Lattice

Composable AI skills that teach assistants structured thinking — design-first, context-aware, and architecture-guided.

License: MIT Claude Code Cursor PRs Welcome martinfowler.com StarMapper

What is Lattice?

AI coding assistants jump straight to code, silently make design decisions, forget constraints mid-conversation, and produce output nobody reviewed against real standards. Lattice fixes this with composable skills in three tiers — atoms, molecules, refiners — that embed battle-tested engineering disciplines plus a living context layer that accumulates your project's standards, decisions, and review insights across every feature cycle.

Three principles guided Lattice's design:

  • Skills over prompts — versioned, team-owned skill files in the repository beat personal prompts on one developer's machine
  • Composability over monoliths — small single-purpose skills that combine into workflows beat one instruction document that tries to cover everything
  • Living context over static config — the .lattice/ folder grows smarter with every feature cycle rather than being configured once and forgotten

The Three Tiers

Tier Purpose
Atoms Single-principle guardrails — clean code, architecture, DDD, secure coding, test quality, design-first, and more
Molecules Multi-step workflows that compose atoms — design, implement, refactor, fix, review
Refiners Guided interviews that produce project-specific standards, customizing how atoms behave for your team

The Composability Model

See How It Works for the full skill inventory and mechanics.

The Pipeline

Skills form a delivery lifecycle: requirement-forgedesign-blueprintcode-forgereview, with refactor-safely and bug-fix covering structural and defect-driven work. requirement-forge starts the pipeline — it acts as a senior PM + BA pair to produce structured feature specs in .lattice/requirements/ that feed directly into design-blueprint. For teams with existing codebases, architecture-compass sits before the pipeline — it scans the repository, runs a structured interview, and produces an agreed architectural direction that orients the team before any code changes begin. Each stage consumes and produces artifacts in .lattice/, growing the living context layer.

Feature Lifecycle Pipeline

Getting Started

  1. Install Lattice — choose the path that fits your setup:

    Option A — Claude Code plugin (also works in Cursor — reads Claude Code skills automatically)

    /plugins marketplace add techygarg/lattice
    /plugins install lattice
    /reload-plugins
    

    Option B — Codex-compatible plugin package

    codex plugin marketplace add techygarg/lattice
    codex plugin add lattice@lattice
    codex plugin list | rg -i lattice
    

    The Codex plugin package lives in plugins/lattice/ and is registered by .agents/plugins/marketplace.json. It contains the same 27 skills flattened for Codex discovery.

    This duplication currently to support codex. In future, we may find a better approach as tools evolves.

    Option C — Clone and install locally (any AI tool)

    git clone https://github.com/techygarg/lattice.git
    cd lattice
    ./tools/install.sh /absolute/path/to/your/skills/folder
    

    Pass the skills directory for your tool: ~/.claude/skills/ for Claude Code, .cursor/skills/ for Cursor, or any tool's skills folder.

    Try it immediately. The repo includes sample/ — a realistic .NET 8 User Service spec with requirements, domain concepts, and constraints already written. Copy the sample/ folder contents into any empty directory and follow the steps below.

  2. Run /lattice-init in your AI tool's chat — scans the project, suggests refiners in priority order, creates .lattice/config.yaml. All skill commands (/lattice-init,/requirement-forge, /design-blueprint, /code-forge, etc.) are typed in the AI chat, not the terminal.

  3. Spec (optional but recommended)/requirement-forge acts as a senior PM + BA pair to define epics and feature specs before any design begins. Accepts existing PRDs, feature lists, or a verbal description. Produces .lattice/requirements/ as direct input to design-blueprint.

  4. Design/design-blueprint walks through five progressive design levels before any code is written.

  5. Implement/code-forge generates implementation from the approved blueprint, applying all quality atoms.

  6. Review/review audits the change and persists insights into .lattice/ for the next cycle.

Learn More

  • Origin Story — why Lattice exists, how five collaboration patterns became an installable framework, and the design philosophy behind it
  • How It Works — full skill inventory, composability mechanics, atoms/molecules/refiners in depth, the pipeline
  • Practical Guide — scenario-driven Q&A: getting started, customization, workflow, transformation, team usage, troubleshooting
  • Architecture Compass — the architectural thinking partner: why it exists, what to expect, and how a session works
  • Configuration Reference — every .lattice/config.yaml key documented
  • Framework Intelligence — verification passes, feedback loops, AI compliance techniques
  • Collaborative Judgment — why AI should ask on genuine judgment calls or missing/conflicting facts and how it works at runtime
  • The Article Series — the five collaboration patterns Lattice operationalizes (martinfowler.com)

Dev Skills

Helper skills for creating and maintaining Lattice itself — see dev-skills/.

Star History

StarMapper

License

MIT