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By a-organvm
Build and orchestrate multi-step AI agent workflows across domains: generative art, data pipelines, backend APIs, mobile apps, knowledge systems, and documentation pipelines using a library of skills with automated execution planning and quality gates.
npx claudepluginhub a-organvm/a-i--skills --plugin document-skillsCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Audio production concepts, DSP fundamentals, mixing/mastering techniques, and DAW workflows. Bridges modular synthesis philosophy with practical audio engineering. Triggers on audio production, mixing, sound design, DSP, or music technology requests.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Craft compelling fiction and creative nonfiction with attention to structure, voice, prose style, and revision. Supports short stories, novel chapters, essays, and hybrid forms. Triggers on creative writing, fiction writing, story craft, prose style, or literary technique requests.
Create algorithmic and generative art using mathematical patterns, noise functions, particle systems, and procedural generation. Covers flow fields, L-systems, fractals, and creative coding foundations. Triggers on generative art, algorithmic art, creative coding, procedural generation, or mathematical visualization requests.
Uses power tools
Uses Bash, Write, or Edit tools
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Collection of example skills demonstrating various capabilities including skill creation, MCP building, visual design, algorithmic art, internal communications, web testing, artifact building, Slack GIFs, and theme styling
Professional skill creation with TDD workflow. Features dual-mode (fast/full), behavioral validation, and automated quality gates for 9.0/10+ scores.
Collection of agent skills demonstrating various capabilities including skill creation, MCP building, visual design, algorithmic art, internal communications, web testing, artifact building, Slack GIFs, and theme styling
Ultimate Claude Code skill creator. Design, scaffold, build, review, evolve, and publish production-grade AI agent skills following the Agent Skills open standard and 3-layer architecture.
Open collection of AI agent skills — reusable, framework-agnostic SKILL.md packages
Unity Development Toolkit - Expert agents for scripting/refactoring/optimization, script templates, and Agent Skills for Unity C# development
Substrate-agnostic methodology for guiding any structural overhaul through five gated phases — landscape discovery, taxonomy design, environment configuration, systemic ingestion, governance evolution. Each phase produces a named artifact; an integrity auditor enforces gate criteria between phases.
Encodes the autonomous-work-assignment primitive — a unit of work that is not task, series, stream, load, or cycle, but a scoped-and-parameterized assignment carrying its own context. Given a grain of sand (a dense prompt), surface its parallel dimensions, compose each as a self-contained 9-section handoff envelope, dispatch in parallel, and reconcile the returns into a composed whole. Phase 0 reads the grain autonomously (no front-gate intake). A pre-Phase-1 gate declines the protocol when the grain is not fan-out shaped. The pingpong-detector ships with annotated risk rather than stalling (recompose cap of 2). Phase 4 enforces composition mechanically (compression ratio ≤50%, minimum 1 PENDING-DECISION). Subagent routing is advisory — no guaranteed expertise delta.
A composable skill framework for AI agent orchestration -- 101 production-ready skill modules spanning creative, technical, enterprise, and governance domains, organized into a federated registry with multi-agent runtime support.
Part of ORGAN-IV: Taxis -- the orchestration and governance layer of the ORGAN system.
a-i--skills is a structured repository of 101 AI agent skills -- self-contained instruction modules that teach large language models how to perform specialized tasks in a repeatable, composable way. Each skill is a directory containing a SKILL.md file with YAML frontmatter (metadata for discovery and activation) and Markdown content (the actual instructions an agent follows).
The repository serves three distinct functions:
Skill Library -- A browsable catalog of 171 skills across 12 categories, from algorithmic art generation to security threat modeling, each with standardized metadata, optional helper scripts, reference documentation, and asset templates.
Orchestration Infrastructure -- Python tooling for skill validation, registry generation, health checking, and multi-agent bundle distribution. A built-in MCP (Model Context Protocol) server enables runtime skill discovery and planning.
Federation Specification -- A published protocol that allows third-party skill repositories to be discovered, validated, and consumed by any compatible agent, enabling a decentralized ecosystem of interoperable skill providers.
The skills themselves range from beginner-level single-file instructions to advanced multi-file modules with executable scripts, OOXML schema references, and comprehensive troubleshooting guides. Four document-processing skills (DOCX, PDF, PPTX, XLSX) demonstrate production-grade complexity -- these are the same skills that power Claude's native document creation capabilities.
| Dimension | Value |
|---|---|
| Total skills | 101 (97 example + 4 document) |
| Skill categories | 12 |
| Multi-agent runtimes supported | 4 (Claude Code, Codex, Gemini CLI, Claude API) |
| Total files | ~3,745 |
| Repository size | ~5.2 MB |
| Federation schema version | 1.1 (stable) |
| Skill spec version | Current |
AI agents are increasingly capable of executing complex, multi-step tasks, but their effectiveness depends heavily on the quality of instruction they receive. A generic prompt produces generic output. A well-structured skill -- with domain-specific vocabulary, explicit constraints, worked examples, and validation criteria -- produces expert-level output repeatedly.
The challenge is organizational: how do you manage dozens or hundreds of such skills across multiple agent runtimes, ensure they remain valid as specifications evolve, and enable external contributors to build compatible skills without centralized coordination?
This repository answers that question with three architectural decisions: