GSD Skill Creator

An adaptive learning and coprocessor architecture for Claude Code, built as an extension to GSD (Get Shit Done).

npx get-shit-done-cc@latest

Table of Contents

• The Problem: Why AI-Assisted Development Breaks Down
• How GSD and Skill Creator Solve It Together
• The Coprocessor Architecture
• GSD-OS Desktop Application
• Quick Start
• Documentation
• Security
• License
• Contributing

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The Problem: Why AI-Assisted Development Breaks Down

AI coding assistants are powerful in short bursts, but they degrade on sustained, complex work. The core issues:

• Context rot -- As a session fills its context window, the AI loses track of earlier decisions, repeats mistakes, and produces lower-quality output. By the time you're deep into implementation, the assistant has forgotten why you made the architectural choices it's now contradicting.
• Amnesia between sessions -- Every new session starts from zero. The AI doesn't know what worked last time, what patterns you prefer, or what it should avoid. You re-explain the same things across every conversation.
• No workflow memory -- Recurring sequences (test, fix, commit, verify) are executed ad-hoc every time. The AI never learns that "after a test failure, you always check the fixture data first" or that "deploy means these seven steps in this order."
• Scaling complexity -- A single AI session can handle a function or a file. It cannot reliably coordinate a multi-phase feature across dozens of files without losing coherence, skipping steps, or drifting from the plan.

These aren't limitations of the models themselves. They're limitations of how work is structured around them.

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How GSD and Skill Creator Solve It Together

GSD is the workflow engine. It solves context rot and scaling complexity by structuring work into phases with atomic execution boundaries. Each phase gets a fresh context window, a detailed plan, and persistent state tracking. The AI executes one well-scoped unit of work at a time, commits atomically, and hands off cleanly to the next phase. Context never rots because it never accumulates beyond what's needed for the current task.

Skill Creator is the learning layer that extends GSD. It doesn't replace any GSD functionality -- it observes how you work within the GSD lifecycle and builds reusable knowledge from your patterns:

• Observe -- Watches tool sequences, file patterns, corrections, and phase outcomes across sessions
• Detect -- Identifies recurring workflows using n-gram extraction and DBSCAN clustering when patterns repeat 3+ times
• Suggest -- Proposes skill creation from detected patterns, always requiring explicit user confirmation
• Apply -- Loads relevant skills automatically based on context, respecting a 2-5% token budget
• Learn -- Refines skills from your corrections with bounded guardrails (minimum 3 corrections, 7-day cooldown, maximum 20% change per refinement)
• Compose -- When skills consistently co-activate (5+ times over 7+ days), composes them into purpose-built agents and multi-agent teams

The two systems working together solve a fundamentally different problem than either one alone. GSD prevents the AI from degrading during work. Skill Creator prevents the AI from forgetting between work. The result is an AI development environment that maintains quality over long projects and gets meaningfully better the more you use it.

What They Solve Together

Problem	GSD's Role	Skill Creator's Role
Context rot	Fresh context per phase, atomic execution	Pre-compiled skill activation eliminates re-explanation
Lost decisions	Persistent .planning/ state artifacts	Session observations capture decision patterns
Repeated mistakes	Plan verification against requirements	Bounded learning from corrections refines behavior
Scaling complexity	Phase decomposition with dependency graphs	Agent teams coordinate specialized roles
Workflow amnesia	Structured lifecycle (plan/execute/verify)	Pattern discovery codifies recurring sequences
Cross-session continuity	STATE.md tracks position and blockers	Warm-start briefings restore learned context

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The Coprocessor Architecture

Complex agent systems face the same coordination challenges that early computer designers solved decades ago: multiple specialized processors need to share resources, communicate efficiently, and synchronize their work without a single bottleneck controlling everything.

Skill Creator's agent architecture (v1.13) is modeled after the Amiga's custom chipset -- a system where dedicated coprocessors handled graphics, sound, and I/O in parallel while a lightweight kernel coordinated scheduling and resource allocation. This isn't an analogy for presentation purposes; it's the actual architectural pattern used to coordinate multi-agent teams.

