Agent skill

Autoreview

by vincentkoc in vincentkoc/tokenjuice

Auto Review closeout. An agent skill from vincentkoc/tokenjuice.

MITAuto-check passedAI & LLM Engineering

Install Autoreview

skills CLI
$ npx skills add vincentkoc/tokenjuice --skill autoreview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vincentkoc/tokenjuice autoreview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vincentkoc/tokenjuice.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/autoreview .claude/skills/autoreview && rm -rf skills-src

Use ~/.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/

Facts

Skill name
autoreview
GitHub stars
524
Token cost
~1.9k tokens
SKILL.md length
976 words
Files
3 (incl. scripts)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Auto Review closeout. An agent skill from vincentkoc/tokenjuice.

  • AI & LLM Engineering work in your project
  • SKILL.md covers Contract, Pick Target, Parallel Closeout and Context Efficiency, plus 2 more sections
  • Calls codex and gh

What it does

Autoreview is an agent skill from vincentkoc/tokenjuice. Auto Review closeout. Codex review is the default when no engine is set and is the recommended reviewer.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts.

It sits in AI & LLM Engineering. The repository describes itself as: 🧃 Token weight loss. Lean output compaction for terminal-heavy agent workflows. Works as a native CLI tool or as an extension to popular coding and agent frameworks. The licence is MIT.

When your agent uses it

  • AI & LLM Engineering work in your project

Example prompts

  • “/autoreview”

What it can do on your machine

Read from SKILL.md and the folder at commit 001d597. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships 2 files in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • codex
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Autoreview loads about 1.9k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 976 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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.

Safety

Auto-check passed

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); the scripts in this folder are not scanned.

SKILL.md

The full file from vincentkoc/tokenjuice at commit 001d597, republished under its MIT licence (© vincentkoc). 976 words, ~1,894 tokens.

Download SKILL.mdSave it as .claude/skills/autoreview/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
autoreview
description
Auto Review closeout. Codex review is the default when no engine is set and is the recommended reviewer.

Auto Review

Run the bundled structured review helper as a closeout check. This is code review, not Guardian auto_review approval routing.

Codex review is the default when no engine is set. It usually delivers the best review results and should remain the normal final closeout engine.

Use when:

  • user asks for Codex review / Claude review / autoreview / second-model review
  • after non-trivial code edits, before final/commit/ship
  • reviewing a local branch or PR branch after fixes

Contract

  • Treat review output as advisory. Never blindly apply it.
  • Verify every finding by reading the real code path and adjacent files.
  • Read dependency docs/source/types when the finding depends on external behavior.
  • Reject unrealistic edge cases, speculative risks, broad rewrites, and fixes that over-complicate the codebase.
  • Prefer small fixes at the right ownership boundary; no refactor unless it clearly improves the bug class.
  • Keep going until structured review returns no accepted/actionable findings.
  • If a review-triggered fix changes code, rerun focused tests and rerun the structured review helper.
  • For security-audit suppression changes, verify accepted findings remain auditable: suppressed findings stay in structured output, active output keeps an unsuppressible suppression notice, and aggregate findings cannot hide unrelated active risk.
  • Never switch or override the requested review engine/model. If the review hits model capacity, retry the same command a few times with the same engine/model.
  • Tools are useful in review mode. The helper allows read-only inspection tools and web search by default so reviewers can check dependency contracts, upstream docs, and current behavior.
  • Security perspective is always included, but it should not cripple legitimate functionality. Report security findings only when the change creates a concrete, actionable risk or removes an important safety check.
  • Do not invoke built-in codex review, nested reviewers, or reviewer panels from inside the review. The helper builds one bundle, calls one selected engine, validates one structured result, and stops.
  • Stop as soon as the helper exits 0 with no accepted/actionable findings. Do not run an extra review just to get a nicer "clean" line, a second opinion, or clearer closeout wording.
  • Treat the helper's successful exit plus absence of actionable findings as the clean review result, even if the underlying Codex CLI output is terse.
  • If rejecting a finding as intentional/not worth fixing, add a brief inline code comment only when it explains a real invariant or ownership decision that future reviewers should know.
  • If gh/Gitcrawl reports database disk image is malformed, run gitcrawl doctor --json once to let the portable cache repair before retrying review; do not bypass the shim unless repair fails and freshness requires live GitHub.
  • If Gitcrawl reports a portable manifest mismatch, source/runtime DB health error, or stale portable-store checkout, run gitcrawl doctor --json and inspect source_db_health, runtime_db_health, and portable_store_status before falling back to live GitHub.
  • Do not push just to review. Push only when the user requested push/ship/PR update.

Pick Target

Dirty local work:

bash
<autoreview-helper> --mode local

Use this only when the patch is actually unstaged/staged/untracked in the current checkout. For committed, pushed, or PR work, point the helper at the commit or branch diff instead; do not force --mode local / --uncommitted just because the helper docs mention dirty work first. A clean local review only proves there is no local patch.

Branch/PR work:

bash
<autoreview-helper> --mode branch --base origin/main

The helper does not fetch remotes unless --fetch is passed. If the base ref is missing or stale, fetch explicitly before review or use --fetch knowingly.

