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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inference-value-ledger
by cfreglyRender the leadership value-prop ledger for the inference effort: the deployable wins vs the FlashInfer-TRTLLM + best-tuned-vLLM 0.21/0.22 baseline, grouped DONE / IN-PROGRESS / NOT-DONE / CLOSED-NEGATIVE, each row data-backed by a perf-lake campaign (live sol_rigor + verdict tier), plus the ranked GRIND FRONTIER of next levers (the always-be-grinding performance ratchet). Joins the curated perf-tune-report/configs/value-findings.yaml registry with the live campaigns via `perftunereport value_view`. Flags any finding whose backing campaign is missing or ungrounded. Use when you need to show value / report to leadership / answer "what have we uncovered that we can deploy, revalidate, or pursue". Triggers on "value ledger", "value prop", "show value", "show the value prop", "leadership view", "what wins do we have", "vs flashinfer", "value-findings", "what can we deploy", or any combination of "value / wins / findings / leadership / report" with "inference / vllm / flashinfer / perf".
inference-perf-bench
by cfreglyCanonical inference perf-bench skill (formal name. The colloquial alias is `ai-bench` - identical behaviour). Drives NVIDIA AIPerf + the replay-playback dataset against an in-cluster vLLM endpoint to measure TTFT, ITL, throughput, tok/s/user, request latency, and prefix cache hit rate. Iterative 9-phase workflow. Use when promoting a model to staging/prod, after vllmArgs / vLLM / KV-cache-dtype changes, or for A/B comparison across configs. Triggers on "perf-bench", "AIPerf", "Replay Playback", "throughput sweep", "TTFT P95", "concurrency sweep on inference", "/run-perf-bench", "benchmark throughput", "benchmark latency", "run aiperf", "performance test", or any combination of "perf / latency / throughput / tps / TTFT / ITL" with "inference / vllm / serverless / bench".
inference-aa-workload
by cfreglyReproduce the Artificial Analysis (AA) language-model performance workload shapes against an OpenAI-compatible chat endpoint using NVIDIA AIPerf. Drives the three AA text shapes (1k input / >=1k answer, 10k / >=1.5k, 100k / >=2k) with temperature 0, top_p 1, and the vLLM-style min_tokens + ignore_eos "at least N answer tokens" guarantee. Two modes: synthetic (AIPerf generates the prompt at the token mean) and dataset-replay (a generated o200k_base-counted JSONL replayed identically). Ships a self-contained script and an `aa` perf_tune_report cell_run backend. Use when comparing a hosted inference endpoint to AA leaderboard numbers or reproducing AA's methodology. Triggers on "artificial analysis workload", "AA benchmark", "AA 1k/10k/100k shapes", "reproduce artificialanalysis.ai", "AA methodology", "aa-10k", "compare to AA leaderboard", or any combination of "artificial analysis / AA" with "workload / shape / benchmark / dataset".
zymtrace-anchored-query
by cfreglyReusable wrapper for the knowledge-base-first SQL pattern, adapted to the zymtrace ClickHouse profiling backend. Operator names the metric / question / time range. Skill anchors the `zymtrace_profiling.events` schema first (DESCRIBE + label-value + cardinality probes), derives the safe narrow SQL, runs it via `kubectl port-forward` + `curl -X POST`, and saves the raw payload to a provenance-bearing bundle per the perf-lake-contract. Workload-agnostic. The ClickHouse cousin of `prometheus-anchored-query`. Triggers on "zymtrace anchored query", "clickhouse anchored query", "zymtrace query", "save zymtrace payload", "anchored zymtrace", "anchored clickhouse", or any combination of "zymtrace / clickhouse / profile" with "anchored / safe / provenance / query / saved-payload".
analyze-zymtrace-workload
by cfreglyInvestigate a GPU or CPU workload through the zymtrace MCP. The MCP does most of the analysis. This skill enforces the cross-view discipline -- always pull the matching opposite-side flamegraph (CPU for GPU workloads, GPU for CPU workloads) with the same filter. Most bottlenecks hide on the side the customer didn't ask about. Triggers on "analyze my GPU workload", "where's the bottleneck in vllm", "investigate my training job", "find the hot kernel", "GPU isn't saturated", "investigate using flamegraph", "use zymtrace mcp to analyze", or any combination of "analyze / investigate / bottleneck / hot kernel" with "GPU / CPU / vllm / training / flamegraph / zymtrace".
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.