Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
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octo-devtools
by meshmakersDrives OctoMesh local development through the octo-tools PowerShell cmdlets — building repos in dependency order, starting and stopping services (always non-interactively for agents via Start-Octo -nonInteractive $true / Stop-Octo), managing Docker or local kind Kubernetes infrastructure, syncing and branching git repos, NuGet package propagation, certificates, and octo-cli context/auth setup. Use it for any OctoMesh dev-environment or DevOps task. Trigger on build, compile, dotnet build, start services, stop services, Start-Octo, Stop-Octo, Invoke-BuildAll, Invoke-Build, infrastructure, docker compose, kind, Kubernetes, Install-OctoKubernetes, Deploy-OctoOperator, git sync, pull repos, push repos, Get-AllGitRepStatus, branch management, test branch, NuGet packages, clean build, kill dotnet, certificates, clone repos, register context, login local/staging/production, infrastructure backup, or any OctoMesh PowerShell cmdlet name.
octo-commit
by meshmakersStructured workflow for committing finished work across OctoMesh repositories: scans repos for changes, resolves or creates the Azure DevOps work item, builds the AB# commit message, verifies the build/tests, and optionally opens a PR. Enforces the review checkpoint — never pushes or opens a PR without explicit user approval in the current session. Trigger on: commit, push, PR, pull request, merge, finish work, done, ship it, wrap up, AB#, work item, ready to commit, close task, create branch, feature branch, dev/, Azure DevOps.
octo-agent
by meshmakersThis skill should be used when the developer needs to investigate OctoMesh bugs, diagnose CK model failures (ResolveFailed, modelState, broken dependencies), inspect MongoDB tenant databases directly, perform selective builds to isolate issues, manage Docker volume backups for safe rollback, understand the DebugL NuGet dependency chain, or run targeted tests. Trigger on "help me fix", "investigate this bug", "what broke", "isolate the issue", "why is the model broken", "check database state", "backup before testing", "restore database", "CK model state", "NuGet dependency chain", "selective build", "test isolation", "service won't start", "build fails", "package not found", "ResolveFailed", "modelState 2", "health check failing", "error in logs", "cascade failure", "version bump broke", "data corruption", "tenant broken", "rollback my changes", "safe rollback", "something broke after upgrade", "why is this failing", "query MongoDB".
octo-mcp
by meshmakersDevelop and extend the OctoMesh MCP server (octo-mcp-service) — the Model Context Protocol server exposing ~181 tools that mirror octo-cli plus generic CK CRUD and aggregation/stream-data queries, used by AI assistants to administer OctoMesh tenants without the CLI or GraphQL. Use when adding or modifying MCP tools, classifying tool risk, wiring *ClientContext helpers, building file-transfer flows, or following the mandatory test conventions. Trigger on: MCP tool development, octo-mcp-service, adding MCP tools, McpRisk, MCP server, model context protocol server work in OctoMesh.
octo-app-builder
by meshmakersBuild a complete OctoMesh-powered application end to end — a custom Construction Kit data model published to the CK catalog, an HTTP API made of dataflow pipelines on the tenant's Mesh Adapter, an optional containerized web app the Communication Operator rolls out to Kubernetes, and a blueprint that packages all of it so a single InstallBlueprint provisions any tenant. Encodes the verified build sequence (model → pipelines → app image → blueprint → install → deploy → verify) and the pitfalls that silently break it (blueprint catalog cache, pipeline registration rotation, HTTP response shaping, registry injection). Use this skill whenever someone wants to CREATE something new on OctoMesh — an app, a demo, an API, a workload, a blueprint — even if they only mention one piece of it. Trigger on - build an app on OctoMesh, create a workload, new OctoMesh application, OctoMesh demo, blueprint authoring, package as blueprint, seedDataPath, ckModelDependencies, InstallBlueprint, Application entity, DeployWorkload, op
octo-deploy
by meshmakersGuide the promotion of an OctoMesh-powered app from local dev to the shared test-2 environment — publish the CK model and blueprint to the shared GitHub catalogs (octo-ckc / octo-bpm), push the app image to docker.mm.cloud, register a test-2 octo-cli context, install + deploy on the target tenant, and verify everything WITHOUT kubectl access. Covers both the repeatable CI lane (azure-pipelines publishing on main) and the manual first-time lane, plus test-2-specific triage (server-side catalog cache refresh, ImagePullBackOff via lastDeploymentError, Tailscale reachability). Staging and production are deliberately out of scope for now. Use this skill whenever someone wants to get an app, blueprint, CK model, or workload onto test-2 or asks how to make their locally working OctoMesh app available to others. Trigger on - deploy to test-2, promote to test, install on test-2, publish blueprint, publish CK model, shared catalog, blueprint-libraries-build, construction-kit-libraries-build, octo-bpm, push to docker.mm
octo-operator
by meshmakersUse this skill for developing and operating the OctoMesh Communication Operator — the .NET 10 KubeOps Kubernetes operator that watches CommunicationPool CRDs, connects to the Communication Controller over SignalR (/operatorHub), and runs helm upgrade/uninstall for Adapter and Application workloads. Covers edge vs central deployment modes, the Helm values layering model (context < base < overrides), secret injection tiers, CRD generation, the TUnit + Microsoft.Testing.Platform test workflow on .NET 10, and the local kind cluster developer loop. Also covers the octo-helm-core chart repository (octo-mesh, octo-mesh-crds, octo-mesh-communication-operator, octo-mesh-schema-provider, octo-mesh-demo-app). Trigger on communication operator, KubeOps, CommunicationPool, CRD, CRD generation, helm chart, helm upgrade, WorkloadReconciler, operatorHub, edge deployment, central deployment, octo-helm-core, kind cluster, workload deployment, ICommunicationPoolKubernetesGateway, IOperatorHubClientFactory.
octo
by meshmakersPrimary entry point for ALL OctoMesh tasks — this skill routes to specialized sibling skills when needed. Use this skill for OctoMesh CLI operations (octo-cli), data model exploration, runtime instance browsing via GraphQL, AND as the gateway for building/DevOps (routes to octo-devtools), debugging/investigation (routes to octo-agent), and pipeline YAML (routes to pipeline-expert). Trigger on anything related to OctoMesh — CLI operations, managing users, roles, tenants, clients, identity providers, service hooks, authentication, environment switching, platform administration, data model exploration, Construction Kit models, CK types, enums, attributes, associations, GraphQL schema introspection, runtime instance queries, listing, counting, searching, filtering, inspecting entities, building projects, starting services, Docker, debugging, investigating bugs, pipeline YAML, ETL, committing changes, creating PRs, finishing work, Azure DevOps work items, AB#, feature branches, or any interaction with the mesh pla
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.