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.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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langfuse-llm-observability
by binrogithubUse this skill when deploying or integrating Langfuse for LLM observability, tracing, generations, usage, latency, cost, errors, evaluations, prompts, or LiteLLM/application instrumentation. Langfuse is not a MaaS token platform, does not issue or manage MaaS API keys, and normally does not call MaaS providers directly.
karmada-k8s-switch-skill
by binrogithubUse when the user wants to prepare a local Karmada lab, install and verify the Karmada control plane, deploy the tested member1/member2 stateless failover PoC in /root/karmada, switch traffic between Kubernetes clusters, validate cutover behavior, or tear the environment down. This skill favors the repo's proven scripts and manifests over ad hoc setup so the PoC is faster and more accurate.
gaussdb-adaptation
by binrogithubUse this skill when porting SQL Server or vanilla PostgreSQL code to Huawei GaussDB (Kernel 505.x / openGauss-based). TRIGGER when source code or SQL contains any of — `Microsoft.Data.SqlClient`, `SqlBulkCopy`, `NEXT VALUE FOR`, `ON COMMIT DROP`, `CREATE TEMPORARY SEQUENCE`, `dbo.`, `NVARCHAR`, `ISNULL(`, `GETDATE()`, `OPTION (MAXDOP`, `TOP N`, `@@IDENTITY`, `WITH (NOLOCK)`, `[bracketed]` identifiers, `#tempTable`; OR csproj references `DotNetCore.GaussDB`, `DotNetCore.EntityFrameworkCore.GaussDB`, `HuaweiCloud.Driver.GaussDB`, `HuaweiCloud.EntityFrameworkCore.GaussDB`, `Npgsql.EntityFrameworkCore.PostgreSQL` alongside a GaussDB target; OR appsettings has `Persistence.Provider=GaussDb`; OR user mentions GaussDB / openGauss / 高斯数据库 / SHA256 SASL / `password_encryption_type` / apuração GaussDB migration.
mgc-cross-region-migration
by binrogithubExecute and troubleshoot Huawei Cloud server migration with Terraform in this repository. Prefer SMS first, and fallback to rsync staged migration when source is SMS-incompatible. Use when users ask to migrate ECS/on-prem VMware (especially la-north-2 and la-south-2), run the end-to-end workflow (`terraform init/apply` + `scripts/mgc_migrate.py`), validate prerequisites, map tfvars to runtime env vars, inspect migration output JSON, or diagnose SMS/MGC errors such as SMS.6504, SMS.6602, SMS.6603, SMS.6617, SMS.7605, and SMS.8115. Also use for Chinese requests like “跨区域迁移”, “MGC/SMS 迁移流程”, or “迁移排障”.
mgc-cross-region-migration
by binrogithubExecute and troubleshoot Huawei Cloud cross-region server migration with MGC/SMS and Terraform in this repository. Use when users ask to migrate ECS across regions (especially la-north-2 to la-south-2), run the end-to-end workflow (`terraform init/apply` + `scripts/mgc_migrate.py`), validate prerequisites, map tfvars to runtime env vars, inspect migration output JSON, or diagnose SMS/MGC errors such as SMS.6602, SMS.6603, SMS.6617, SMS.7605, and SMS.8115. Also use for Chinese requests like “跨区域迁移”, “MGC/SMS 迁移流程”, or “迁移排障”.
mrs-dws-finance-skill
by binrogithubUse this skill when setting up a financial risk control pipeline on Huawei Cloud. It helps configure OBS for raw and result data storage, MRS for Spark-based risk analysis and anomaly detection, and DWS for data warehousing and regulatory reporting. The skill covers risk scoring, AML/KYC compliance, cross-border monitoring, structuring detection, and automated report generation without relying on environment-specific details.
openshift-huawei-cloud-maas-skill
by binrogithubUse this skill when integrating OpenShift Dev Spaces or Eclipse Che browser-based VS Code with Cline and Huawei Cloud MaaS. It covers OpenAI-compatible MaaS configuration, Cline provider setup, verification, compatibility checks, and troubleshooting for browser-based development environments.
project-memory-rag-skill
by binrogithubBuild project-level knowledge persistence with RAG for enterprise agents that combines learned memory (derived insights that are not re-computed) with document RAG (raw knowledge), supports GraphRAG for connected facts and relationship traversal, MCP integration for tool-accessible memory, skill-based procedural memory (how-to not just what-happened), hybrid retrieval across learned and document stores, Huawei Cloud CSS/OpenSearch and OBS deployment, privacy controls, and audit trails. Use when Codex must design, provision, or generate a project memory RAG system with learned memory storage, document RAG pipeline, GraphRAG for entity relationships, MCP tool registry for memory access, skill procedural memory, hybrid search, or enterprise knowledge persistence.
oh-my-opencode-slim-huawei-maas
by binrogithubBootstrap AI coding stack: deploy LiteLLM proxy (via LiteLLM-Huawei-MaaS-Proxy skill), install opencode + oh-my-opencode-slim, mint virtual key, wire everything. Supports multi-key MaaS load balancing. TRIGGER on: opencode + Huawei MaaS setup, full-stack bootstrap, oh-my-opencode-slim-huawei-maas, deploy-litellm.
openhands-huawei-maas
by binrogithubConfigure, run, verify, or troubleshoot OpenHands with Huawei Cloud MaaS / ModelArts MaaS through an OpenAI-compatible endpoint. Use when Codex needs to install OpenHands CLI or uv, run OpenHands locally with Docker or CLI, set LLM_API_KEY/LLM_BASE_URL/LLM_MODEL, use MaaS models such as glm-5.1 or qwen coder models, verify MaaS connectivity, or explain how OpenHands replaces GitHub Copilot-style agent workflows with Huawei MaaS.
handoff
by binrogithubCompact the current conversation into a handoff document for another agent to pick up.
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.