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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zeroize-audit
by vigoliumDetects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.
xss-browser-confirm
by vigoliumTurn a suspected Cross-Site Scripting reflection or DOM sink into proof of JavaScript execution by firing a uniquely-tagged dialog in a real headless browser via the browser_probe tool — not by string-matching the response. Covers reflected, stored, and DOM-based XSS, context-aware payload crafting (HTML body, attribute, JS string, URL/href, DOM sink), light WAF/encoding evasion when a payload reflects but doesn't execute, and persisting a finding sized by real impact. Use when a parameter's value appears in the response, when a DOM sink (innerHTML, document.write, eval, location) consumes input, when CWE-79 was flagged, or when a scanner saw reflection but couldn't confirm execution.
vigolium-scanner
by vigoliumUse when operating the vigolium CLI for web vulnerability scanning, security testing, traffic ingestion, server management, AI agent-driven scanning and code review, cloud-storage management, or writing custom JavaScript extensions. Invoke for scan commands, scan-url, scan-request, run, ingest, server, agent (query/autopilot/swarm/olium/piolium/audit/session), traffic browsing, database queries, storage uploads/downloads, module management, extension scripting, export, project management, and configuration tuning.
vigolium-audit
by vigoliumUse when the user asks to run a security audit, find vulnerabilities in a repo, "audit this codebase", check for exploitable bugs, or otherwise drive `vigolium-audit` — the autonomous multi-agent security auditor. Covers install, mode selection (lite / balanced / deep / revisit / reinvest / confirm / diff / merge / longshot), resume, and machine-readable output.
audit
by vigoliumUse when running a full security audit of an arbitrary source code repository, especially large, complex, multi-component, distributed, or non-standard architectures. Defines a 10-phase security audit methodology combining advisory intelligence, patch bypass analysis, knowledge base construction, baseline and custom SAST, spec gap analysis, deep bug hunting, false positive elimination, variant analysis, and final reporting with realistic PoC construction. Triggers on "audit this repo", "run a full security audit", "find vulnerabilities in this codebase", "check for security issues", "is this secure?", "run the security agents", or any request combining advisory regression, SAST, and manual review.
fp-check
by vigoliumSystematically verifies suspected security bugs to eliminate false positives. Produces TRUE POSITIVE or FALSE POSITIVE verdicts with documented evidence for each bug.
vuln-report
by vigoliumDraft a single-vulnerability report in GitHub advisory style from an audit finding, bug note, patch diff, PoC, or code review evidence. Use when Codex needs to turn one confirmed security issue into a clean disclosure-ready report with the fixed section set — Summary; Severity, Confidence, Vulnerability Type; Impact; Affected Component; Source to Sink Flow; Vulnerable Code; Proof of concept & Evidence; Preconditions; Remediation — with embedded code snippets, explanatory prose that points to the vulnerable code, and inline GitHub markdown links to source evidence.
core
by vigoliumCore agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth, waiting for content, running multiple browser sessions in parallel, and troubleshooting common failures. Use when the user asks to interact with a website, fill a form, click something, extract data, take a screenshot, log into a site, test a web app, or automate any browser task.
audit-auth
by vigoliumAudit authentication and session-management code for common issues — weak JWT config, session fixation, password-handling flaws, insecure cookies, broken OAuth flows, and missing auth checks on routes. Use when the user asks to review auth code or when source-aware scanning targets login/session/token handling.
command-injection-rce
by vigoliumTurn suspected OS command injection (a parameter that lands in a shell or a child process) into proof of remote code execution via an OAST callback, plus one safe demonstration of follow-on impact (read a file, list users, env dump). Use when a parameter feeds an exec/spawn/system call, when payloads with $(), `` ` ``, `;`, `|`, `&&` cause response differences, or when audit flags CWE-78 / CWE-77. Never sends destructive commands.
escalate-auth-bypass
by vigoliumTurn a suspected or confirmed authentication/authorization bypass into impact — admin access, session takeover, privilege escalation, or cross-tenant read. Use when you find a missing auth check on a route, a weak JWT verifier, a session cookie that's predictable or reusable across users, a privilege field client-controllable, or an audit finding tagged CWE-287/CWE-863/CWE-639. Walks from probe to admin-equivalent capability and persists a finding with the highest-impact action you reached.
idor-blast-radius
by vigoliumWhen you find an Insecure Direct Object Reference (a URL/body/header parameter that lets you read or write another user's or another tenant's object), discover the ID space, prove the access is unauthorized, quantify the blast radius (how many records reachable, what data class, read vs write, same-tenant vs cross-tenant), and persist a finding sized by real impact rather than by the existence of the flaw. Use when an ID parameter (numeric, UUID, hash, slug, or an indirect ref in a header/cookie) changes the response across IDs, when CWE-639/CWE-284/BOLA was flagged, or when an audit finding hints at object-level access control gaps.
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.