Lattice

<p align="center"> <img src="docs/image/logo.png" alt="lattice" width="500" height="500"> </p>

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

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

The Composability Model

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

The Pipeline

Skills form a delivery lifecycle: requirement-forge → design-blueprint → code-forge → review, 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 26 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.

1. 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.

1. 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.

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

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

1. 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

<a href="https://www.star-history.com/?repos=techygarg%2Flattice&type=date&legend=top-left"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=techygarg/lattice&type=date&theme=dark&legend=top-left" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=techygarg/lattice&type=date&legend=top-left" /> <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=techygarg/lattice&type=date&legend=top-left" /> </picture> </a>

StarMapper

<a href="https://starmapper.bruniaux.com/techygarg/lattice"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://starmapper.bruniaux.com/api/map-image/techygarg/lattice?theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://starmapper.bruniaux.com/api/map-image/techygarg/lattice?theme=light" /> <img alt="StarMapper" src="https://starmapper.bruniaux.com/api/map-image/techygarg/lattice" /> </picture> </a>

License

MIT