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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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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cx-alerts
by coralogixThis skill should be used when the user asks to "manage alerts", "create alert", "list alerts", "delete alert", "check alert status", "enable alert", "disable alert", "investigate firing alerts", "check which alerts are active", "find alerting rules", "set up an alert", "configure alerting", "mute an alert", "silence an alert", "see alert definitions", "check alert priority", or wants to manage Coralogix alert definitions using the cx CLI.
cx-search-dashboard
by coralogixThis skill should be used when the user asks to "find a dashboard", "search dashboards", "does a dashboard exist for X", "find widgets that query Y", "which dashboards use this field", "find a dashboard about errors", "look up dashboards by description", "search for existing monitoring dashboards", "find widgets that reference a field", or wants to discover existing Coralogix dashboards or widgets using natural-language or field-based search.
cx-platform-admin
by coralogixUse this skill when the user asks "who has access", "audit permissions", "check user roles", "list API keys", "review access controls", "rotate API keys", "create API key", "delete expired keys", "send data keys", "IP allowlist", "IP access restrictions", "check IP whitelist", "add user", "deactivate user", "manage team groups", "user permissions", "role-based access", "manage scopes", "system roles", "API key admin", "team member keys", "group membership", or wants to audit, manage, or configure access controls for a Coralogix account.
cx-olly
by coralogixThis skill should be used when the user asks to "chat with AI", "ask Olly", "ask the agent", "send message to AI", "continue a chat", "follow up on chat", "get artifact", "download artifact", "list artifacts", "retrieve generated content", "AI-generated charts", "AI analysis", "conversational observability", "natural language query", or wants to interact with the Coralogix Observability Agent (Olly) using the cx CLI.
cx-observability-setup
by coralogixUse this skill when the user asks to "set up monitoring", "configure observability", "onboard new service", "create saved view", "set up notifications", "configure webhook", "set up Slack integration", "outgoing webhook", "automation action", "webhook for alerts", "create view", "saved view", "view folder", "organize dashboards", "install integration", "configure extension", "contextual data", "connect external service", "create notification connector", "set up email alerts", "configure PagerDuty", "notification routing", "deploy extension", "test webhook", "notification preset", "test notification", "webhook actions", or wants to set up, configure, or manage the observability stack for a service or team.
cx-incident-management
by coralogixUse this skill when the user asks to "investigate incident", "triage this alert", "what's firing", "who got paged", "incident response", "check incident status", "SLO breaching", "error budget burned", "check service level", "SLI status", "who was notified", "check notification delivery", "verify alert routing", "MTTR", "incident severity", "error budget", "burn rate", "acknowledge incident", "resolve incident", "production incident", "what alerts are active", "incident timeline", "on-call triage", or wants to triage, manage, or respond to incidents using alerts, SLOs, and notifications.
cx-data-pipeline
by coralogixUse this skill when the user asks to "set up parsing", "create parsing rule", "extract fields from logs", "regex extraction", "log parsing", "enrich logs", "add context to logs", "custom enrichment table", "lookup table", "geo enrichment", "create metric from logs", "events to metrics", "convert logs to metrics", "generate metrics from events", "recording rule", "precomputed metrics", "PromQL recording", "configure data pipeline", "transform log data", "data processing rules", "rule group", "enrichment settings", "E2M definition", "labels cardinality", "bulk delete rules", "enrichment limits", "search enrichment table", or wants to configure how Coralogix processes, enriches, or transforms ingested data.
cx-dashboards
by coralogixBuild and deploy a Coralogix dashboard for a given service from its logs, spans, metrics, and service specs. Discovers telemetry via cx CLI commands, emits importable Coralogix JSON, verifies every PromQL and DataPrime query live through the `cx` CLI, and creates or updates dashboards via `cx dashboards create` and `cx dashboards replace`. Use whenever the user asks to create, build, generate, deploy, update, replace, or modify a Coralogix dashboard, monitoring dashboard, or observability dashboard for a service, app, or pipeline.
cx-cost-optimization
by coralogixUse this skill when the user asks to "check data usage", "list TCO policies", "reduce Coralogix costs", "optimize observability spend", "lower our logging bill", "data budget exceeded", "TCO policy", "retention tier", "archive storage", "ingestion costs", "frequent search vs archive", "why is our bill so high", "spending too much on logs", "data retention settings", "cost analysis", "usage breakdown", "optimize log volume", "control data ingestion", "archive cold data", "billing units", "plan consumption", "daily plan", "overage", "PAYG", "usage anomaly", "usage trend", "cx_data_usage_units", or wants to investigate, analyze, or reduce Coralogix data costs.
cx-cases
by coralogixTriage and manage Coralogix Cases with the `cx cases` CLI — e.g. acknowledge, assign, resolve, or re-prioritize a case, or inspect its event timeline or notification deliveries.
cx-telemetry-querying
by coralogixUse this skill for any question involving telemetry data: "investigate an issue", "debug a problem", "find out why something is slow", "check error rates", "analyze user behavior", "understand a production incident", "query telemetry data", "look at logs", "search logs", "find errors", "find stack traces", "filter by severity", "check traces", "examine spans", "investigate request latency", "debug service-to-service calls", "look up a trace ID", "analyze RUM data", "check frontend performance", "frontend errors", "Core Web Vitals", "JavaScript exceptions", "query metrics", "check CPU usage", "run a PromQL query", "check error rate", "look up a metric", "check memory usage", "how do I write a DataPrime query", "DataPrime syntax", or wants to answer questions using observability data from logs, metrics, traces, RUM, or APM.
cx-cli
by coralogixThis skill should be used whenever the user runs any cx command, uses the cx CLI, Coralogix CLI, or mentions cx in the context of Coralogix observability. This skill provides cross-cutting concerns like update notifications that apply to all cx commands.
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.