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By a-organvm
Guide any structural overhaul through five gated phases — landscape discovery, taxonomy design, environment configuration, systemic ingestion, and governance evolution — with an integrity auditor that enforces gate criteria between phases.
npx claudepluginhub a-organvm/a-i--skills --plugin pentaphase-structural-architectPhase 5 of the pentaphase structural-overhaul protocol. Codifies operational protocols, onboards the ecosystem of participants, programs behavior monitoring, and establishes an iteration cadence so the substrate evolves rather than calcifies. Use when the user invokes phase 5 of an overhaul, asks to "establish governance" or "lock in the protocols", or has completed ingestion and is ready to declare the substrate operational. Consumes phase-4-ingestion-report.md; produces phase-5-governance-charter.md, which closes the protocol.
Phase 1 of the pentaphase structural-overhaul protocol. Inventories assets, maps current flow, identifies friction, and defines value metrics for any substrate. Use when the user invokes phase 1 of an overhaul, requests a baseline audit, asks to "discover the landscape" of a system, or wants to understand current state before redesigning. Produces phase-1-landscape-report.md.
Threads the full five-phase structural-overhaul protocol — landscape discovery, taxonomy design, environment configuration, systemic ingestion, governance evolution — for any substrate the user names. Use when the user requests a structural overhaul, system redesign, or end-to-end restructuring of a documentation system, asset registry, code monorepo, knowledge base, or operational workflow; or when they explicitly invoke the pentaphase methodology. Coordinates handoffs between phase-skills and seats validation gates between phases.
Phase 3 of the pentaphase structural-overhaul protocol. Translates the taxonomy model into objective technical criteria, evaluates candidate mechanisms or frameworks, instantiates the chosen architecture, and programs validation rules. Use when the user invokes phase 3 of an overhaul, asks to "select a system" or "configure the environment", or has a taxonomy model and is ready to choose technology. Consumes phase-2-taxonomy-model.md; produces phase-3-environment-spec.md.
Phase 4 of the pentaphase structural-overhaul protocol. Purges redundancies, enriches and aligns legacy entities to the new schema, executes phased ingestion into the new environment, and audits integrity. Use when the user invokes phase 4 of an overhaul, asks to "migrate the data" or "ingest into the new system", or has a configured environment ready to accept legacy entities. Consumes phase-3-environment-spec.md; produces phase-4-ingestion-report.md.
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Ultra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
Comprehensive UI/UX design plugin for mobile (iOS, Android, React Native) and web applications with design systems, accessibility, and modern patterns
AI image generation Creative Director powered by Google Gemini Nano Banana models. Claude interprets intent, selects domain expertise, constructs optimized prompts, and orchestrates Gemini for best results.
Qiushi Skill: methodology skills for AI agents guided by seeking truth from facts, with Claude Code, Cursor, OpenClaw, Codex, OpenCode, and Hermes guidance.
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
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 159 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: