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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element-interactions
by civitas-cerebrumUse this skill whenever the user mentions testing, test automation, or anything related to verifying application behavior. Triggers on general testing intent: "test the app", "test this", "lets test", "write tests", "add tests", "run tests", "e2e tests", "end-to-end tests", "browser testing", "UI testing", "functional testing", "test automation", "automate tests", "test scenario", "test case", "happy path", "smoke test", "regression test", "test coverage", "QA", "quality assurance". Also triggers on framework-specific keywords: Playwright tests, @civitas-cerebrum/element-interactions, @civitas-cerebrum/element-repository, Steps API, ElementRepository, ElementInteractions, baseFixture, ContextStore, page-repository.json, or any request to write, fix, or add to a Playwright test in this project. Also use when asked to add entries to a page-repository JSON file, use fixtures, select dropdowns, verify elements, wait for states, or perform any browser interaction through this framework. Always consult this skill b
bug-report
by civitas-cerebrumUse when a defect, issue, or unexpected behavior is found during testing and needs to be reported in a defect tracking system (Jira). Use when asked to write, create, or file a bug ticket, defect report, or issue report.
journey-mapping
by civitas-cerebrumMap user journeys through a web application before writing tests. Discovers pages, builds app context incrementally, identifies all user flows, and prioritizes them by business impact. This is a mandatory prerequisite for coverage expansion (test-composer) and the full pipeline. Invoke when: starting a full E2E suite, before coverage expansion, before any major test composing activity, or when asked to "map the app", "discover user journeys", "map user flows", or "understand the app".
test-catalogue
by civitas-cerebrumUse when asked to "produce a test catalogue", "generate a scenario report", "catalogue the suite", "client-ready catalogue", "export the scenario inventory", or any request for a stakeholder-facing list answering "what scenarios are we running, and why?". Opt-in and on-demand only: never activates during test writing, coverage expansion, repair, or debugging. Requires a sentinel-bearing `tests/e2e/docs/journey-map.md` and at least one spec file to be present.
contract-testing
by civitas-cerebrumUse whenever a backend HTTP endpoint is being consumed in a test — write, run, review, or just call. The skill owns the structured shape every API interaction should produce: status code expectations, response-schema assertion, error-envelope validation, header coverage. It auto- activates on the explicit-intent triggers (contract test, schema test, pact, OpenAPI conformance, lock API contract, verify API shape, breaking change detection, consumer-driven contract, provider verification, API compatibility, spec conformance, ensure API hasn't changed) AND on any test or planned test that calls `steps.apiGet`, `steps.apiPost`, `steps.apiPut`, `steps.apiDelete`, `steps.apiPatch`, `steps.apiHead`, `steps.verifyApiStatus`, or `steps.verifyApiHeader` — even inside a UI-flow spec. The rationale: every backend call deserves at minimum a status-code + error-envelope assertion; without those the test silently passes when the endpoint regresses. Also activates on phrases that signal a backend interaction is about to land
test-composer
by civitas-cerebrumUse this skill to compose the full test portfolio for **one** user journey — happy path, error states, edge cases, mobile, negative flows, and data-lifecycle variants — and to drive that journey to high test coverage. Triggers on requests like "write all tests for the login journey", "compose tests for journey X", or when invoked by coverage-expansion with a journey reference. Do NOT use for iterating across an entire application — that is coverage-expansion. Do NOT use for writing a single test scenario — this skill composes the journey's whole variant set.
workflow-reviewer
by civitas-cerebrumSubagent-only skill. Loaded by every workflow-reviewer-phase<N>: / workflow-reviewer-pass<N>: / workflow-reviewer-cycle<N>: dispatch. Reviews the closing handover of an onboarding phase, a coverage-expansion pass, or a journey-mapping cycle against the canonical methodology exit criteria; returns verdict approve | reject | escalate per the workflow-reviewer.schema.json contract. Owns the 3-cycle reject cap and the skip / early-stop authorisation rules. Triggers when the brief carries one of the three role prefixes, or when the orchestrator names the skill in a Skill-tool invocation.
