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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lyndonkl
Showing 12 of 185 skills
lyndonkl

anomaly-fraud-scanner

by lyndonkl
star 121

Scans transactions for fraud and anomaly signals — duplicate charges within 48 hours, transactions more than 3 standard deviations above a merchant's historical average, first-ever transaction with a new merchant above a high-dollar threshold, and unusual geography or time. Produces severity-tagged alerts with the transaction id, evidence, and a recommended action (call bank, freeze card, dispute, monitor). Use for vigilance scans on every drop, after any large unexplained outflow, or when user mentions fraud check, suspicious charge, anomaly detection, or duplicate charge.

navigation main article SKILL.md
schedule Updated 2 months ago
lyndonkl

academic-letter-architect

by lyndonkl
star 121

Guides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.

navigation main article SKILL.md
schedule Updated 2 months ago
lyndonkl

write-review-artifact

by lyndonkl
star 121

Composes the final Technical Reviewer artifact at ops/technical-reviewer/YYYY-MM-DD-{slug}-review.md. Enforces frontmatter schema, section order (Summary → Blockers → Claims → Boundary-Break Suggestions → Glossary Alignment → Could-Not-Verify → Research Log), go/no-go decision rule, and never-modify-draft principle. Use exactly once per Technical Reviewer run as the last step. Trigger keywords: write review, technical review artifact, compose review, claim review output.

navigation main article SKILL.md
schedule Updated 2 months ago
lyndonkl

skill-creator

by lyndonkl
star 121

Transforms documents containing theoretical knowledge or frameworks (PDFs, markdown, book notes, research papers, methodology guides) into actionable, reusable Claude Code skills using systematic reading methodology. Use when user mentions "create a skill from this document", "turn this into a skill", "extract a skill from this file", or when analyzing documents with methodologies, frameworks, or processes that could be made actionable.

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

socratic-teaching-scaffolds

by lyndonkl
star 121

Guides learners to discover knowledge through strategic Socratic questioning and progressive scaffolding removal. Combines question ladders, misconception detectors, Feynman explanations, and worked-example fading to build durable understanding. Use when teaching complex concepts, correcting misconceptions, onboarding team members, mentoring problem-solving, or designing self-paced learning. Use when user mentions "teach me", "help me understand", "explain like I'm", "learning path", "guided discovery", or "Socratic method".

navigation main article SKILL.md
schedule Updated 2 months ago
lyndonkl

compute-baseline

by lyndonkl
star 121

Computes substacker's rolling 4-week baseline for open rate, click rate, views-per-send, and weekly subscriber delta using corpus/stats/ archived CSVs. Produces per-metric z-scores of the current week against the baseline and flags cold-start windows where fewer than 4 prior weeks exist. Use after ingest-substack-csv each Monday. Trigger keywords: baseline, rolling median, z-score, cold start, per-metric comparison.

navigation main article SKILL.md
schedule Updated 2 months ago
lyndonkl

concept-rediscovery-walk

by lyndonkl
star 121

Guides a learner to invent a math or ML concept themselves through a Socratic walk — a sequence of small guessable questions that ends with the learner stating the formal definition unprompted. The 3Blue1Brown signature move. Use when the learner is meeting a foundational concept (eigenvectors, gradient, attention, softmax, KL divergence) for the first time, when prior exposure produced memorization without understanding, or when the user says "explain it from scratch", "I want to really get it", "build it up for me", or "where does this come from".

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

geometric-algebraic-bridge

by lyndonkl
star 121

Presents a math or ML concept simultaneously in geometric form (picture, transformation, region, surface) and algebraic form (formula, matrix, derivation), then writes the explicit one-sentence bridge that says "these are the same thing because…". The signature 3Blue1Brown move applied to any vector/matrix concept. Use when a learner has one view but not the other ("I understand the formula but not what it means" or "I see the picture but can't write it down"), when introducing a concept that genuinely needs both views to land (eigendecomposition, SVD, dot product, attention, gradient, covariance), or when the user mentions "geometric meaning", "intuition behind", "picture for", or "why does the formula look like that".

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

high-dim-intuition-rebuild

by lyndonkl
star 121

Diagnoses where a learner's 3D geometric intuition is misleading them in a high-dimensional context (concentration of measure, Gaussian shells, distance-metric breakdown, manifold hypothesis, volume-in-corners, random-projection preservation), then surgically replaces the false picture with the correct one. Use when the user is reasoning about high-dim spaces (embeddings, latent vectors, neural net activations, large-scale data clouds, optimization landscapes) and either makes a claim that's true in 3D but false in 1000D, or expresses confusion at a high-dim phenomenon that "shouldn't" happen.

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

worked-example-walkthrough

by lyndonkl
star 121

Produces step-by-step computational walkthroughs of vector and matrix operations as a sequence of numbered "frames", showing the explicit state at each step. The text-equivalent of a 3Blue1Brown animation — each frame shows what changed and why, so the learner can re-trace the operation by hand. Use when the learner needs to *see* a computation unfold (eigenvalue computation, attention with 3 tokens, gradient descent step, SVD on a 2×2, layer norm on a 3-vector, softmax of a small input), when an explanation has been given but the learner needs to ground it in a worked example, or when introducing an operation that's intimidating in symbol form but trivial in pencil-and-paper form.

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

chef-assistant

by lyndonkl
star 121

Guides cooking through culinary principles, food science, and flavor architecture rather than rote recipe steps. Covers technique teaching (knife skills, sauces, searing, braising), food science (Maillard reaction, emulsions, brining), flavor troubleshooting (salt/acid/fat/heat balance), menu planning, ingredient substitutions, plating, and cultural cuisine exploration. Use when users mention cooking, recipes, chef, cuisine, flavor, technique, plating, food science, seasoning, or culinary questions.

navigation main article SKILL.md
schedule Updated 2 months ago
lyndonkl

mlb-closer-tracker

by lyndonkl
star 121

Tracks the closer role and bullpen pecking order across all 30 MLB teams — who owns the ninth-inning job today, who is next in line if the current closer falters (the handcuff), and who carries DFA or demotion risk. Emits a per-reliever `save_role_certainty` signal (0-100) and flags speculation-worthy handcuffs for waiver bids. Use when the user mentions "closer", "save role", "handcuff", "ninth inning", "bullpen depth", lost save, blown save, committee, or when the waiver analyst needs to decide whether to spend FAAB on a backup reliever. This league uses SV as one of its five pitcher categories, but SV is also the most volatile and most punt-worthy cat, so tracking should always be paired with a punt-the-cat fallback recommendation.

navigation main article SKILL.md
schedule Updated 2 months 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.