Optional review context is first-class:

bash
<autoreview-helper> --mode branch --base origin/main --prompt-file /tmp/review-notes.md --dataset /tmp/evidence.json

If an open PR exists, use its actual base:

bash
base=$(ghx pr view --json baseRefName --jq .baseRefName)
<autoreview-helper> --mode branch --base "origin/$base"

Committed single change:

bash
<autoreview-helper> --mode commit --commit HEAD

Use commit review for already-landed or already-pushed work on main. Reviewing clean main against origin/main is usually an empty diff after push. For a small stack, review each commit explicitly or review the branch before merging with --base.

Show full SKILL.md (365 more words)Show less

Parallel Closeout

Format first if formatting can change line locations. Then it is OK to run tests and review in parallel:

bash
scripts/autoreview --parallel-tests "<focused test command>"

Tradeoff: tests may force code changes that stale the review. If tests or review lead to code edits, rerun the affected tests and rerun review until no accepted/actionable findings remain. Once that rerun exits cleanly, stop; do not spend another long review cycle on redundant confirmation.

Context Efficiency

Run the helper directly so target selection, engine choice, structured validation, and exit status all stay in one path. If output is noisy, summarize the completed helper output after it returns; do not ask another agent or reviewer to rerun the review.

Helper

Repo-local helper:

bash
.agents/skills/autoreview/scripts/autoreview --help

agent-scripts checkout helper:

bash
skills/autoreview/scripts/autoreview --help

Global helper from agent-scripts:

bash
~/.codex/skills/agent-scripts/autoreview/scripts/autoreview --help

The helper:

  • chooses dirty local changes first
  • otherwise uses current PR base if ghx pr view or gh pr view works
  • otherwise uses origin/main for non-main branches
  • supports --engine codex, claude, droid, copilot, pi, and opencode; default is AUTOREVIEW_ENGINE or codex; Codex should remain the default when nothing is set
  • --engine pi requires an explicit --model because the helper isolates Pi's config directory during review
  • use --mode commit --commit <ref> for already-committed work, especially clean main after landing
  • should be left in --mode auto or forced to --mode branch for PR/branch work; do not force --mode local after committing
  • writes only to stdout unless --output or --json-output is set
  • supports --dry-run, --fetch, --parallel-tests, --prompt, --prompt-file, --dataset, --no-tools, --no-web-search, and commit refs
  • allows read-only tools and web search by default where the selected CLI supports them; forbids nested review in the prompt; Codex is run through codex exec from an isolated temporary workspace with user config and project rules disabled, read-only sandbox, and structured output
  • prints autoreview clean: no accepted/actionable findings reported when the selected review command exits 0
  • exits nonzero when accepted/actionable findings are present

Final Report

Include:

  • review command used
  • tests/proof run
  • findings accepted/rejected, briefly why
  • the clean review result from the final helper/review run, or why a remaining finding was consciously rejected

Do not run another review solely to improve the final report wording. If the final helper run exited 0 and produced no accepted/actionable findings, report that exact run as clean.

© vincentkoc, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in .agents/skills/autoreview of vincentkoc/tokenjuice.

  • SKILL.md
  • scripts/autoreview
  • scripts/test-review-harness

Open the folder on GitHubat commit 001d597

Compare with similar skills

Autoreview 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.

Autoreview compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Autoreview this skillvincentkoc/tokenjuice524—~1.9kAutomated safety check: PassMIT
Agent BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT
Add Uint Supportpytorch/pytorch104k2 repos~2.3kAutomated safety check: PassCustom licence
LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs13k8 repos~3kAutomated safety check: PassMIT
Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs13k8 repos~3.3kAutomated safety check: PassMIT
1passwordtrpc-group/trpc-agent-go1.9k14 repos~656Automated safety check: PassApache-2.0

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Questions about Autoreview

What does Autoreview do?

Auto Review closeout. An agent skill from vincentkoc/tokenjuice. Autoreview is an agent skill from vincentkoc/tokenjuice. Auto Review closeout.

When should I use Autoreview?

Autoreview fits situations like: AI & LLM Engineering work in your project.

How do I install Autoreview in Claude Code?

Run `npx skills add vincentkoc/tokenjuice --skill autoreview -a claude-code`. Or copy the skill folder (.agents/skills/autoreview in vincentkoc/tokenjuice) into .claude/skills/autoreview in your project. Claude Code loads it when a task matches its description.

How do I install Autoreview in Codex?

Run `npx skills add vincentkoc/tokenjuice --skill autoreview -a codex`. Or copy the skill folder (.agents/skills/autoreview in vincentkoc/tokenjuice) into .agents/skills/autoreview in your project. Codex loads it when a task matches its description.

Can I use Autoreview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vincentkoc/tokenjuice --skill autoreview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/autoreview, .gemini/skills/autoreview, .github/skills/autoreview and .opencode/skills/autoreview in your project.

What does Autoreview need to run?

Going by SKILL.md and its folder, Autoreview needs the command-line tools its instructions call (codex and gh).

Does Autoreview access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Autoreview safe to install?

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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Autoreview use?

Autoreview is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Autoreview use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Autoreview?

Skills that share tags, products or a category with Autoreview: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autoreview?

vincentkoc (a GitHub user) maintains it in vincentkoc/tokenjuice, which has 524 GitHub stars. The repository was last updated on September 15, 2026.

Source: vincentkoc/tokenjuice on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.