Lattice 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…
Install engineering discipline into any AI coding assistant. Composable skills for design, implementation, review, and team standards. Better process, not just better prompts.
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
Tier
Purpose
Atoms
Single-principle guardrails — clean code, architecture, DDD, secure coding, test quality, design-first, and more
Guided interviews that produce project-specific standards, customizing how atoms behave for your team
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.
Getting Started
Install Lattice — choose the path that fits your setup:
Option A — Claude Code plugin (also works in Cursor — reads Claude Code skills automatically)
The Codex plugin manifest lives in .codex-plugin/ and is registered by .agents/plugins/marketplace.json. Like every host plugin here, it’s a thin manifest only — it points at the same shared, flat skills/ folder every other host uses, plus the verification runner script (scripts/run-verification.sh) — Codex has no subagent concept, so verification always runs the script directly rather than via a subagent. Grok (.grok-plugin/) and Kimi (.kimi-plugin/) follow the same pattern.
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.
Option D — Agent Plugins 1.0-conformant clients
A root plugin.json conforming to the open, vendor-neutral Agent Plugins standard ships alongside the host-specific manifests above — any conformant client auto-discovers skills straight from the skills/ folder with zero extra install steps. Shipped in Codex CLI, Cursor, VS Code / GitHub Copilot, and Kiro as of this writing.
See docs/plugins.md for the full per-host status table and how to add a new host.
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.
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.
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.
Design — /design-blueprint walks through five progressive design levels before any code is written.
Implement — /code-forge generates implementation from the approved blueprint, applying all quality atoms.
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
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