Agent skill

Autoreview

by udecode in udecode/plate-playground-template

Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

MITAuto-check passedDevelopment

Install Autoreview

skills CLI
$ npx skills add udecode/plate-playground-template --skill autoreview -a claude-code

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

GitHub CLI
$ gh skill install udecode/plate-playground-template 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/udecode/plate-playground-template.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
240
Used in
1 other repo
Token cost
~5.3k tokens
SKILL.md length
2,641 words
Files
24 (incl. scripts, references)
Skills in repo
4
Repo updated
First seen
Licence
MIT

At a glance

Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

  • Development work in your project
  • SKILL.md covers Run, Context and severity, Engines and Image review, plus 2 more sections
  • Runs Python, TypeScript, PowerShell and Swift scripts from its folder; calls codex, python3 and gh; reaches api.openai.com; needs AMP_API_KEY

What it does

Autoreview is an agent skill from udecode/plate-playground-template. Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 27 other files, including scripts and reference files (for example `AGENTS.md`, `references/diagnostics-and-results.md` and `references/repository-entrypoint.md`).

It sits in Development. The repository describes itself as: Plate AI template with React 19, Next 16, Tailwind 4, MCP. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/autoreview”

Requirements

  • Python 3
  • Node.js
  • PowerShell

What it can do on your machine

Read from SKILL.md and the folder at commit 324070e. 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 6 files in scripts/ (Python, TypeScript, PowerShell and Swift, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • codex
    • python3
    • gh
    • git
    • python

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.openai.com

    Also links to:

    • developers.openai.com
    • learn.chatgpt.com
    • github.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • AMP_API_KEY

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

Context cost

Autoreview loads about 5.3k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 25 tokens; SKILL.md has 2,641 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.5k

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 udecode/plate-playground-template at commit 324070e, republished under its MIT licence (© udecode). 2,641 words, ~5,280 tokens.

Download SKILL.mdSave it as .claude/skills/autoreview/SKILL.md (or your agent's skills folder). This skill also uses 23 other files; get the full folder from GitHub.
name
autoreview
description
Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

Auto Review

Run an independent review when the user or an owning workflow asks for one. This is code review, not Guardian approval routing. Let the reviewer choose how to analyze the change; provide the target, relevant context, and desired severity. Findings are advice to verify, not instructions to apply blindly.

Before starting a review, read the complete diagnostic and result guidance. It is part of this skill; follow its output-path, status, failure, usage, and diagnostic rules.

Run

Use scripts/autoreview beside this skill. Keep its custom codex exec path: native codex review cannot combine explicit Git target flags with custom instructions. The helper combines those with evidence, severity filtering, and validated JSON; it leaves review judgment to Codex. Install this skill once with the canonical repository's python3 scripts/install-skills autoreview. The default installation at ~/.agents/skills/autoreview links to this source checkout.

Run from the repository being reviewed:

bash
AUTOREVIEW="$HOME/.agents/skills/autoreview/scripts/autoreview"
python3 "$AUTOREVIEW" --mode local

In the canonical agent-skills repo, the path is skills/autoreview/scripts/autoreview. Use the selected installation path when installed with --target. On Windows, invoke the helper with Python 3.10 or newer, or use the adjacent autoreview.ps1 launcher. Use --help for the complete flags and environment overrides.

Repositories keep only the shared-skill entrypoint. Read this full skill and its required references from the shared installation. Keep repository-specific thresholds and review requirements in the consumer's instructions. Upstream changes to shared behavior here.

Update the canonical checkout once to update every symlinked consumer. Finish active reviews before updating their helper. Copy-mode installations require re-running scripts/install-skills --mode copy --force autoreview after updating the source checkout. Review commands never download or update themselves.

Choose the Git target explicitly when the default is ambiguous:

TargetArgumentsScope
Local work--mode localHEAD → index → working tree, plus untracked files
Local candidate against a base--mode local --base <ref>Pinned base → index → working tree, plus untracked files
Committed branch/PR--mode branch --base <ref>Merge-base → HEAD; excludes dirty work
One commit--mode commit --commit <ref>Raw parent → commit; a root compares against the empty tree

--mode auto selects local work when dirty, otherwise a branch review using the PR base or origin/main. Clean main has no implicit review target. --mode uncommitted is an alias for local. The helper does not fetch refs.

Registered nested linked checkouts from the same repository are outside the current review scope. Their presence or edits do not make the parent dirty; ordinary adjacent files remain included in the review. Worktree boundaries are revalidated without changing Git ignore rules.

