381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 14 skills
zkysar1

reflect-maintain

by zkysar1
star 5

Performs maintenance reflection: curates memory (reasoning-bank entries, tree nodes, pattern signatures), applies active forgetting to low-utility items, grooms aspirations, and detects stuck goals that may need unblock conversion or archival. Use whenever the loop hits the maintenance cadence, the reasoning bank exceeds healthy size, the knowledge tree accumulates low-confidence nodes, or goals have stalled without progress. Invoked via /reflect --curate-memory or /reflect --curate-aspirations.

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schedule Updated 1 month ago
zkysar1

aspirations-all-blocked

by zkysar1
star 5

Handles the all-goals-blocked state inside the aspirations loop (phases B0-B7): scans the coordination board, generates constraint-aware aspirations, runs the idle playbook, triggers evolution, kicks off research, performs reflection, and applies exponential backoff. Use whenever the goal selector returns zero executable goals AND selection_reason starts with "all_blocked" — the orchestrator invokes this automatically. Internal-only sub-skill of /aspirations; not user-invocable.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

reflect-on-self

by zkysar1
star 5

Runs deep self-model reflection: synthesizes patterns across recent outcomes, extracts strategies into the archive, updates the Level 2 self-model, and calibrates confidence scores. Use whenever the loop hits the self-reflection cadence, sq-012 fires ("does this outcome change my core purpose?"), cumulative learning warrants rewriting self.md, or /reflect dispatches with --extract-patterns / --calibration-check. Produces self-model updates, not outcome-level insights.

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schedule Updated 1 month ago
zkysar1

aspirations-complete-review

by zkysar1
star 5

Reviews aspirations approaching completion: sweeps remaining goals for outstanding work, checks motivation fulfillment, runs the maturity gate (Phase 7.4 intent satisfaction), decides archival or continuation, and creates replacement aspirations. Use whenever an aspiration reaches high completion_ratio, the zombie scan (aspirations-precheck Phase 0.5.0a) surfaces high-completion-stale-blocked aspirations, or an aspiration's last executable goal finishes.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

priority-review

by zkysar1
star 5

Shows the priority dashboard (all active aspirations ranked by aggregate goal score) and lets the user interactively reorder priorities to update the aspiration queue. Use whenever the user says "what are you working on next", "re-rank my priorities", "show me the priority dashboard", "show the queue", or invokes /priority-review directly. Closes the feedback loop between autonomous goal selection and user strategic direction. Valid in any mode including reader.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

aspirations-precheck

by zkysar1
star 5

Runs pre-selection checks at the start of every aspirations-loop iteration: completion runners, aspiration health, guardrail checks, blocker resolution re-probe, zombie-aspiration scan (Phase 0.5.0a), and recurring-goal surfacing. Use whenever the aspirations loop starts a new iteration and needs to tidy state before /aspirations-select runs. Internal sub-skill — invoked only from inside the orchestrator, never by the user.

navigation main article SKILL.md
schedule Updated 22 days ago
zkysar1

aspirations-graceful-stop

by zkysar1
star 5

Handles the graceful-stop path for the aspirations loop: recovers in-flight iteration checkpoints, completes pending verify/state-update obligations, and runs the deferred stop sequence D1-D7. Use whenever {agent}/session/stop-requested is detected at Phase -1.4 of the aspirations loop, or the loop needs to exit cleanly without dropping in-flight work. Internal handler — only /aspirations invokes it; the user-facing /stop command writes stop-requested which this handler then reads.

navigation main article SKILL.md
schedule Updated 22 days ago
zkysar1

aspirations-verify

by zkysar1
star 5

Phase 5 of the aspirations loop: verifies a just-executed goal using hypothesis outcomes, unified outcome/check evaluation, Q1/Q2/Q3 verification escalation, streak tracking, and dependent-goal unblocking. Use whenever /aspirations-execute finishes and the loop must confirm the goal actually met its verification criteria before state-update fires. Internal sub-skill — never invoke outside the aspirations loop; assumes the just-executed goal is still in working context.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

review-hypotheses

by zkysar1
star 5

Resolves open hypotheses whose horizons have elapsed, extracts lessons from each outcome, calculates per-category accuracy stats, and generates a calibration report. Use whenever the aspirations loop hits the hypothesis-review cadence, the user says "how accurate are my predictions" or "review my hypotheses", or enough hypotheses have reached their horizon to warrant a batch resolve. Modes: --resolve, --learn, --accuracy-report, --full-cycle, --category-comparison.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

reflect

by zkysar1
star 5

Orchestrates Reflexion-based learning: dispatches to /reflect-on-outcome (hypothesis ABC chains, execution patterns, batch micro-hypotheses), /reflect-on-self (pattern synthesis, Level 2 self-model, calibration), or /reflect-maintain (memory curation, active forgetting, aspiration grooming) based on --mode. Use whenever the aspirations loop hits a reflection cadence, a hypothesis resolves, the user asks to "reflect on recent work", or after major outcomes that warrant pattern extraction.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

verify-learning-staleness

by zkysar1
star 5

Scans verify-learning/SKILL.md for stale Check:/Bash: assertions whose targets (paths, phase headers, grep patterns) no longer exist in the codebase. Four lanes: L1 path references, L2 Phase/Step references inside SKILL.md targets, L3 grep-target patterns, L3b `Grep Phase X for `pattern`` variant. Skips negative assertions ("MUST NOT exist", "test ! -f", "absent") and external paths (meta/, world/). Use whenever the agent suspects verification-checklist drift — after major SKILL.md refactors, after extracting inline pseudocode into scripts, after renaming phases, OR via the recurring goal under asp-115 (g-115-219) that fires weekly. Sister to felt-sense-checkin Phase 5b (which calls the same scanner at 75-goal cadence) — this skill is the on-demand entry point.

navigation main article SKILL.md
schedule Updated 1 month ago
zkysar1

aspirations-learning-gate

by zkysar1
star 5

Enforces learning obligations inside the aspirations loop: the learning gate, retrieval gate, meta-learning signal, periodic reflection cadence, conclusion audit, and batch reflection. Use whenever a goal completes and the loop must verify that encoding, retrieval, and reflection actually happened before moving to the next goal. Catches sessions that ship commits without producing tree encodings or hypothesis resolutions. Internal sub-skill — never invoke directly.

navigation main article SKILL.md
schedule Updated 1 month ago
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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.