Vercel Composition Patterns
supabase/supabase
React composition patterns that scale. An agent skill from supabase/supabase.
Facilitate a structured conversation to define architecture principles for a repository.
$ npx skills add techygarg/lattice --skill architecture-refiner -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install techygarg/lattice architecture-refiner --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/techygarg/lattice.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/architecture-refiner .claude/skills/architecture-refiner && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "architecture-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/architecture-refiner into .claude/skills/architecture-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "architecture-refiner", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/techygarg/lattice/tree/main/skills/architecture-refinerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add techygarg/lattice --skill architecture-refiner -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install techygarg/lattice architecture-refiner --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techygarg/lattice.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/architecture-refiner .agents/skills/architecture-refiner && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "architecture-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/architecture-refiner into .agents/skills/architecture-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "architecture-refiner", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add techygarg/lattice --skill architecture-refiner -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install techygarg/lattice architecture-refiner --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techygarg/lattice.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/architecture-refiner .cursor/skills/architecture-refiner && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "architecture-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/architecture-refiner into .cursor/skills/architecture-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "architecture-refiner", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/techygarg/lattice.git --path skills/architecture-refiner--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add techygarg/lattice --skill architecture-refiner -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install techygarg/lattice architecture-refiner --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techygarg/lattice.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/architecture-refiner .gemini/skills/architecture-refiner && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "architecture-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/architecture-refiner into .gemini/skills/architecture-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "architecture-refiner", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install techygarg/lattice architecture-refinerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add techygarg/lattice --skill architecture-refiner -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/techygarg/lattice.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/architecture-refiner .github/skills/architecture-refiner && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "architecture-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/architecture-refiner into .github/skills/architecture-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "architecture-refiner", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add techygarg/lattice --skill architecture-refiner -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install techygarg/lattice architecture-refiner --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/techygarg/lattice.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/architecture-refiner .opencode/skills/architecture-refiner && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "architecture-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/architecture-refiner into .opencode/skills/architecture-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "architecture-refiner", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
architecture-refinerFacilitate a structured conversation to define architecture principles for a repository.
Architecture Refiner is an agent skill from techygarg/lattice. 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'…
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including assets (for example `assets/template-clean-arch.md` and `assets/template-generic.md`).
It sits in Development, covering Design patterns. The repository describes itself as: Install engineering discipline into any AI coding assistant. Composable skills for design, implementation, review, and team standards. Better process, not just better prompts. The licence is MIT.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 4d6c35f. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Architecture Refiner loads about 3.7k tokens when it runs. Until then it costs about 152 tokens; SKILL.md has 1,867 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from techygarg/lattice at commit 4d6c35f, republished under its MIT licence (© techygarg). 1,867 words, ~3,704 tokens.
.claude/skills/architecture-refiner/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Before anything else, ask the user which architecture style their team uses:
"What architecture style does your team use?
Branching:
./assets/template-clean-arch.md. Output: .lattice/standards/architecture.md. Config key: paths.architecture. No architecture_mode key needed (defaults to clean)../assets/template-generic.md. Output: .lattice/standards/architecture.md. Config key: paths.architecture. Additionally, set architecture_mode: custom in .lattice/config.yaml.The rest of this document describes the clean architecture flow (Option 1). For the generic flow (Options 2–4), read ./assets/template-generic.md and follow its <!-- INTERVIEW GUIDANCE: --> comments. The facilitation approach, conversation style, output assembly, and document quality checks below apply to both flows — substitute the appropriate template, output path, and config key.
For clean architecture (Option 1):
.lattice/standards/architecture.md (or custom path from .lattice/config.yaml → paths.architecture)mode: overlay): A slim document containing only sections that differ from the defaults. The architecture atom reads its embedded clean-architecture defaults first, then applies this document's sections on top. This is the expected common case.mode: override): A comprehensive standalone document that fully replaces the atom's embedded defaults. For teams that want to define clean architecture from scratch.paths.architecture in .lattice/config.yaml./assets/template-clean-arch.md for the full document structure, default content, and interview guidance commentsFor other styles (Options 2–4):
.lattice/standards/architecture.md (or custom path from .lattice/config.yaml → paths.architecture)override — there are no embedded defaults to overlay onto for non-clean-architecture stylespaths.architecture in .lattice/config.yamlarchitecture_mode: custom in .lattice/config.yaml./assets/template-generic.md for the document structure and interview guidance commentsBefore starting the interview, check whether a custom document already exists:
.lattice/config.yaml — check paths.architecture.Look for signals that inform the conversation:
src/ (or equivalent) already have layers? What are they named?Share relevant findings with the user at the start: "I noticed your project already has [X structure]. I'll use that as context."
If the project is new with no code, proceed with pure defaults as the starting point.
The first decision in the conversation. Present the three options:
"How would you like to define your architecture principles?
The defaults cover standard clean architecture well. Option 1 is recommended unless your architecture is fundamentally different."
Map the choice:
mode: overlaymode: overrideThis should be fast. Many sections will be "keep as-is."
This is thorough. Every section gets attention and appears in the output.
Read ./assets/template-clean-arch.md (for clean architecture) or ./assets/template-generic.md (for other styles) and follow the <!-- INTERVIEW GUIDANCE: --> comments for each section. Those comments contain the specific questions to ask, probing questions, and what is customizable vs fixed.
Decisions in early sections affect later sections. When a user changes an early section, flag the dependent sections:
| Decision in | Affects | How |
|---|---|---|
| §1 — Layer names | All sections | Names must be consistent everywhere |
| §1 — Extra layers | §2 (diagram), §3 (per-layer rules) | New layers need dependency placement and rules |
| §3.2 — Service pattern (unified vs CQRS) | §4.1, §4.2 | CQRS uses separate handlers instead of unified service |
| §3.4 — Provider pattern (yes/no) | §4.2, §4.3, §6 | No Provider → reads go through Repository; comparison table and checklist change |
When a dependency is triggered, inform the user: "Since you changed [X], we should also review [Y] — it's affected by that decision."
For each of the 6 default sections:
For each of the 6 default sections:
mode: overlayclean-architecture-defaults.md exactly (the atom matches sections by heading)mode: overrideStrip all <!-- INTERVIEW GUIDANCE: --> comments from the output. The final document is a clean specification.
Determine output path:
.lattice/config.yaml exists and has paths.architecture, use that path..lattice/standards/architecture.md.This is the same for all styles — both clean architecture customizations and other styles write to paths.architecture.
Write the document:
.lattice/standards/ directory (and .lattice/ parent) if it does not exist.Update config:
For clean architecture (Option 1):
.lattice/config.yaml does not exist, create it with:paths:
architecture: .lattice/standards/architecture.md.lattice/config.yaml exists but has no paths.architecture, add the key. Preserve all existing content..lattice/config.yaml exists and already has the key, no config change needed.For other styles (Options 2–4):
.lattice/config.yaml does not exist, create it with:paths:
architecture: .lattice/standards/architecture.md
architecture_mode: custom.lattice/config.yaml exists, add or update:paths.architecture pointing to the output patharchitecture_mode: customConfirm to user:
For clean architecture:
"Your architecture document has been written to [PATH] in [overlay|override] mode. The architecture atom will now use it [on top of the clean-architecture defaults | instead of the clean-architecture defaults]."
For other styles:
"Your architecture document has been written to [PATH] with architecture_mode: custom. The architecture atom will use it as your project's sole architecture standard."
Before writing the final document, verify:
defaults.md exactly (for section matching by the atom)<!-- INTERVIEW GUIDANCE: --> comments remainmode: overlay<!-- INTERVIEW GUIDANCE: --> comments remainmode: overridemode: override in frontmatter<!-- INTERVIEW GUIDANCE: --> comments remainarchitecture_mode: custom set.lattice/config.yaml) is correctly updated© techygarg, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (assets) in skills/architecture-refiner of techygarg/lattice.
