeScript/Node.js. Build multichannel agents for Teams/M365/Copilot Studio with AgentApplication routing, Express hosting, streaming responses, and Copilot Studio client integration. Triggers: "Microsoft 365 Agents SDK", "@microsoft/agents-hosting", "AgentApplication", "startServer", "streamingResponse", "Copilot Studio client", "@microsoft/agents-copilotstudio-client".
wiki-agents-md
✓★ 2,676by microsoft
Generates AGENTS.md files for repository folders — coding agent context files with build commands, testing instructions, code style, project structure, and boundaries. Only generates where AGENTS.md is missing.
wiki-llms-txt
✓★ 2,676by microsoft
Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.
fetching-dbt-docs
★ 608by dbt-labs
Retrieves and searches dbt documentation pages in LLM-friendly markdown format. Use when fetching dbt documentation, looking up dbt features, or answering questions about dbt Cloud, dbt Core, or the dbt Semantic Layer.
repo-intake-and-plan
★ 504by lllllllama
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
creating-agents-in-medusa
★ 195by medusajs
Use when building an internal admin-facing AI agent in a Medusa project. These agents are operated by merchants and store operators — not customers. Covers data models, module service, agent runtime (tools, system prompt, streamText), streaming API routes (NDJSON), and admin UI chat extensions. Load for any internal agent type: store operations assistant, product audit, cohort analysis, customer service tooling for support staff, etc. Do NOT use for customer-facing agents (storefront chatbots, b
agent-skills
★ 139by datadog-labs
Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.
agent-observability-eval-bootstrap
★ 139by datadog-labs
Bootstrap 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 confi
agent-observability-trace-rca
★ 139by datadog-labs
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
dd-apm
★ 139by datadog-labs
APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.
agent-install
★ 139by datadog-labs
Install the Datadog Agent on Linux hosts via SSH with Single Step Instrumentation (SSI) enabled — SSI automatically instruments applications for APM without code changes. Only use if no agent is installed yet.
agent-observability-session-classify
★ 139by datadog-labs
Classify whether user intent was satisfied in a Datadog Agent Observability trace or session. Three modes: (1) session_id — classify a single CMD+I assistant session with RUM; (2) trace_id — classify a single Agent Observability trace without RUM; (3) ml_app — sample and classify multiple sessions or traces from a given LLM app. Output is compact by default (verdict + one-sentence reason). Use when evaluating satisfaction, classifying sessions/traces, labeling data, or generating signal for agen
agent-observability-experiment-analyzer
★ 139by datadog-labs
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.
performing-multi-agent-code-review
★ 121by bitwarden
Perform a rigorous, multi-agent code review with architecture-compliance, parallel quality/security analysis, finding validation, and severity audit. Use when the user asks for a structured, deep, thorough, multi-pass, or multi-agent code review — or a review that includes architecture/pattern compliance, confidence-scored findings, or a severity audit. Use when the user asks for a code review across a commit range, time window, or N most recent commits in a locally checked-out repo.
reproduce-bug-report
★ 119by warpdotdev
Launch Oz cloud agents with computer use to reproduce UI-focused bug reports, capture visual evidence, and report reproduction findings. Use when investigating a specific interactive or visual bug from an issue, ticket, support report, or prompt.
council
★ 119by warpdotdev
Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation. Use this skill whenever the user asks for a council, second opinions, multiple agents/models to evaluate one question, parallel investigation, red-team/blue-team comparison, or help deciding between competing technical approaches.