Vercel Composition Patterns
supabase/supabase
React composition patterns that scale. An agent skill from supabase/supabase.
Facilitate a structured conversation to customize how the review molecule works -- atom loading rules, severity classification, report format, scope rules, insight capture, and health logging.
$ npx skills add techygarg/lattice --skill review-refiner -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install techygarg/lattice review-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/review-refiner .claude/skills/review-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 "review-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/review-refiner into .claude/skills/review-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-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/review-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 review-refiner -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install techygarg/lattice review-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/review-refiner .agents/skills/review-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 "review-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/review-refiner into .agents/skills/review-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-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 review-refiner -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install techygarg/lattice review-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/review-refiner .cursor/skills/review-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 "review-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/review-refiner into .cursor/skills/review-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-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/review-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 review-refiner -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install techygarg/lattice review-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/review-refiner .gemini/skills/review-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 "review-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/review-refiner into .gemini/skills/review-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-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 review-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 review-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/review-refiner .github/skills/review-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 "review-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/review-refiner into .github/skills/review-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-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 review-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 review-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/review-refiner .opencode/skills/review-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 "review-refiner" agent skill from https://github.com/techygarg/lattice/tree/main/skills/review-refiner into .opencode/skills/review-refiner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-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.
review-refinerFacilitate a structured conversation to customize how the review molecule works -- atom loading rules, severity classification, report format, scope rules, insight capture, and health logging.
Review Refiner is an agent skill from techygarg/lattice. Facilitate a structured conversation to customize how the review molecule works -- atom loading rules, severity classification, report format, scope rules, insight capture, and health logging. Produces a formal review-standards.md document that the review molecule will use as its process configuration. Use when the user says 'customize review', 'configure review', 'review preferences', 'review settings', 'change review process', or 'set up review'.
Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including assets (for example `assets/template.md`).
It sits in Development. 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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit ed226a0. 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.
Review Refiner loads about 3.2k tokens when it runs. Until then it costs about 117 tokens; SKILL.md has 1,703 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 ed226a0, republished under its MIT licence (© techygarg). 1,703 words, ~3,245 tokens.
.claude/skills/review-refiner/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub..lattice/standards/review-standards.md (or custom path from .lattice/config.yaml → paths.review_standards)mode: overlay): A slim document containing only sections that differ from the defaults. The review molecule reads its embedded 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 molecule's embedded defaults. For teams with fundamentally different review processes.paths.review_standards in .lattice/config.yaml./assets/template.md for the full document structure, default content, and interview guidance commentsThis refiner configures the review process -- how the review molecule orchestrates atom output. It does NOT configure what atoms check for.
| Belongs here (process orchestration) | Belongs in atom refiners (quality standards) |
|---|---|
| Which atoms load and when | What checks an atom runs |
| Severity level definitions | What constitutes a violation |
| Report format and grouping | Checklist items and anti-patterns |
| Delta scope rules | Layer definitions, naming rules |
| Insight capture preferences | Domain modeling rules |
| Health log format | Security check thresholds |
| Custom review dimensions | Atom-specific validation logic |
If a user asks about changing what an atom checks for, redirect them to the appropriate atom refiner (architecture-refiner, clean-code-refiner, ddd-refiner).
Before starting the interview, check whether a custom document already exists:
.lattice/config.yaml — does paths.review_standards point to a file?Look for signals that inform the conversation:
.lattice/reviews/review-log.md — what atoms have been loading? What severity patterns exist? Are there recurring findings?.lattice/learnings/operational-learnings.md — what patterns have been captured? Is the file growing dense in any category?.lattice/config.yaml for paths.architecture, paths.clean_code, paths.ddd_principles) This tells you which atoms the team cares about.Share relevant findings with the user at the start: "I looked at your review history and noticed [patterns]. I'll use that as context for our conversation."
If the project is new with no review history, proceed with defaults as the starting point.
The first decision in the conversation. Present the three options:
"How would you like to configure your review process?