For a complete PR candidate including dirty rewrites, use local mode with its pinned merge base—not branch mode:

bash
pr_base=$(gh pr view --json baseRefName --jq .baseRefName)
merge_base=$(git merge-base HEAD "origin/$pr_base")
"$AUTOREVIEW" --mode local --base "$merge_base"

When a file has both staged and unstaged changes, both states are reviewed. A defect in the index remains actionable even if the working tree fixes it; the report labels it INDEX-only. Git display settings cannot suppress context markers or add patch colors; repository configuration is not changed. Source paths and text retain literal whitespace. An empty present source uses line 1, column 1, and an empty excerpt; empty physical lines also use an empty excerpt at column 1. Source identity remains mandatory.

Binary deletions remain in scope as Git deletion metadata; their former contents are not included or reviewed. Each local transition is checked independently: deleting a file in the working tree cannot hide a staged binary change.

Finding locations may use native absolute paths that resolve inside the reviewed repository; these become repository-relative paths before scope and attribution checks, preserving a changed symlink's path when its target is also inside. Parent traversal and paths resolving outside the repository remain invalid. An invalid location still fails the report; findings are never silently dropped.

Local selection honors core.autocrlf from external operator Git configuration, with repository-local values and attributes retaining precedence. Only its validated scalar value reaches diff/status; other global and system Git configuration stays disabled. Repository-owned or relative global-config overrides are not imported, and reviewed source bytes are not rewritten.

Local collection disables effective Git clean/process commands and requires conversion to succeed. Unused drivers, unchanged filtered neighbors, staged-only changes, and deletions can still be reviewed without executing converters. If Git needs executable conversion to assemble the diff, collection fails before any reviewer starts. This can include an unchanged filtered file whose stat cache needs refreshing. Use explicit branch or commit mode for committed content in that case. Built-in line-ending normalization remains enabled; raw bytes never stand in for a required executable conversion. PR-base discovery uses trusted external Git and a scoped GitHub CLI environment, preserving external authentication/configuration and proxy settings while excluding inherited Git routing, GH_REPO redirection, and checkout-owned executables. A differently named AUTOREVIEW_GIT override that cannot also be selected as git by the child requires an explicit --base; rejected GitHub configuration paths also require one.

Context and severity

Use --prompt for task-specific guidance, or --prompt-file and --dataset for repository-relative context files. Context does not expand the selected Git target. The reviewer cannot read unchanged repository files from its empty sandbox; supply relevant source or dependency evidence when the diff is insufficient. --prompt-file also accepts an absolute path inside the repository; the same sensitive-path, symlink, and mutation checks apply. --dataset stays repo-relative. Repeated paths in the same evidence role share one validated capture. Equal content at different paths and prompt-file versus dataset roles stay distinct.

For unchanged committed source, use repeatable --source-context <repo-relative-path> with branch or commit mode. Use --source-context-file <repo-relative-path> when that source must stay intact in every review pass. Both read the exact regular-file blob from the frozen reviewed commit (branch HEAD or --commit), including executable source files. Local mode, including an auto-selected local target, is unsupported. No separate context revision or working-copy substitution is accepted. The checkout path must remain a regular file; its bytes and path topology are revalidated throughout review. Repeated normalized source-context paths share one capture after every argument is validated; different paths and evidence roles remain distinct.

Both roles use tracked-source filename classification, so source names such as src/token_count.py are accepted. Credential directories, stores and keyfiles remain forbidden. Existing prompt-file and dataset restrictions are unchanged. Every source block carries path, commit, blob and mode provenance. --source-context bytes are partitioned with the change when needed. --source-context-file blocks stay complete in every pass and must fit with the instructions and change framing; the helper refuses an over-capacity plan without dropping required evidence. Context never adds finding targets or instruction authority. This is a source-provenance contract, not secret-content scanning.

bash
"$AUTOREVIEW" --mode branch --base origin/main --source-context src/token_count.py
"$AUTOREVIEW" --mode branch --base origin/main --source-context-file src/token_count.py

The default threshold is P0 only: material blockers to normal operation or safety. Use --max-priority P1, P2, or P3 when the caller requests a wider review. AUTOREVIEW_MAX_PRIORITY accepts the same P0–P3 values; an explicit flag overrides it. Invalid resolved priorities fail during argument parsing, before preparation or reviewer startup. Do not add unrelated redesign goals or prescribe file counts, reading sequences, or ritual extra passes. Historical blame requires a verified parent-relative patch; otherwise leave the attribution unknown.

bash
"$AUTOREVIEW" --mode local --prompt-file review-notes.md --dataset evidence.json

Engines

For automatic reviewer selection, try OpenAI models through Codex before Claude. Start with --engine codex even when the invoking agent uses Codex or asks for an independent second opinion. Use Claude only when the user explicitly selects it or Codex is unavailable for the review; report the concrete availability failure before switching. Do not switch because a review is slow, rate-limited, or returns findings, or to bypass a safety refusal or isolation failure.