Open the folder on GitHubat commit 4d6c35f
Architecture Refiner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Architecture Refiner this skilltechygarg/lattice | 198 | — | ~3.7k | Automated safety check: Pass | MIT | |
| Vercel Composition Patternssupabase/supabase | 111k | 59 repos | ~726 | Automated safety check: Pass | MIT | |
| Swiftui View RefactorDimillian/Skills | 4k | 5 repos | ~2k | Automated safety check: Pass | MIT | |
| RTK Rust Design Patternsrtk-ai/rtk | 83k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Effect Client WrapperUsefulSoftwareCo/executor | 4.1k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Architecture PatternsKartikLabhshetwar/better-shot | 2.4k | 2 repos | ~1.4k | Automated safety check: Pass | Custom licence |
supabase/supabase
React composition patterns that scale. An agent skill from supabase/supabase.
Dimillian/Skills
Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage.
rtk-ai/rtk
Describes seven Rust design patterns for the RTK CLI filter modules, with when to use each, RTK examples, and notes on when a pattern is overkill.
UsefulSoftwareCo/executor
Pattern for wrapping third-party SDK clients (Stripe, Resend, AWS, etc.) with Effect.
KartikLabhshetwar/better-shot
Deep dive into software architecture for macOS. An agent skill from KartikLabhshetwar/better-shot.
GitTools/GitVersion
Gives repository-specific .NET guidance for GitVersion: build and test commands, central package management, project layout and coding conventions.
techygarg/lattice
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…
techygarg/lattice
Guided setup and upgrade-check experience for Lattice projects -- scans the repository, detects existing configuration and outdated conventions, suggests refiners and available upgrades in priority…
techygarg/lattice
Audit and fix all Lattice documentation, README, docs/, PROJECT.md, GitHub issue templates, and CLAUDE.md to ensure they are fully aligned with the current skill inventory.
techygarg/lattice
Validate any Lattice SKILL.md against all tier conventions — atoms, molecules, and refiners.
techygarg/lattice
Facilitate a structured conversation to define clean code principles for a repository.
techygarg/lattice
Manage per-feature living documents that capture decisions, constraints, and reasoning across AI sessions during active development.
Categories
Facilitate a structured conversation to define architecture principles for a repository. Architecture Refiner is an agent skill from techygarg/lattice. Facilitate a structured conversation to define architecture principles for a repository.
Architecture Refiner fits situations like: setting up a new project; defining architecture standards; the user says setup architecture; architecture principles.
Run `npx skills add techygarg/lattice --skill architecture-refiner -a claude-code`. Or copy the skill folder (skills/architecture-refiner in techygarg/lattice) into .claude/skills/architecture-refiner in your project. Claude Code loads it when a task matches its description.
Run `npx skills add techygarg/lattice --skill architecture-refiner -a codex`. Or copy the skill folder (skills/architecture-refiner in techygarg/lattice) into .agents/skills/architecture-refiner in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add techygarg/lattice --skill architecture-refiner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/architecture-refiner, .gemini/skills/architecture-refiner, .github/skills/architecture-refiner and .opencode/skills/architecture-refiner in your project.
SKILL.md names no scripts, command-line tools or credentials: Architecture Refiner is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Architecture Refiner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Architecture Refiner: Vercel Composition Patterns (supabase/supabase, 111k stars), Swiftui View Refactor (Dimillian/Skills, 4k stars), RTK Rust Design Patterns (rtk-ai/rtk, 83k stars) and Effect Client Wrapper (UsefulSoftwareCo/executor, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
techygarg (a GitHub user) maintains it in techygarg/lattice, which has 198 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 6, 2026.
Source: techygarg/lattice on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.