The defaults cover a solid review workflow. Option 1 is recommended unless your review process needs to be 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.
secure-coding from conditional to always-loaded.Read ./assets/template.md 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 Atom Loading | §2, §3, §5, §6 | Per-atom severity overrides reference atom names; report sections map to loaded atoms; insight categories follow atoms; log atom names must match |
| §2 Severity | §3, §5, §6, §7 | Report ordering follows severity levels; capture criteria reference severity; log counts use severity names; custom dimensions need severity assignment |
| §4 Scope Rules | §1, §7 | Expanded scope may trigger more conditional atoms; custom dimensions follow scope rules |
| §7 Custom Dimensions | §2, §3 | Custom dimensions contribute findings needing severity classification and report placement |
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 7 default sections:
For each of the 7 default sections:
mode: overlaymode: 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.review_standards, use that path..lattice/standards/review-standards.md.Write the document:
.lattice/standards/ directory (and .lattice/ parent) if it does not exist.Update config:
.lattice/config.yaml does not exist, create it with:paths:
review_standards: .lattice/standards/review-standards.md.lattice/config.yaml exists but has no paths.review_standards, add the key. Preserve all existing content..lattice/config.yaml exists and already has the key, no config change needed.Confirm to user:
"Your review standards document has been written to [PATH] in [overlay|override] mode. The review molecule will now use it [on top of the defaults | instead of the defaults] when running reviews."
Before writing the final document, verify:
<!-- INTERVIEW GUIDANCE: --> comments remainmode: overlay<!-- INTERVIEW GUIDANCE: --> comments remainmode: override.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 1 other file (assets) in skills/review-refiner of techygarg/lattice.
Open the folder on GitHubat commit ed226a0
Review 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 |
|---|---|---|---|---|---|---|
| Review Refiner this skilltechygarg/lattice | 199 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Vercel Composition Patternssupabase/supabase | 111k | 58 repos | ~726 | Automated safety check: Pass | MIT | |
| Finishing a Development Branchobra/superpowers | 297k | 5 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Typescript Advanced Typesrolling-scopes/rsschool-app | 10k | 25 repos | ~4.2k | Automated safety check: Pass | MPL-2.0 | |
| PR Babysitteropeninterpreter/openinterpreter | 69k | 3 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 4 repos | ~1.1k | Automated safety check: Pass | MIT |
supabase/supabase
React composition patterns that scale. An agent skill from supabase/supabase.
obra/superpowers
Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.
rolling-scopes/rsschool-app
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications.
openinterpreter/openinterpreter
Watches an open GitHub pull request until it merges, handling review comments, diagnosing CI failures and retrying flaky checks along the way.
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
onyx-dot-app/onyx
Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments.
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 architecture principles for a repository.
techygarg/lattice
Facilitate a structured conversation to define clean code principles for a repository.
Categories
Facilitate a structured conversation to customize how the review molecule works -- atom loading rules, severity classification, report format, scope rules, insight capture, and health logging. Review Refiner is an agent skill from techygarg/lattice. Facilitate a structured conversation to customize how the review molecule works -- atom loading rules, severity classification, report format, scope rules, insight capture, and health logging.
Review Refiner fits situations like: the user says customize review; configure review; review preferences; review settings.
Run `npx skills add techygarg/lattice --skill review-refiner -a claude-code`. Or copy the skill folder (skills/review-refiner in techygarg/lattice) into .claude/skills/review-refiner in your project. Claude Code loads it when a task matches its description.
Run `npx skills add techygarg/lattice --skill review-refiner -a codex`. Or copy the skill folder (skills/review-refiner in techygarg/lattice) into .agents/skills/review-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 review-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/review-refiner, .gemini/skills/review-refiner, .github/skills/review-refiner and .opencode/skills/review-refiner in your project.
SKILL.md names no scripts, command-line tools or credentials: Review 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.
Review 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 3.2k tokens (SKILL.md is roughly 13k 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 Review Refiner: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k 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 199 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 10, 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.