Codex defaults to gpt-6.1-sol, high reasoning, with a single gpt-6-sol retry only for an account-access failure. Explicit gpt-6.1-sol selections use the same retry. Explicit gpt-6-sol selections retain their access-only gpt-6-luna retry; other explicit models, including Luna and Astra, have no model fallback. Explicit gpt-5.6-sol selections retain their access-only gpt-5.6-terra retry. GPT-6.1 Sol rejects none and minimal effort before review preparation; GPT-6 Sol and Luna reject minimal. An effort-only override keeps the default model. Honor explicit user engine/model choices. The helper does not automatically fall back between engines.

Use --engine, --model, and --thinking to override the defaults. --codex-speed fast selects priority service when supported; --codex-speed ultrafast selects Ultrafast when the active model catalog lists it (Codex otherwise silently sends the standard tier). Only Claude accepts --fallback-model. Per-engine environment overrides use AUTOREVIEW_<ENGINE>_*.

If your account cannot access Sol or Luna, pin an available model. To require GPT-6 Astra without a model fallback, select it explicitly:

bash
"$AUTOREVIEW" --mode local --model gpt-6-astra --thinking high

GPT-6.1 Sol and GPT-6 Astra support low, medium, high, xhigh, and max; neither supports none or minimal. GPT-6 Sol and Luna additionally support none, but not minimal. AutoReview defaults to high and does not fall back from an explicit Luna or Astra selection. Codex's ultra mode uses automatic delegation and is outside this helper's supported effort levels. Use max for its deepest supported review. For EU data residency, use --codex-speed default; GPT-6 fast mode is unavailable there. See the GPT-6.1 Sol, GPT-6 Sol, and GPT-6 Luna model docs and Codex reasoning modes.

By default, Codex preserves only authentication settings from user configuration; provider, profile, context and catalogue settings remain ignored. To project a named route, select it explicitly through the existing config override:

bash
"$AUTOREVIEW" --mode local --codex-config 'model_provider="review_api"'

The selector must match model_provider in the operator's external CODEX_HOME/config.toml. It accepts one bare or simply quoted identifier; provider definitions and other capabilities cannot be supplied through overrides. Projection requires Python 3.11 or tomli; default auth-only operation retains its existing fallback parser.

The selected route must use https://api.openai.com/v1 and command authentication with an absolute external executable. Fixed arguments belong in that executable's wrapper; omitted or empty auth.args are accepted. Omitted wire_api and requires_openai_auth retain Codex's responses and false defaults. Optional auth timing and context settings keep native defaults and semantics.

On POSIX, a private launcher restores the validated caller HOME only for the selected authentication executable; the engine and reviewer tools retain their isolated environment and filesystem access. Caller HOME must be an available absolute directory with no repository-owned path or symlink provenance. Windows keeps the native executable route. Command-auth runs suppress raw provider diagnostics and report fixed failure categories, while retaining compact progress, usage and assistant report streaming. An empty final report fails without exposing captured stdout.

Catalogue and authentication working-directory paths resolve relative to the operator config directory and must remain outside the reviewed repository. A supplied catalogue is copied byte-for-byte into the private client runtime; retries use the same route and catalogue snapshot. Dry runs check the same ownership and route shape without executing authentication. Codex owns catalogue validation, model access and context clamping. Other custom provider forms and split context overrides are unsupported when projection is selected.

Optional enginePrerequisites
ClaudeCLI 2.1.169+; safe mode with web-only tools
AmpAMP_API_KEY for a plugin-free account; local POSIX execution, no custom endpoint or cloud/orb agent
PiCLI 0.79.0+; configured model; no tools or project resources

--engine kimi remains recognized but is refused for reviews and --dry-run before any Kimi process, configuration read or authentication setup. The supported Kimi Code prompt mode accepts review content only as a command-line argument; the helper has no supported private prompt input channel for it. This intentionally retires the previous Kimi execution path rather than exposing the bundle in process arguments. Existing --kimi-bin arguments remain accepted for the same clear refusal; the helper never silently selects another engine. A custom agent file is not an equivalent replacement because it changes the input into a templated system prompt.

