shadcn/improvePublic

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

AI summary: An agent skill that audits codebases and writes verifiable implementation plans for cheaper execution models.

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MITCreated Jun 10, 2026Last push 1mo ago+69 stars this week+69 this month

Star history

since Jun 7, 2026
02.5K5K7.5KJun 2026Jun 2026Jul 2026Aug 2026
8.8K stars as of Aug 6, 2026, tracked back to Jun 7, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

Contribution activity

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14 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Breakout launch

    8,760 stars in 58 days

  • Permissive license

    MIT

  • Repeat trending

    5 trending appearances

What improve does

Delegates complex repository analysis to high-capability AI models to produce detailed implementation specs, which are then handed off to smaller, cheaper models to execute. It parallelizes audits across multiple categories like security, tech debt, and performance, grounding its findings in the repository's own design documents. The resulting plans include exact code excerpts, verified local test commands, and strict scope boundaries to keep the executor on track. It does not modify source code directly, leaving that to the executor in isolated worktrees.

Engineering teams and open-source maintainers using AI agents to manage codebase health and automate implementation.

  • Decoupled planning: separates the expensive analysis phase from the cheaper execution phase
  • Verifiable execution gates: includes the exact build, test, and lint commands required to validate the output
  • Context-aware auditing: reads intent docs like ADRs and product PRDs to prevent flagging intentional design tradeoffs
  • Automated drift checking: stamps plans with the Git commit hash to prevent executors from operating on outdated code
  • Executor isolation: spawns cheaper models in disposable worktrees and reviews their diffs before proposing a merge

Where teams use it

Tech debt mitigation

Identifies duplicated logic or performance bottlenecks and writes step-by-step plans to refactor them.

Cost-efficient AI coding

Uses premium LLMs strictly for architecture and review while spending cheap tokens on writing the actual boilerplate.

Repository onboarding

Generates prioritized feature suggestions or security fixes based strictly on the current state of a new codebase.

Asynchronous agent workflows

Publishes actionable implementation plans directly as GitHub issues for distributed agent teams.

Getting started: npx skills add shadcn/improve

README

main branch

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 <description>     skip the audit, spec one thing
/improve review-plan <file>     critique and tighten an existing plan
/improve execute <plan>         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:

  • Self-contained. All context is inlined: exact file paths, current-state code excerpts, repo conventions with an exemplar file, verified commands. No "as discussed above."
  • Verification gates. Every step ends with a command and its expected output. Done criteria are machine-checkable. The executor never has to judge whether it succeeded.
  • Hard boundaries. Explicit out-of-scope lists, and STOP conditions — "if X, stop and report" — instead of letting a small model improvise when reality doesn't match the plan.

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:

  • execute <plan> spawns a cheaper executor subagent in an isolated git worktree, hands it the plan, then reviews the result like a tech lead — re-runs every done criterion, checks scope compliance, reads the diff against intent. Verdict: approve (merging stays your call), send back for revision (max 2 rounds), or block and refine the plan.
  • reconcile processes what happened since: verifies DONE plans still hold, investigates BLOCKED ones and rewrites around the obstacle, refreshes drifted plans, retires findings that got fixed independently.
  • --issues publishes plans as GitHub issues — same self-contained body, so any agent or human can pick them up where work already lives.

Hard rules

  • Never modifies source code itself. The only writes go to plans/; executors edit only in disposable worktrees, and merging is always yours.
  • Never runs commands that mutate your working tree — read, search, and read-only analysis only.
  • Never reproduces secret values. Locations and credential types only, rotation always recommended.
  • Asked to implement? It declines and points at the plan (or offers execute).

License

MIT © shadcn

View on GitHub

Recent activity

commits and pull requests

Code frequency

additions and deletions
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+908 lines added, -33 removed over the last year.

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last 52 weeks
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14 commits in the last 52 weeks.

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Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks
Maintainer commits17 (77%)
Community commits5 (23%)

22 commits in total over the last year.

DateListRankStars gained
Jun 15, 2026daily#16+22
Jun 14, 2026daily#7+60
Jun 13, 2026daily#15+42
Jun 12, 2026daily#19+21
Jun 11, 2026daily#17+22