Install engineering discipline into any AI coding assistant. Composable skills for design, implementation, review, and team standards. Better process, not just better prompts.
git clone https://github.com/techygarg/lattice ~/.claude/skills/lattice24 items in this repository
Audit and fix all Lattice documentation, README, docs/, GitHub issue templates, and CLAUDE.md to ensure they are fully aligned with the current skill inventory. Documentation drift is the most common source of user confusion in Lattice — a skill exists in the codebase but not in the docs, or a renamed skill leaves a stale reference in the bug report template. If you've made any change to skills/ and haven't run this, run it now. Use when the user says 'align docs', 'audit docs', 'update documentation', 'skill align', 'check docs are in sync', 'audit skill inventory', 'ensure docs are aligned', 'are the docs up to date', or 'what needs updating'. Standalone — does not call other skills.
Create a new Lattice skill — atom, molecule, or refiner — following all framework conventions. Writing skill files manually almost always produces convention violations: wrong section order, missing confirmation gates, defaults.md without the right structure. This skill knows all of that and guides you through it. Use whenever adding any new atom, molecule, or refiner to Lattice, or when the user says 'create a new skill', 'add an atom', 'add a molecule', 'add a refiner', 'build X for Lattice', 'new lattice skill', or 'skill forge'. Does not validate, align docs, or deploy — those are separate skills you run after.
Deep behavioral audit of a Lattice skill — proposes 3 review personas relevant to the skill, runs independent scenario analysis from each persona's perspective, then merges only the high-confidence, practical findings into a severity-ordered gap report with proposed fixes. Structural validation (conventions, cross-references) is skill-validate's job — this skill finds gaps that would realistically surface when someone actually uses the skill: missing scenario handling, ambiguous instructions, silent failure cases, and behavioral inconsistencies. Filters out theoretical edge cases, low-likelihood speculation, and findings owned by other skills. Use after writing or significantly changing any skill, or when the user says 'review this skill', 'deep review', 'does this skill work', 'find gaps in this skill', 'stress test this skill', 'review from different angles', or 'skill review'. Standalone — does not call other skills.
Validate any Lattice SKILL.md against all tier conventions — atoms, molecules, and refiners. Catches structural errors, broken cross-references, and convention violations before they reach the repo. If you just wrote or modified a Lattice skill file and haven't run this yet, run it now — manual review consistently misses the same categories of errors this skill is specifically designed to catch. Use when the user says 'validate this skill', 'check this skill', 'does this follow conventions', 'review this skill file', 'check my SKILL.md', or 'skill validate'. Reports PASS/FAIL with specific file-and-section findings and actionable fixes. Standalone — does not call other skills.
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 — it orients. Use when the user says 'assess my codebase architecture', 'what direction should my codebase go', 'architecture compass', 'understand my architecture', 'audit architecture drift', 'architectural assessment', or 'help me understand what is wrong with my codebase'.
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 when setting up a new project, defining architecture standards, or when the user says 'setup architecture', 'define layers', 'architecture principles', 'help me define my architecture', 'hexagonal architecture', 'modular monolith', 'ports and adapters', or 'define my architecture style'.
Enforce architectural rules when generating or modifying code. Defaults to clean architecture; supports any architecture style via the architecture-refiner. Validates layer responsibilities, dependency direction, and structural constraints using the loaded architecture rules. Use when generating code, reviewing architecture, creating new files, or when the user mentions 'architecture', 'layers', 'structure', 'dependency rules', 'hexagonal architecture', 'ports and adapters', 'modular monolith', or 'onion architecture'. Also use when reviewing generated code for structural compliance.
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', 'repair this issue', or 'why is this broken'.
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 coding standards', 'code quality principles', 'coding guidelines', or 'help me define my code standards'.
Apply clean code principles when generating or modifying implementation code. Enforces function focus, naming clarity, complexity management, error handling, and self-documenting style. Use during code generation, refactoring, or when the user mentions 'clean code', 'code quality', 'refactor this', 'simplify this', 'improve this', 'make this cleaner', 'clean this up', 'tidy this', 'coding guidelines', or 'implementation quality'. This skill governs the craft of writing individual code units -- not architecture (see architecture), not security posture (see secure-coding), and not test structure (see test-quality).
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 user says 'implement', 'code this', 'build it', 'forge the code', or 'generate the code'.
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, when facts are missing or contradictory, when the user asks 'what should we do here?', 'is this a judgment call?', 'should I ask about this?', 'am I guessing here?', 'what are the tradeoffs?', or when deciding between two reasonable architectural or design options. Also composed by molecules to define how judgment calls and clarification requests are surfaced and resolved.
