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Agent Skills

Agent-Skills.md is a agent skills marketplace, to find the right agent skills for you.

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flow-nexus-swarm

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

natea
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ReasoningBank with AgentDB

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

natea
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github-multi-repo

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

natea
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performance-analysis

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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github-project-management

Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning

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Pair Programming

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

natea
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github-code-review

Comprehensive GitHub code review with AI-powered swarm coordination

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swarm-advanced

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

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stream-chain

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

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sparc-methodology

SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration

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Skill Builder

Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.

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monetization-analyzer

Analyze game concepts for monetization potential, willingness-to-pay, viral mechanics, and revenue generation. Ranks concepts by total monetization score and identifies top revenue opportunities.

natea
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AgentDB Performance Optimization

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

natea
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AgentDB Vector Search

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

natea
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Hooks Automation

Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

natea
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market-analyst

Synthesize multiple sentiment analyses to identify market trends, gaps, opportunities, and predict likely hits. Cross-analyzes patterns to find underserved markets and highlight unique innovations.

natea
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reddit-sentiment-analysis

Conduct comprehensive sentiment analysis of Reddit discussions for any product, brand, company, or topic. Analyzes what people like, dislike, and wish were different with structured output summaries.

natea
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demo-builder

Automatically generate playable game demos from concept documents. Parses game designs, creates Three.js prototypes with scoring, characters, textures, and music. Transforms ideas into interactive experiences.

natea
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ReasoningBank Intelligence

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

natea
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34

flow-nexus-neural

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

natea
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34

hive-mind-advanced

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

natea
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34

github-workflow-automation

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

natea
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Verification & Quality Assurance

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

natea
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threejs-game

Three.js game development. Use for 3D web games, WebGL rendering, game mechanics, physics integration, character controllers, camera systems, lighting, animations, and interactive 3D experiences in the browser.

natea
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Swarm Orchestration

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

natea
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github-release-management

Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management

natea
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34

gemini-research-browser-use

Use Chrome DevTools Protocol to allow the AI to "ask Gemini" or "research with Gemini" directly. This uses the user's logged-in Chrome session, bypassing API limits and leveraging the web interface's reasoning capabilities.

grasseed
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google-search-browser-use

Use browser-use to perform Google searches, open results, and extract key information from live pages. Use when the user asks to "search Google", "look this up on Google", or needs current web results via a real browser session (often to avoid bot blocks).

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review-package

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aesthetic-guide

Research a UI design aesthetic and produce exhaustive, implementation-ready design guidelines for coding agents. Use when the user names an aesthetic (brutalist, glassmorphism, retro-futuristic, Swiss modernist, Apple HIG, neumorphism, minimalism, cyberpunk, Material Design, art deco, vaporwave, etc.) and wants a complete style guide with exact CSS values, color palettes, component states, animations, and typography — detailed enough for a coding agent to faithfully implement the aesthetic with zero ambiguity.

petekp
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agent-changelog

Compile an agent-optimized changelog by cross-referencing git history with plans and documentation. Use when asked to "update changelog", "compile history", "document project evolution", or proactively after major milestones, architectural changes, or when stale/deprecated information is detected that could confuse coding agents.

petekp
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agent-telemetry

Make application behavior visible to coding agents by exposing structured logs and telemetry. Use when asked to "add telemetry", "make logs accessible to agents", "add observability", "debug with logs", or when an agent needs to understand runtime behavior but has no way to query logs. Also use when debugging is difficult because there are no structured logs, when agent docs (CLAUDE.md, AGENTS.md) lack instructions for querying application logs, or when setting up logging infrastructure for a new or existing web application.

petekp
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agentic-docs

Write clear, plain-spoken code comments and documentation that lives alongside the code. Use when writing or reviewing code that needs inline documentation like file headers, function docs, architectural decisions, or explanatory comments. Works well for both human readers and AI coding assistants who see one file at a time.

petekp
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architectural-refactor

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architecture-scaffold

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autonomous-agent-readiness

Assess a codebase's readiness for autonomous agent development and provide tailored recommendations. Use when asked to evaluate how well a project supports unattended agent execution, assess development practices for agent autonomy, audit infrastructure for agent reliability, or improve a codebase for autonomous agent workflows. Triggers on requests like "assess this project for agent readiness", "how autonomous-ready is this codebase", "evaluate agent infrastructure", or "improve development practices for agents".

