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Pr Improver

OrganizationPopular
trailofbits
pr-improver

Runs an autonomous review-and-fix improvement loop over the current branch's changes until a PR review comes back clean, scoped mechanically to the directories the branch touched. Reviews are performed by an installed PR-review skill (default: pr-review-toolkit's review-pr). Use to fix review findings on a branch before opening or updating a pull request ('clean up this branch', 'fix this PR until review passes', 'run review-and-fix on my changes'). NOT for a one-time review — run the PR-review skill directly.

Overview

Publishertrailofbits
Repositoryskills
Skill namepr-improver
Stars
7.1K
Forks
611
Bundled files
Instructions only
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by trailofbits on GitHub. Read the source before you install it.

Installation

Install the Pr Improver AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trailofbits/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/code-improver/skills/pr-improver .claude/skills/pr-improver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr Improver in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pr Improver on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pr Improver is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PR Improver

Improve the current branch by running /code-improver:improve — a dynamic workflow that loops a PR reviewer and a fixer subagent over the branch's changes until a review reports zero critical/major findings. The loop, its ledger, and its guards live in the workflow; this skill derives the scope from the branch diff and relays the outcome.

Starting the loop

The user provided: $ARGUMENTS (if empty, take base branch and preferences from the conversation).

1. Resolve the branch and its change surface

  1. Repo root: git rev-parse --show-toplevel. Fail loudly outside a repository.
  2. Base: the argument if given, else the repository's default branch (git symbolic-ref refs/remotes/origin/HEAD → its short name, falling back to main). Refuse to run when the current branch IS the base — there is no diff to improve.
  3. Changed files: git diff --name-only <base>...HEAD. If empty, say so and stop.
  4. Scope: the changed files' directories, widened — per-file globs are too tight (PR fixes legitimately add tests next to changed code). Map each changed file to its repo-relative directory glob <dir>/** (** at the repo root only if files at the root changed), then deduplicate and drop globs covered by another.

2. Resolve the loop script

The loop is the dynamic workflow workflows/improve.js in this plugin. Launch it by path: scriptPath takes a resolved absolute path, and the Workflow tool's name resolves built-in and project workflows, so a marketplace-installed one may not answer to code-improver:improve. Try in order, first hit wins — the home directories come before . so an installed copy beats a checkout of this marketplace:

  1. Bash: ls -d -- "${CLAUDE_PLUGIN_ROOT}/workflows/improve.js"
  2. Bash: ls -d -- "${CODEX_PLUGIN_ROOT}/workflows/improve.js" (if that variable is set instead)
  3. Bash: find ~/.claude ~/.codex . -maxdepth 7 -path '*/code-improver/workflows/improve.js' -print -quit 2>/dev/null

Use the path exactly as printed. Its plugin directory — the path with /workflows/improve.js removed — is pluginRoot. If all three come back empty, try {name: "code-improver:improve"} once; if that is unavailable too, stop and say the loop could not be located. Do not assemble a path by hand and do not improvise the loop.

3. Invoke the workflow

Run it with the Workflow tool, {scriptPath: "<the path from step 2>", args: {...}}:

json
{
  "target": "<repo root>",
  "reviewer": {
    "kind": "skill",
    "name": "pr-review-toolkit:review-pr",
    "notes": "Review the working tree's changes against <base> as a pull request: correctness, tests, error handling, and the review dimensions the skill prescribes."
  },
  "scope": ["<derived-dir-glob>/**"],
  "pluginRoot": "<the plugin directory from step 2>",
  "maxRounds": 5
}
  • reviewer — the default above requires the pr-review-toolkit plugin. When the user names a different PR reviewer (skill or agent), use it, with kind set accordingly.
  • maxRounds only if the user asked for a different cap.
  • pluginRoot lets the run find its metrics collector; omit the key only if step 2 fell through to the workflow name — the workflow then searches for itself.
  • finalize defaults are right for PRs: no version bump unless the branch sits inside a plugin, narration strip and docs pass on.
  • decision only on continuation (below).

The loop's baseline snapshot is the tree at loop start — its scope guard protects the branch's uncommitted work; the PR's own commits are what the reviewer reviews.

The workflow runs in the background and needs no babysitting: it reviews, fixes, re-reviews, checks scope after every fix round, and can only complete on a clean review. It never commits; all changes stay in the working tree.

If the Workflow tool is unavailable or denied, stop and say so. Do not improvise the loop inline with direct edits — the ledger, scope guard, and escalation guarantees live in the workflow, and an inline imitation has none of them.

If the result is halted: "reviewer-unavailable", relay it and stop. The reviewer is not installed in this session; tell the user which plugin provides it (the default needs pr-review-toolkit) and re-run after installing. Do not review the branch yourself.

Do not end your turn while the loop is running. The Workflow tool returns a task id immediately; the result comes later. In an interactive session the completion notification re-invokes you — wait for it. In a non-interactive run (scripted, CI, eval) there is no later turn: stopping abandons the loop mid-round, so after launching, poll the task (TaskOutput with the returned task id, or sleep-and-recheck) until it completes, then relay the result. A session that answers "the loop is running, I'll report later" has lost the run.

Relaying the result

The workflow returns a structured result. Report it honestly — the distinctions matter:

  • converged: true — the last action was a review with zero critical/major findings. Report rounds used, remaining minor findings (open_minor_count), and the artifact paths (ledger_path, metrics).
  • capped: true — the fix budget ran out and the FINAL review still found blocking issues. Say plainly: capped, NOT converged, and list open_blocking. Do not present this as success.
  • escalation — the loop detected it was not converging (recurring findings, non-decreasing counts, or a fix relocating a problem). Relay the escalation message and finding ids to the user: this needs a design decision, not more rounds.
  • halted — a guard fired (scope violation, unregistered new files, a dead or unavailable reviewer, or a finalize pass whose own edits failed the check that follows it). Relay the paths in violations/new_untracked_files, the sites in finalize_regressions, and the notes.
  • notes always travel with the result — surface them; they include loud warnings such as "a git repository was initialized".

Continuing after an escalation

The loop stops on escalation by design. When the user decides, start a fresh run with the same args plus:

json
{ "decision": "<the user's ruling, verbatim>" }

The new run reloads the on-disk ledger, so every finding, rejection, and verdict carries over — rounds restart, re-derivation does not.

To stop a running loop, stop the workflow task (TaskStop); the ledger on disk is current to the last round and a re-run resumes from it.

When NOT to use

  • One-time review: run the PR-review skill directly; the loop's value is iteration
  • A skill: use the skill-improver entry — it wires the right reviewer
  • Unpushed exploratory work: review-and-fix loops harden a diff; while the shape is fluid, manual iteration gives more control

Frequently asked questions

What does the Pr Improver AI skill do?

Runs an autonomous review-and-fix improvement loop over the current branch's changes until a PR review comes back clean, scoped mechanically to the directories the branch touched. Reviews are performed by an installed PR-review skill (default: pr-review-toolkit's review-pr). Use to fix review findings on a branch before opening or updating a pull request ('clean up this branch', 'fix this PR until review passes', 'run review-and-fix on my changes'). NOT for a one-time review — run the PR-review skill directly.

Why use Pr Improver on TypingMind?

Because you install it once and use it with any model. Pr Improver is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pr Improver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills/tree/main/plugins/code-improver/skills/pr-improver. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pr Improver?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pr Improver?

As many as you like. As long as a model supports skills, you can use Pr Improver with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pr Improver AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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