--- title: "lattice" description: "Composable AI skills framework covering the full software delivery lifecycle, design, architecture, implementation, testing, and review." canonical: https://agentpluginsdirectory.com/plugins/lattice last-updated: 2026-10-21 --- # lattice Composable AI skills framework covering the full software delivery lifecycle, design, architecture, implementation, testing, and review. - Slug: lattice - Publisher: Rahul Garg - Repository: https://github.com/techygarg/lattice - Manifest: plugin.json - Version: 2.1.0 - License: MIT - Category (editorial): code-review - Skills: 27 (architecture-compass, architecture-refiner, architecture, bug-fix, clean-code-refiner, clean-code, code-forge, collaborative-judgment, context-anchoring, ddd-refiner, design-blueprint, design-first, domain-driven-design, knowledge-priming-refiner, knowledge-priming, language-idioms-refiner, lattice-init, learning-harvest, refactor-safely, refiners-update, requirement-forge-refiner, requirement-forge, requirement-quality, review-refiner, review, secure-coding, test-quality) - MCP servers: 0 - Stars: 207 - Repository created: 2026-03-02 - Repository last pushed: 2026-10-10 - Publisher type: User - Listing: https://agentpluginsdirectory.com/plugins/lattice - Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json ## What lattice does, in the publisher's words Composable AI skills that teach assistants structured thinking, design-first, context-aware, and architecture-guided. 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 From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/techygarg/lattice/HEAD/README.md ## Skills - architecture-compass: Architectural thinking partner for an existing repository, scans the codebase, conducts a structured interview, agrees on current architectural state and recommended direction, and produces a shareable insights document. Scoped to one repository, module, or folder. Does not execute transformation… - architecture-refiner: Facilitate a structured conversation to define architecture principles for a repository. Supports multiple architecture styles: clean architecture (default), hexagonal / ports & adapters, modular monolith, or custom. Produces a formal architecture document that the corresponding atom will use. Use… - architecture: Enforce architectural rules when generating or modifying code, and validate proposed designs before approval (design mode). Defaults to clean architecture; supports any architecture style via the architecture-refiner. Validates layer responsibilities, dependency direction, and structural constraint… - bug-fix: Investigate, reproduce, and safely fix a bug with regression protection. Composes context, diagnosis, architecture, code quality, and testing guardrails into a reproduce-first repair workflow. Use when the user says 'fix this bug', 'debug this', 'investigate this failure', 'patch this regression',… - clean-code-refiner: Facilitate a structured conversation to define clean code principles for a repository. Produces a formal clean-code.md document that the clean-code atom will use as its override. Use when setting up coding standards, defining code quality rules, or when the user says 'setup clean code', 'define cod… - clean-code: Apply clean code principles when generating or modifying implementation code. Enforces function focus, naming clarity, complexity management, error handling, and self-documenting style. Use when the user mentions 'clean code', 'code quality', 'coding guidelines', or 'implementation quality'. Loaded… - code-forge: Generate implementation code from an approved design blueprint or verbal requirements. Composes context anchoring, architecture, clean code, DDD, security, and test quality into an inside-out implementation workflow. Use when moving from design to code, implementing approved contracts, or when the… - collaborative-judgment: Protocol for handling ambiguous decisions and missing/conflicting knowledge during code generation, design, and review. Ensures AI surfaces genuine judgment calls with structured options and stops on hallucination risk instead of silently assuming. Use when a decision has multiple valid approaches,… - context-anchoring: Manage per-feature living documents that capture decisions, constraints, and reasoning across AI sessions during active development. Scoped to feature-level work, design, implementation, bugfix, refactor, not for codebase-wide assessments or product-wide specifications (those define their own doc… - ddd-refiner: Facilitate a structured conversation to define DDD guardrails for domain design within a repository. Produces a formal ddd-principles.md document that the domain-driven-design atom will use as its override. Use when setting up domain design principles, defining aggregate rules, or when the user say… - design-blueprint: Run a complete design workflow: from establishing context through four progressive design levels (Capabilities, Components, Interactions, Contracts) to an approved blueprint. Composes knowledge-priming, context-anchoring, learning-harvest, collaborative-judgment, design-first, architecture, and d… - design-first: Guide structured design thinking through 5 progressive levels before any code is written. Levels: Capabilities, Components, Interactions, Contracts, Implementation. Use when building new features, refactoring significant code, designing modules, or when the user says 'design this', 'architect this'… - domain-driven-design - knowledge-priming-refiner - knowledge-priming - language-idioms-refiner - lattice-init - learning-harvest - refactor-safely - refiners-update - requirement-forge-refiner - requirement-forge - requirement-quality - review-refiner - review - secure-coding - test-quality Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.