petekp
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blog-drafter

Interview-driven blog post drafting for technical product audiences. Use when user wants to write a blog post, article, or essay and needs help developing their thesis, structure, and initial draft. Triggers on "write a blog post", "draft an article", "help me write about X", "blog drafter", or when user has a topic they want to turn into written content. Conducts structured interviews using AskUserQuestion to extract the user's unique insights before generating drafts.

petekp
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capture-learning

Analyze recent conversation context and capture learnings to project knowledge files (for project-specific insights) or skills/commands/subagents (for cross-project patterns). Use when the user asks to "capture this learning", "update the docs with this", "remember this for next time", "document this issue", "add this to CLAUDE.md", "save this knowledge", or "update project knowledge". Also triggers after resolving build/setup issues, discovering non-obvious patterns, or completing debugging sessions with valuable insights.

petekp
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checkpoint

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codebase-study-guide

Generate a pedagogically-grounded study guide for learning an unfamiliar codebase. Use when the user wants to onboard onto a codebase, understand a project's architecture, create learning materials for a team, or asks things like \"help me learn this codebase\", \"create an onboarding guide\", \"I'm new to this project\", \"how does this system work\", \"study guide for this repo\", or \"explain this codebase to me\". Produces a structured document that builds understanding from purpose to systems to patterns, using evidence-based learning techniques (elaborative interrogation, concept mapping, threshold concepts, worked examples, progressive disclosure).

petekp
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data-sleuth

Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.

petekp
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de-slop

Remove LLM-isms and AI writing patterns from text. This skill should be used when editing prose to sound less like AI output — removing overused words, fixing structural tells, and restoring natural human voice. Triggers: \"de-slop\", \"remove AI writing\", \"humanize this\", \"sounds too AI\", \"LLM-isms\", \"AI slop\", or when reviewing text that reads like chatbot output.

petekp
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dead-code-sweep

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deep-research

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deep-work

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docs-changelog

Write changelog entries for open source documentation sites using Keep a Changelog format. Use when asked to "write a changelog", "update the changelog", "add changelog entry", "document recent changes", or after a release/set of changes that should be recorded. Reviews git commits since the last changelog entry and produces a categorized, human-readable entry.

petekp
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exhaustive-systems-analysis

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unix-macos-engineer

Expert Unix and macOS systems engineer for shell scripting, system administration, command-line tools, launchd, Homebrew, networking, and low-level system tasks. Use when the user asks about Unix commands, shell scripts, macOS system configuration, process management, or troubleshooting system issues.

petekp
petekp
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explainer-visuals

Create high-quality animated explainer visuals for essays and blog posts. Use when the user wants to visualize concepts, processes, data, or ideas with interactive web animations. Triggers on requests like "create a visual for", "animate this concept", "make an explainer", "visualize this idea", "diagram this process", "show this data", or when essay content would benefit from visual explanation. Handles abstract concepts (mental models, frameworks), technical processes (algorithms, systems), and data visualization (trends, comparisons). Outputs self-contained HTML/CSS/JS that embeds directly in web content.

petekp
petekp
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explanatory-playground

Build interactive debugging interfaces that reveal internal system behavior. Use when asked to "help me understand how this works", "show me what's happening", "visualize the state", "build a debug view", "I can't see what's going on", or any request to make opaque system behavior visible. Applies to state machines, data flow, event systems, algorithms, render cycles, animations, CSS calculations, or any mechanism with hidden internals.

petekp
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Adoption

Agent Skills are supported by leading AI development tools.

FAQ

Frequently asked questions about Agent Skills.

01

What are Agent Skills?

Agent Skills are reusable, production-ready capability packs for AI agents. Each skill lives in its own folder and is described by a SKILL.md file with metadata and instructions.

02

What does this agent-skills.md site do?

Agent Skills is a curated directory that indexes skill repositories and lets you browse, preview, and download skills in a consistent format.

03

Where are skills stored in a repo?

By default, the site scans the skills/ folder. You can also submit a URL that points directly to a specific skills folder.

04

What is required inside SKILL.md?

SKILL.md must include YAML frontmatter with at least name and description. The body contains the actual guidance and steps for the agent.

05

How can I submit a repo?

Click Submit in the header and paste a GitHub URL that points to a skills folder. We’ll parse it and add any valid skills to the directory.