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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sysdig-remediate
by sysdigRemediate one specific vulnerable container image. Fetches Critical/High CVEs from Sysdig, resolves a safe fix version via chain analysis, and opens a PR/MR (GitHub/GitLab) or emits a local patch. Triggers: "fix the nginx image", "patch CVE-2024-1234 in api-server", "remediate quay.io/org/app:tag", "/sysdig-remediate <image>". Not for: discovery, prioritization, or ticket creation — use /sysdig-investigate.
sysdig-posture
by sysdigAuthor Sysdig Secure Posture custom controls (Rego) and custom policies, and emit Terraform via the Sysdig provider. Use when the user wants to "write a posture rule," "create a custom CSPM control," "fail my policy when an S3 bucket is unencrypted," or "group these CIS controls into a custom policy." Never writes to Sysdig directly — all writes go through Terraform on user approval. Not for: zone management, built-in Sysdig controls, runtime threat detection, vulnerable-image triage or remediation, or onboarding cloud accounts.
sysdig-investigate
by sysdigInvestigate vulnerable images in a Sysdig-monitored environment. Fetches and ranks images by a chosen risk metric (finding_count, exposure_time_weighted, exposure_time_avg, sla_compliance, or actually_exploitable_findings), builds a remediation plan, optionally creates a tracking ticket (Jira / Linear / GitHub Projects) using Sysdig-side signals to determine the assignee, and hands off to /sysdig-remediate. Triggers on: "investigate", "what should I fix", "show me vulnerable images", "prioritize vulnerabilities", "/sysdig-investigate". Not for opening PRs, applying code fixes, or generating Dockerfile patches — use /sysdig-remediate for that.
sysdig-onboarding
by sysdigInteractive onboarding assistant for Sysdig Secure. Guides users through connecting AWS cloud accounts and Kubernetes clusters to Sysdig. Presents security capabilities in plain language instead of jargon. Supports guided (interview) and autonomous (all-at-once) modes. Generates Terraform configurations for cloud accounts and Helm values for Kubernetes, validates prerequisites, deploys, and verifies connectivity.
sysdig-runtime-investigate
by sysdigUse this skill when investigating a runtime threat detected by Sysdig end-to-end. Surfaces the highest-priority threat, scores vulnerability vs runtime correlations on a 1-5 confidence scale, deep-dives into network blast radius or suspicious-binary VirusTotal lookups depending on the event class, and hands the case off to Jira or PagerDuty. Triggers on: "investigate runtime threat", "what is this Falco alert", "triage this SOC alert", "analyze runtime incident". Not for vulnerability prioritization (use `sysdig-investigate`) or remediation (use `sysdig-remediate`).
sysdig-sysql
by sysdigSysQL query language reference for Sysdig Secure. Use when writing, debugging, or explaining SysQL graph queries against the Sysdig security datastore. Triggers on: SysQL queries, Sysdig inventory/vulnerability/posture/identity/runtime queries, "write a SysQL query", "query sysdig for", "find vulnerable images", "show me workloads", or any task involving MATCH/RETURN syntax against Sysdig entities. Also use when exploring the SysQL schema (entities, fields, relationships).
sysdig-runtime-remediate
by sysdigClose the runtime loop on a Sysdig-detected threat: turn the investigation context into proposed response actions, analyse the blast radius on the affected workload, and execute (or file) the actions the user approves — one at a time, with explicit confirmation. Triggers: "remediate this runtime threat", "respond to event <id>", "act on this incident", "isolate / kill / pause that container", "/sysdig-runtime-remediate". Not for vulnerability fixes (use sysdig-remediate) or threat investigation itself (use sysdig-runtime-investigate).
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.