The Chipset Model

Just as the Amiga distributed work across specialized chips rather than routing everything through the CPU, Skill Creator distributes agent responsibilities across four domain-specific chips:

Chip	Domain	Real Computer Analog
Agnus	Context management -- memory allocation, context window budgets, state tracking	Memory controller
Denise	Output generation -- code production, documentation, rendering	Graphics processor
Paula	I/O operations -- file access, API calls, external tool integration	I/O controller
Gary	Glue logic -- routing, lifecycle coordination, inter-chip communication	Bus controller

Each chip has dedicated budget channels (token budgets with guaranteed minimums), message ports (FIFO queues with reply-based ownership), and a 32-bit signal system for lightweight wake/sleep coordination -- the same primitives that real hardware uses for inter-processor communication.

Pipeline Lists: Declarative Workflow Programs

Pipeline Lists are declarative workflow programs -- sequences of WAIT, MOVE, and SKIP instructions synchronized to GSD lifecycle events. Inspired by the Amiga's Copper coprocessor (which executed display lists synced to the video beam), Pipeline Lists bring the same concept to workflow automation:

# A Pipeline List synchronized to GSD lifecycle events
- wait: phase-planned        # Block until planning completes
- move:
    target: skill
    name: test-generator
    mode: sprite              # Lightweight activation (~200 tokens)
- wait: tests-passing        # Block until tests pass
- skip:
    condition: "!exists:.planning/phases/*/SUMMARY.md"
- move:
    target: script
    name: generate-docs
    mode: offload             # Execute outside context window

Pipeline Lists pre-compile during planning and execute automatically during phase transitions. The AI doesn't decide what skills to load at runtime -- the workflow program has already determined the optimal activation sequence based on observed patterns. This eliminates the overhead of skill selection from the critical path.

The Offload Engine: Bulk Operations Outside the Context Window

The Offload engine handles deterministic operations that don't need AI reasoning -- running test suites, generating boilerplate, formatting code, computing metrics. These operations are "promoted" from skill metadata to standalone scripts and executed as child processes, freeing the context window for work that actually requires intelligence.

The Exec Kernel

A prioritized round-robin scheduler coordinates the chips, managing 18 typed message protocols for inter-team communication and per-team token budgets with burst mode for temporary overallocation. Teams at different priority levels (phase-critical at 60%, workflow at 15%, background at 10%, pattern detection at 10%) share resources without starvation.

Why This Matters

The chipset architecture means that building a complex agent system -- one with specialized roles, coordinated communication, resource budgets, and synchronized execution -- uses the same proven patterns that make high-performance computers work. You don't architect message passing from scratch. You define chips, wire up ports, write Pipeline Lists, and the kernel handles scheduling. The system learns which Pipeline Lists work well from execution feedback, refining activation sequences over time.

Skills, agents, and teams generated by Skill Creator follow the official Claude Code and Agent Skills specifications. They work natively in Claude Code and export to OpenAI Codex CLI, Cursor, GitHub Copilot, and Gemini CLI.

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GSD-OS Desktop Application

GSD-OS is a native desktop application that wraps the entire skill-creator ecosystem in an Amiga Workbench-inspired environment. Built with Tauri v2 (Rust backend + webview frontend), it provides a WebGL 8-bit graphics engine with CRT post-processing, an embedded terminal with native PTY, and a live planning dashboard -- all running locally with no cloud dependencies.

Features

• WebGL CRT shader engine -- Scanlines, barrel distortion, phosphor glow, chromatic aberration, and vignette with per-effect intensity controls
• 32-color indexed palette -- Five retro-computing presets (Amiga 1.3/2.0/3.1, C64, custom) with OKLCH-based palette generation and copper list raster effects
• Native PTY terminal -- Rust-backed pseudo-terminal with xterm.js emulator, watermark-based flow control, and tmux session binding
• Window manager -- Amiga-style depth cycling, drag/resize, custom chrome with frameless Tauri window
• Desktop shell -- Taskbar with process indicators, pixel-art icons, system menu, keyboard shortcuts (Alt+Tab, F10, Ctrl+Q)
• Live dashboard -- All 6 planning doc pages render inside GSD-OS windows with file-watcher-driven refresh
• First-boot calibration -- Three-screen wizard for color picking, CRT tuning, and theme selection
• Boot sequence -- Amiga chipset initialization animation (Agnus, Denise, Paula, Gary), skippable
• Accessibility mode -- Auto-activates on prefers-reduced-motion or prefers-contrast, disabling CRT effects and applying a high-contrast palette

Prerequisites

Node.js 18+ and Rust (via rustup) are required.

