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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zenml-backport
by zenml-ioBackport docs/examples changes to a pre-existing ZenML release. Use when changes merged to `develop` need to be reflected in a live release version. Triggers include "backport", "cherry-pick to release", "update release docs", or when docs/examples changes need to be applied to an existing release branch.
kitaru-release
by zenml-ioGuide the Kitaru release process end-to-end — diff develop against the last tag, classify commits (src / docs content / docs infra / release infra), filter docs-infra-only PRs out of the Python library CHANGELOG, remember that marketing-site work now lives in sibling zenml-io-v2, check zenml-io/zenml-frontend-monorepo for the latest stable kitaru-ui-v* release that will be bundled into the Python package and then copied into the Docker image, suggest a version bump, update CHANGELOG.md, run the smoke test, trigger the release workflow via gh, and rewrite the auto-generated GitHub Release notes into structured Highlights / Changed / Fixed sections. Interactive — pauses for user confirmation at version choice, CHANGELOG diff, smoke-test result, and release-notes draft. Use when the user invokes /kitaru-release, or says "cut a release", "make a release", "release kitaru", "new kitaru version", "ship a release", "prepare a release", "what would be in the next release", "bump kitaru version".
vertexai-to-zenml-migration
by zenml-ioMigrate Vertex AI Pipelines (Kubeflow Pipelines v2 / PipelineJob workflows) to idiomatic ZenML pipelines. Handles concept mapping (`@dsl.pipeline` -> `@pipeline`, `@dsl.component` -> `@step`, `PipelineJob.create_schedule(...)` -> `Schedule(...)`), artifact-contract translation (`Input[Dataset]`, `InputPath`, `.uri`, `.path`), Google Cloud Pipeline Components (GCPC) rewrites, dynamic control flow (`dsl.If`, `dsl.ParallelFor`, `dsl.Collected`), resource/config migration, and flags unsupported patterns (compiled template workflows, `dsl.ExitHandler`, path-coupled artifacts, schedule lifecycle parity) for human review. Use this skill whenever the user mentions Vertex AI Pipelines migration, KFP v2 to ZenML, PipelineJob migration, GCPC migration, or asks how a Vertex/KFP concept maps to ZenML — even if they do not explicitly say "migrate". Also use when they paste KFP DSL code, compiled pipeline YAML/JSON, Vertex submission code, or describe a workflow using Vertex/KFP terminology (`dsl.component`, `dsl.pipeline`,
dagster-to-zenml-migration
by zenml-ioMigrate Dagster assets, ops, graphs, jobs, and software-defined asset workflows to idiomatic ZenML pipelines. Handles concept mapping (asset->step output, job->pipeline, IOManager->artifact store/materializer + explicit IO steps), asset-boundary planning, code translation, scheduling, retry config, resources/config migration, and flags unsupported patterns (asset selection, partitions/backfills, sensors, declarative automation, freshness policies, observable source assets) for human review. Use this skill whenever the user mentions Dagster migration, converting Dagster assets or jobs, porting workflows from Dagster, replacing Dagster with ZenML, or asks how a Dagster concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when they paste Dagster code and ask to make it work with ZenML, or when they describe a workflow using Dagster terminology (`@asset`, `@multi_asset`, `Definitions`, `IOManager`, `ConfigurableResource`, partitions, sensors, asset checks) in a ZenML context. If the use
zenml-scoping
by zenml-ioScope and decompose ML workflow ideas into realistic ZenML pipeline architectures. Runs an in-depth interview to help users break down ambitious or over-engineered plans into well-composed multi-pipeline setups, identify what belongs in a pipeline vs. what doesn't, define cross-pipeline data flow via the Model Control Plane, and select an MVP to build first. Produces a pipeline_architecture.md specification document. Use this skill whenever a user describes a complex ML workflow, mentions multiple pipelines, talks about end-to-end ML platforms, asks how to structure their ML system in ZenML, or seems to be over-engineering their pipeline design. Also use when the user's pipeline idea sounds like it's trying to do too many things at once, or when they ask about composing pipelines together. Even if the user just says "I want to build an ML pipeline" with a long list of requirements, this skill helps scope it down before the pipeline-authoring skill takes over.
sagemaker-to-zenml-migration
by zenml-ioMigrate Amazon SageMaker Pipelines and workflow code to idiomatic ZenML pipelines. Handles concept mapping (Pipeline->@pipeline, ProcessingStep/TrainingStep->@step, PropertyFile/JsonGet->artifacts), code translation, SagemakerOrchestratorSettings mapping, scheduling, model-registration strategy, and flags unsupported or high-risk patterns (CallbackStep, LambdaStep handshake semantics, step.properties placeholders, dynamic-pipeline scheduling on SageMaker) for human review. Use this skill whenever the user mentions SageMaker migration, converting SageMaker Pipelines, porting workflow code from SageMaker, replacing SageMaker DSL authoring with ZenML, or asks how a SageMaker Pipelines concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when they paste `sagemaker.workflow.*` code and ask to make it work with ZenML, or when they describe a workflow using SageMaker terms (`ProcessingStep`, `TrainingStep`, `ConditionStep`, `PropertyFile`, `JsonGet`, `ModelStep`, `PipelineSession`) in a Ze
zenml-quick-wins
by zenml-ioImplements ZenML quick wins to enhance MLOps workflows. Investigates codebase and stack configuration, recommends high-priority improvements, and implements metadata logging, experiment tracking, alerts, scheduling, secrets management, tags, git hooks, HTML reports, Model Control Plane setup, live streaming events, and resource-pool-aware step requests. Use when: user wants to improve their ZenML setup, asks about MLOps best practices, mentions "quick wins", wants to enhance pipelines, or needs help with ZenML features like experiment tracking, alerting, scheduling, or model governance.