test-repair
by civitas-cerebrumUse this skill to restore a rotted Playwright test suite to a stable, verified green state. Triggers on requests like "repair the suite", "fix my tests", "restore green", "heal the suite", "the tests are broken", "the suite rotted", "triage the failures", "diagnose the whole suite", "my suite is flaky", "the app changed and now tests fail everywhere". Also auto-escalates from `failure-diagnosis`, `test-composer`, or `bug-discovery` when a single run produces many failures (≥5 failures or ≥30% of executed tests) or when failures repeat across diagnostic attempts — batch clustering finds shared root causes faster than per-failure diagnosis at scale. Those callers explicitly invoke this skill via the Skill tool when their own escalation criteria fire (no always-load reliance). Do NOT use for a single failing test — that stays with `failure-diagnosis`. Do NOT use to find new bugs adversarially — that is `bug-discovery`. Do NOT use to write new tests — that is `test-composer`.
companion-mode
by civitas-cerebrumUse when a QA engineer needs ad-hoc functional verification of a specific task with rich evidence (per-step screenshots, video, trace, HAR, console) — not a durable suite test, not a full coverage pass, not bug-hunting. Triggers on "companion mode", "companion entry mode", "QA companion", "verify this flow with evidence", "evidence package for X", "screenshot every step", "record this scenario", "video of this flow", "evidence-backed test", "daily QA task", "manual test assistance", "help me check that <feature> still works", "show me proof that X works", "capture screenshots while you do this", "run a quick verification with proof". Use for single-task functional verification with evidence output. Do NOT use to grow a durable test suite (that is Stages 1-4 of `element-interactions`), to expand coverage iteratively (that is `coverage-expansion`), to compose a journey portfolio (that is `test-composer`), to hunt bugs adversarially (that is `bug-discovery`), to repair a rotted suite (that is `test-repair`), or
selector-development
by civitas-cerebrumUse when test authoring needs a stable selector for an element that has none — and only when the test work lives in the same project as the frontend source. Adds a single inert test attribute (data-testid or the project's detected convention) to the offending element, runs an 8-step hook-enforced guardrail pipeline (typecheck + unit + e2e + visual diff), and lets the calling skill resume. Triggers from Stage 2 inspection escalation (no stable selector found), from failure-diagnosis (fragile-selector root cause), or directly when the user says "add stable selectors to <X>" / "audit selectors across the app". Never modifies structure, class, id, aria, handlers, or text — only appends one attribute. Refuses if the workspace doesn't contain both frontend source and tests/e2e/. Two modes: JIT (one element, default) and Audit (whole-app workflow, opt-in).
bug-discovery
by civitas-cerebrumUse when asked to "find bugs", "break the app", "bug hunt", "quality audit", "edge case testing", "stress test the app", "exploratory testing", "find issues", or "bug discovery". Triggers on any request for systematic adversarial testing of a web application after an existing test suite passes. Do NOT use for writing initial tests — that is element-interactions Stages 1-4. Do NOT use for expanding coverage — one journey's variant set is test-composer; whole-app iteration is coverage-expansion. Do NOT use for evidence-first single-task verification — that is companion-mode. Use only when the goal is to actively discover bugs.
coverage-expansion
by civitas-cerebrumIteratively expand E2E test coverage across an entire mapped application. Owns priority ordering, journey-by-journey iteration, parallel dispatch for independent journeys, model selection per journey size, and map reconciliation between passes. Calls the test-composer skill per journey for compositional passes and invokes bug-discovery per journey for adversarial passes; does not compose tests itself. Runs in three modes: `breadth` (one horizontal sweep, fast); `standard` (three compositional passes + two adversarial passes + ledger dedup, journey-by-journey — the default); or `depth` (strict per-journey parallel on every pass, ~20× the dispatch cost — picked explicitly for high-fidelity audits). Triggers on "increase coverage", "expand tests", "iterative coverage", "deep coverage pass", and when invoked by the onboarding skill as its Phase 5.
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.