Linux -- Install Tauri system dependencies:

# Debian/Ubuntu
sudo apt install libwebkit2gtk-4.1-dev build-essential curl wget file \
  libxdo-dev libssl-dev libayatana-appindicator3-dev librsvg2-dev

# Fedora
sudo dnf install webkit2gtk4.1-devel openssl-devel curl wget file \
  libxdo-devel libappindicator-gtk3-devel librsvg2-devel

# Arch
sudo pacman -S webkit2gtk-4.1 base-devel curl wget file openssl \
  libxdo libappindicator-gtk3 librsvg

macOS -- Install Xcode Command Line Tools:

xcode-select --install

See the Tauri v2 prerequisites guide for the full list.

Install

# Clone the repository
git clone <repository-url>
cd gsd-skill-creator

# Install root dependencies (skill-creator library)
npm install

# Install desktop frontend dependencies
cd desktop && npm install && cd ..

Run (Development)

# Launch GSD-OS in development mode (hot-reload for both Rust and webview)
npm run desktop:dev

This starts the Vite dev server on port 1420 and opens the Tauri window. Changes to desktop/src/ hot-reload instantly; changes to src-tauri/src/ trigger a Rust recompile.

Build (Production)

# Build a release binary
npm run desktop:build

Produces platform-specific packages in src-tauri/target/release/bundle/:

• Linux: .deb and .AppImage
• macOS: .dmg

Test

# Run desktop test suite
npm run desktop:test

# Run skill-creator library tests (21,002 tests)
npm test

Technology Stack

Component	Technology	Version
Desktop framework	Tauri	v2.10.x
Frontend bundler	Vite	v6.x
Terminal emulator	xterm.js	v5.5.x
PTY management	portable-pty	0.9.0
File watching	notify	8.2.0
Color science	culori	v4.0
Schema validation	Zod	v4.x

Architecture

src/           TypeScript library (skill-creator CLI, dashboard, AMIGA, AGC, brainstorm)
src-tauri/     Rust backend (PTY, file watcher, tmux, Claude sessions, MCP host, IPC)
desktop/       Vite webview frontend (WebGL engine, desktop shell, terminal)
infra/         Bash infrastructure (PXE, VM provisioning, Minecraft, runbooks)

Strict module boundaries: src/ never imports @tauri-apps/api; desktop/ never imports Node.js modules. All communication between Rust and the webview goes through Tauri IPC commands, events, and channels. infra/ is self-contained bash scripts with no TypeScript dependencies.

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Quick Start

Skill Creator CLI

# Clone and install
git clone <repository-url>
cd gsd-skill-creator
npm install && npm run build

# Link globally (optional)
npm link

# Verify
skill-creator help

# Create your first skill
skill-creator create

# See what patterns have been detected
skill-creator suggest

# Check active skills and token budget
skill-creator status

See INSTALL.md for detailed installation instructions.

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Documentation

All documentation lives in docs/.

Document	Description
Getting Started	Installation, quickstart, and tutorials
Features	Full capability table and version history
Core Concepts	Skills, scopes, observations, and agents
How It Works	The 6-step observe-detect-suggest-apply-learn-compose loop
CLI Reference	Complete command documentation
API Reference	Programmatic usage for library consumers
Skill Format	Frontmatter fields, descriptions, official vs extension
Official Format	Claude Code official skill/agent specification
Token Budget	Budget management and priority tiers
Bounded Learning	Refinement guardrails and drift tracking
Agent Generation	Auto-composed agents from skill clusters
Agent Teams	Multi-agent coordination and topologies
Pattern Discovery	Session log scanning and DBSCAN clustering
GSD Orchestrator	Intent classification and lifecycle routing
Workflows & Roles	Skill workflows, roles, bundles, and events
Configuration	Thresholds, retention, and cluster settings
File Structure	Project and source code layout
Development	Building, testing, and contributing
Requirements	All shipped requirements across 57 milestones
GSD Teams Guide	Teams vs subagents for GSD workflows
Comparison	Skills vs Agents vs Teams
Release History	Version index linking to per-release notes
Troubleshooting	Common issues and solutions
Examples	34 ready-to-use skills, agents, and teams

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Security

See SECURITY.md for vulnerability reporting, threat model, and security boundaries.

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Project Stats

57 milestones shipped (v1.0-v1.49.10 + v1.8.1 patch) | 495 phases | 1,174 plans | ~564K LOC TypeScript, Rust, GLSL, Bash & Python | 21,002 tests

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License

MIT

Contributing

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Submit a pull request

All contributions should include tests and pass the existing test suite.