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 document lifecycles). Handles creating new context documents, loading existing ones, and enriching them with new decisions. Use when starting a new feature, resuming work, making technical decisions, resolving questions, or when context needs to persist across sessions. Use this skill whenever the user mentions 'load context', 'update context', 'context doc', 'decisions', 'continue where we left off', 'what did we decide', or 'capture this decision'.
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 says 'setup DDD', 'define domain rules', 'DDD principles', or 'help me define my domain patterns'.
Run a complete design workflow -- from establishing context through 5 progressive design levels to an approved blueprint. Composes context anchoring, design-first methodology, architecture, and DDD into a unified process. Handles both new features (create context doc) and resuming existing work (load context doc). Use when starting a design, planning architecture, or when the user says 'design a feature', 'blueprint', 'start designing', 'plan the architecture', or 'let's design before coding'.
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', 'let's think before coding', 'walk me through the design', or 'whiteboard this'. For simple utilities or single-component tasks, enter at Level 4 (Contracts). Do not use for quick bug patches.
Apply DDD tactical patterns when working with domain code. Enforces aggregate design, value objects over primitives, entity identity rules, and bounded context boundaries. Use when creating or modifying domain models, designing aggregates, working in the domain layer, or when the user mentions 'domain', 'aggregate', 'value object', 'entity', 'bounded context', or 'DDD'.
Facilitate a structured conversation to create a project-specific knowledge base document. Produces a knowledge-base.md that primes AI with the project's tech stack, architecture, trusted sources, and project structure. Use when the user says 'set up knowledge base', 'prime the project', 'onboard AI', 'create knowledge base', 'set up project context', or 'configure AI context'.
Load project-specific context -- tech stack, architecture overview, directory layout, trusted sources, and conventions -- so that all skills operate with awareness of what this project actually is. Use when a knowledge base document exists, or when the user asks about the project's tech stack, architecture, conventions, framework, directory layout, or says 'tell me about this project', 'what are we using?', 'what's our stack?', or 'what framework is this?'. Use the knowledge-priming-refiner to create a knowledge base document.
Facilitate a structured conversation to define language-specific idioms and patterns for a repository. Produces a language-idioms.md document consumed by multiple atoms to adapt pseudocode defaults to the project's language. Use when setting up a new project, switching languages, or when the user says 'setup language', 'define language idioms', 'configure language', 'language patterns', or 'adapt for Go/Rust/Python'.
Guided setup experience for new Lattice projects -- scans the repository, detects existing configuration, suggests refiners in priority order, and creates the .lattice/ config. Bridges the gap between installing skills and getting first value. Use when the user says 'lattice init', 'set up lattice', 'initialize lattice', 'get started with lattice', or 'configure lattice for this project'.
Manage the operational learnings lifecycle — load prior learnings to inform current work, harvest new patterns worth preserving, and keep the document tight over time. Provides a protocol for accumulating actionable patterns from practice that complement standards and defaults. Use when a workflow session completes and produced insights worth persisting, when starting a session that should benefit from prior patterns, or when the user says 'harvest learnings', 'what have we learned', 'capture this pattern', 'tighten learnings', or 'operational learnings'.
Restructure existing code safely without changing externally observable behavior. Composes context, design, architecture, code quality, and testing guardrails into a characterization-first refactoring workflow. Use when the user says 'refactor this', 'clean this up', 'untangle this module', 'move this to the right layer', 'simplify this code', or 'improve this structure'.
Facilitate a structured conversation to define requirement standards for a project — epic and feature definitions, scenario structure, AC format, priority notation, status workflow, and naming conventions. Produces a formal requirement-standards.md that the requirement-quality atom reads via config resolution, customising its embedded defaults for the team's product process. Use when setting up a new project, defining product standards, or when the user says 'set up requirement standards', 'define feature standards', 'configure requirement forge', 'define how features should be structured', or 'requirement forge refiner'.
Skills overview
What people ask about lattice
What is techygarg/lattice?
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techygarg/lattice is skills for the Claude AI ecosystem. Install engineering discipline into any AI coding assistant. Composable skills for design, implementation, review, and team standards. Better process, not just better prompts. It has 132 GitHub stars and was last updated yesterday.
How do I install lattice?
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You can install lattice by cloning the repository (https://github.com/techygarg/lattice) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is techygarg/lattice safe to use?
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techygarg/lattice has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.
Who maintains techygarg/lattice?
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techygarg/lattice is maintained by techygarg. The last recorded GitHub activity is from yesterday, with 2 open issues.
Are there alternatives to lattice?
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Yes. On ClaudeWave you can browse similar skills at /categories/skills, sorted by popularity or recent activity.
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