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Code Improver

OrganizationPopular
trailofbits
code-improver

Runs an autonomous review-and-fix improvement loop over any code target — a skill, plugin, module, or directory — using a reviewer the user names: any installed skill or agent. Keeps a cross-round findings ledger, escalates when fixes stop converging, and guards scope mechanically. Use when asked to 'improve this code until review passes', 'run an improvement loop with <reviewer>', or to iterate review-and-fix with a specific reviewer. For skills prefer the skill-improver entry; for a branch prefer pr-improver.

Overview

Publishertrailofbits
Repositoryskills
Skill namecode-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 Code 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/code-improver .claude/skills/code-improver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code 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 Code 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 Code 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.

Code Improver

Improve any code target by running /code-improver:improve — a dynamic workflow that loops the named reviewer and a fixer subagent until a review reports zero critical/major findings, then strips its own residue. The loop, its ledger, and its guards live in the workflow; this skill collects the three inputs the generic entry requires and relays the outcome.

Starting the loop

The user provided: $ARGUMENTS (if empty, take the details from the conversation).

1. Collect the three required inputs — no guessing

  1. Target: the absolute path to the directory under improvement. Resolve relative paths against the working directory; verify the directory exists.
  2. Reviewer: the installed skill or agent that performs every review. The user must name it — there is no default and no bundled reviewer. Determine the kind:
    • a namespaced agent (e.g. plugin-dev:skill-reviewer) → "kind": "agent"
    • an installed skill (e.g. pr-review-toolkit:review-pr) → "kind": "skill" If the name could be either, check the session's skill listing; if still ambiguous, ask the user. If no reviewer was named, ask — do not pick one.
  3. Scope: repo-relative globs the loop may touch. The generic entry requires it explicitly; if the user did not give one, propose the target directory (<repo-relative-target>/**) and confirm before launching.

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": "<absolute target path>",
  "reviewer": { "kind": "agent|skill", "name": "<namespaced-name>", "notes": "<what the reviewer should know about the target>" },
  "scope": ["<repo-relative-glob>/**"],
  "pluginRoot": "<the plugin directory from step 2>",
  "maxRounds": 5
}
  • 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 ({"version_bump": bool, "narration_strip": bool, "docs_pass": bool}) only to override the defaults: version bump when the target sits inside a plugin, narration strip and docs pass always.
  • decision only on continuation (below).

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 named reviewer is not installed in this session; tell the user which plugin provides it and re-run after installing. Do not review the target 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 target and reviewer 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

  • A Claude Code skill: use the skill-improver entry — it wires the right reviewer
  • A branch / pull request: use the pr-improver entry — it derives scope from the diff
  • One-time review: dispatch the reviewer directly; the loop's value is iteration
  • Quick single fixes: edit the file directly

Frequently asked questions

What does the Code Improver AI skill do?

Runs an autonomous review-and-fix improvement loop over any code target — a skill, plugin, module, or directory — using a reviewer the user names: any installed skill or agent. Keeps a cross-round findings ledger, escalates when fixes stop converging, and guards scope mechanically. Use when asked to 'improve this code until review passes', 'run an improvement loop with <reviewer>', or to iterate review-and-fix with a specific reviewer. For skills prefer the skill-improver entry; for a branch prefer pr-improver.

Why use Code Improver on TypingMind?

Because you install it once and use it with any model. Code 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 Code Improver in TypingMind?

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

Which AI models can use Code 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 Code Improver?

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

Is the Code 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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