prefect-to-zenml-migration
by zenml-ioMigrate Prefect flows, tasks, and deployment patterns to idiomatic ZenML pipelines. Handles concept mapping (`@flow`→`@pipeline`, `@task`→`@step`, result persistence→artifacts), dynamic-execution analysis, code translation, scheduling, retries, Blocks/secrets decomposition, and flags unsupported patterns (`allow_failure()`, `return_state=True`, pause/suspend, global concurrency, task-runner semantics) for human review. Use this skill whenever the user mentions Prefect migration, converting Prefect flows, porting workflows from Prefect, replacing Prefect with ZenML, or asks how a Prefect concept maps to ZenML — even if they do not explicitly say "migrate". Also use when they paste Prefect code and ask to make it work with ZenML, or when they describe a workflow using Prefect terminology (`@flow`, `@task`, `.submit()`, `.map()`, `State`, Blocks, Deployments, work pools, Automations) in a ZenML context. If the user asks a quick conceptual question ("what is the ZenML equivalent of a Prefect Block?"), answer it d
zenml-pipeline-authoring
by zenml-ioAuthor ZenML pipelines: @step/@pipeline decorators, type hints, multi-output steps, dynamic vs static pipelines, artifact data flow, ExternalArtifact, YAML configuration, DockerSettings and ResourceSettings for remote execution, custom materializers, metadata logging, secrets management, pipeline deployments, live streaming events, and custom visualizations. Use this skill whenever asked to write a ZenML pipeline, create ZenML steps, make a pipeline work on Kubernetes/Vertex/SageMaker, add Docker settings, write a materializer, create a custom visualization, handle "works locally but fails on cloud" issues, or configure pipeline YAML files. Even if the user doesn't explicitly mention "pipeline authoring", use this skill when they ask to build an ML workflow, data pipeline, or training pipeline with ZenML.
metaflow-to-zenml-migration
by zenml-ioMigrate Metaflow flows and Outerbounds-flavored Metaflow projects to idiomatic ZenML pipelines. Handles concept mapping (FlowSpec->pipeline, @step->@step, self.* artifacts->explicit returns and inputs), code translation for Parameters, IncludeFile, Config, self.next transitions, branch/join, foreach, scheduling, retry/resource/dependency decorators, and flags unsupported or high-risk patterns (@catch, merge_artifacts, resume and checkpoint semantics, recursion, event triggers, @batch) for human review. Use this skill whenever the user mentions Metaflow migration, converting FlowSpec code, porting flows from Metaflow or Outerbounds, replacing Metaflow orchestration with ZenML, or asks how a Metaflow concept maps to ZenML -- even if they don't explicitly say "migrate". Also use when they paste FlowSpec code or describe workflows using Metaflow terminology (self.next, foreach, current, Parameter, IncludeFile, Config, @catch, @kubernetes, @batch, Runner, Deployer) in a ZenML context. If the user just asks a quick
kedro-to-zenml-migration
by zenml-ioMigrate Kedro pipelines and projects to idiomatic ZenML pipelines. Handles concept mapping (node->step, Pipeline->pipeline, Data Catalog->explicit boundary steps plus artifacts, params:->typed parameters), catalog analysis, code translation, hooks/runners/deployment mapping, and flags unsupported patterns (transcoding, dataset lifecycle hooks, namespace remapping, SharedMemoryDataset, slicing semantics) for human review. Use this skill whenever the user mentions Kedro migration, converting a Kedro project to ZenML, porting Kedro pipelines, replacing Kedro orchestration or deployment plugins with ZenML, or asks how a Kedro concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when the user pastes `catalog.yml`, `parameters.yml`, `pipeline_registry.py`, node code, hook code, or describes a workflow using Kedro terminology such as node, pipeline, Data Catalog, `params:`, namespace, modular pipeline, runner, `MemoryDataset`, or transcoding in a ZenML context. If the user just asks a quic
flyte-to-zenml-migration
by zenml-ioMigrate Flyte workflows, tasks, LaunchPlans, and Flytekit code to idiomatic ZenML pipelines. Handles concept mapping (`@task`->`@step`, `@workflow`->`@pipeline`, `map_task()`->dynamic `.map()`, `conditional()`->dynamic branching, `LaunchPlan`->schedule/config split), code translation, special-type migration (`FlyteFile`, `FlyteDirectory`, `StructuredDataset`, `FlyteSchema`), Docker/image mapping, and flags unsupported patterns (`@eager`, `ContainerTask`, reference entities, checkpointing, interruptible semantics) for human review. Use this skill whenever the user mentions Flyte migration, converting Flyte to ZenML, porting Flyte workflows, replacing Flyte with ZenML, or asks how a Flyte concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when they paste Flytekit code and ask to make it work with ZenML, or when they describe a workflow using Flyte terminology (`@dynamic`, `LaunchPlan`, `map_task`, `conditional`, `ImageSpec`, `FlyteFile`, `StructuredDataset`, `reference_task`, `refer
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.