shadcn/improve

▲ 333 stars today★ 10,353⑂ 427

Use your most capable model to audit your codebase and write plans for cheaper models to execute.

About shadcn/improve

shadcn/improve is an open-source project on GitHub, mainly written in several languages. Use your most capable model to audit your codebase and write plans for cheaper models to execute. It currently holds 10,353 stars and 427 forks with 17 open issues, and was last pushed on 2026-09-12 (repository created 2026-06-10).

Project Overview

Git Homed tracks it on the Today's Trending board, currently at rank #21 with 333 new stars today.

GitHub Repository Details

Repository shadcn/improve · default branch main · size 17 KB · watchers 29 · source: GitHub REST API and repository README

README

improve

An agent skill that audits any codebase and writes implementation plans for other agents to execute.

The idea: use your most capable model for the part where intelligence compounds — understanding the codebase, judging what's worth doing, writing the spec — and hand execution to cheaper models. The skill never implements anything itself. The plan is the product.

you          →  /improve                    (expensive model, advises)
plans/       →  001-fix-n-plus-one.md       (self-contained specs)
other agent  →  implements, tests, ships    (cheap model, executes)

Install

npx skills add shadcn/improve

Works in any agent that supports Agent Skills format. The plans it writes are plain markdown, so any agent (or human) can pick them up.

Usage

/improve                        full audit → prioritized findings → plans
/improve quick                  cheap pass: hotspots, top findings only
/improve deep                   exhaustive: every package, every category
/improve security               focused audit (also: perf, tests, bugs, ...)
/improve branch                 audit only what the current branch changes
/improve next                   feature suggestions — where to take the project
/improve plan      skip the audit, spec one thing
/improve review-plan      critique and tighten an existing plan
/improve execute          dispatch a cheaper executor, review its work
/improve reconcile              refresh the backlog: verify, unblock, retire
/improve ... --issues           also publish plans as GitHub issues

How to use

A typical first run, start to finish:

1. Open your agent in the repo and run /improve (or /improve quick to keep it cheap). 2. It maps the repo, audits it, and comes back with a findings table. Reply with the ones you want planned — "plan 1, 3 and 5". 3. Plans land in plans/ — one file each, plus an index with the recommended order. Read them; they're meant to be reviewed. 4. Hand a plan to any agent ("implement plans/001-*.md"), or let the skill run it: /improve execute 001. It dispatches a cheaper model in an isolated worktree, reviews the diff against the plan, and reports back with a verdict. Merging stays up to you. 5. Next session, run /improve reconcile to clean up the backlog: verify what landed, refresh what drifted, unblock what got stuck.

Before a PR, /improve branch does the same thing scoped to just what your branch changes.

Example

A run against shadcn/ui came back with findings like:

| # | Finding                                        | Category  | Effort | Confidence |
|---|------------------------------------------------|-----------|--------|------------|
| 1 | shadow-config duplicated in search.ts/view.ts, | tech-debt | M      | HIGH       |
|   | copies already drifted (TODO at search.ts:31)  |           |        |            |
| 2 | O(n²) icon migration (migrate-icons.ts:168)    | perf      | S      | HIGH       |

…and rejected a few, with reasons recorded so they don't come back next run:

- [SEC-01] https_proxy env var "SSRF": by-design — standard proxy convention,
  every CLI honors it. Not a finding.

Picking #1 produced this plan — current code excerpted, exact steps, the repo's own test/lint commands as verification gates, and STOP conditions for when reality doesn't match.

How it works

Recon. Maps the repo: stack, conventions, and the exact build/test/lint commands — these become verification gates in every plan. It also ingests intent and design docs when present — ADRs (docs/adr/), PRDs, CONTEXT.md, DESIGN.md, PRODUCT.md — so decided tradeoffs aren't re-flagged as findings, direction suggestions stay grounded in stated product intent, and plans speak the repo's own vocabulary. Composes with any repo that already maintains these docs.

Audit. Fans out parallel subagents across nine categories: correctness, security, performance, test coverage, tech debt, dependencies & migrations, DX, docs, and direction (feature suggestions — every one must cite evidence from the repo itself, no generic idea-slop). Every finding carries file:line evidence, impact, effort, and confidence.

Vet. Subagents over-report, so the advisor re-reads every cited location itself before showing you anything — false positives get dropped, wrong attributions get corrected, rejections get recorded.

Prioritize. Findings land in a table ordered by leverage (impact ÷ effort, weighted by confidence). You pick what becomes plans.

Plan. One file per selected finding, written into plans/ with an index, priority order, and dependency graph.

What makes the plans executable

Plans are written for the weakest plausible executor — a model that has never seen the advisor session and may be much smaller. Three properties carry that:

Each plan also stamps the git commit it was written against, so executors run a mechanical drift check before touching anything.

Closing the loop

Plans aren't fire-and-forget:

Hard rules

License

MIT © shadcn

GitHub Stars & Activity

10,353Stars
427Forks
17Open issues
-Language

GitHub Popularity

GitHub stars10,353
Forks427
Open issues17
Primary language-
LicenseMIT
Stars gained today333
Created2026-06-10
Last pushed2026-09-12

Trending History

Daily boardrank #21 · ▲ 333 stars

Related GitHub Projects

1

mattpocock / skills

Shell★ 321,452⑂ 26,944▲ 1,225 stars
→
2

obra / superpowers

Shell★ 315,986⑂ 28,108▲ 656 stars
→
3

anthropics / skills

Python★ 192,782⑂ 22,682▲ 1,679 stars
→
4

ohmyzsh / ohmyzsh

Shell★ 190,508⑂ 27,294▲ 49 stars
→
5

microsoft / PowerToys

C★ 140,861⑂ 8,728▲ 63 stars
→
6

ggml-org / llama.cpp

C++★ 134,057⑂ 25,389▲ 120 stars
→
7

krahets / hello-algo

Python★ 132,196⑂ 15,616▲ 72 stars
→
8

rustdesk / rustdesk

Rust★ 128,999⑂ 20,549▲ 135 stars
→

More Trending Repositories