Show full SKILL.md (783 more words)Show less

Image review

Branch mode with Codex supports added, single-frame PNG, JPEG and WebP files. Install Pillow in the Python environment running the helper (python -m pip install Pillow). Use a vision-capable Codex model and a CLI supporting codex exec --image. No new bypass flag is required. Full decoding rejects corrupt and animated files. Images must have at most 16,777,216 pixels and no dimension above 16,384 pixels; decoder bomb warnings fail closed before pixel loading. Added image paths are limited to 20 MiB of encoded bytes each and 100 MiB total, checked against Git object sizes before capture. Exceeding a limit fails the entire review.

The helper captures exact bytes from the pinned HEAD, stages only those images in its isolated workspace, and attaches them through Codex's native image input. Every pass receives the path, media type, byte count and SHA-256 manifest alongside the image attachments and text diff. Image findings use the original path and line 1. Text-only review does not require Pillow.

Binary deletions, including images, are reviewed as deletion metadata in every mode without image attachments or Pillow. Other binaries, modified images, local/commit image additions or modifications, and image review with other engines remain unsupported and fail closed. Missing Pillow or provider image limits fail the review rather than silently dropping assets. Sensitive-path, source-mutation, authentication and sandbox controls remain enabled.

For partial clones, materialize required Git objects before review. The isolated Git reader intentionally disables lazy network fetching; do not weaken that boundary.

Runtime boundaries

The helper owns reviewer isolation, sanitized authentication, process cleanup, Git scope, and structured result validation. Keep those controls enabled. Before repository detection or target selection, Git must pass --version within 10 seconds. Failure exits 2 with an incomplete diagnostic and the resolved executable (or the unresolved selection); it never means scoped-clean. Executable discovery skips inaccessible search candidates; an inaccessible explicit executable override still fails preflight. Set AUTOREVIEW_GIT to a trusted external Git executable to override every helper-owned Git invocation. On macOS with a broken selected Xcode, use DEVELOPER_DIR=/Applications/Xcode.app/Contents/Developer for the invocation. Only an absolute, external DEVELOPER_DIR is additionally retained in Git's sanitized environment; neither override is forwarded to the isolated reviewer environment.

Every reviewer pass must inspect its bundle for real credentials and report suspected credentials as P0 findings without reproducing their values. Harmless placeholders and test fixtures are not credentials. Autoreview does not require or invoke an external secret scanner. Never work around an isolation failure.

Intentional scanner-free policy

Keep approved secret scanning outside autoreview; reviewer findings happen after transmission. Reintroducing a scanner requires an explicit maintainer decision. See #240 for rationale and history.

Reviewer isolation

On macOS, reviewer tools cannot access the shared /tmp and /var/tmp trees (including their /private aliases). Codex preflight rejects those temporary roots before workspace, runtime, or authentication setup; unset a shared TMPDIR/TMP/TEMP override to use macOS's private temporary directory. Other engines and platforms retain their normal isolation. Tools installed in shared scratch or requiring writes there will be denied too.

Text review files have no size/count cap and are never truncated; image inputs use the explicit safety limits above. Large diffs and datasets are partitioned automatically. Change partitions retain complete datasets when they fit with sufficient change space. This preference may use more passes or prompt bytes than evidence batching; the explicit pass budget still applies. Terminal fallbacks preserve a feasible complete-evidence plan when batch framing cannot fit. Intact instructions, source-context files and required mixed source context must fit the per-pass prompt budget. A failed pass does not produce a partial clean verdict. Otherwise, the planner compares a bounded set of evidence allocations and keeps the existing plan unless total prompt bytes improve without more passes, or equal bytes need fewer passes. Every change is still reviewed against every evidence batch.

Each pass is an independent assignment, not a continuing conversation. Its private completion field must confirm a finished assessment; deferring to another pass leaves the overall review incomplete.

Do not edit inputs during a review: the helper verifies captured sources before sending and publishing results. Long reviews are normal; advancing heartbeats mean progress. Use --stream-engine-output for visibility, not extra reviewer runs. --dry-run checks preparation and startup without contacting a reviewer. Both dry runs and execution print planned pass count and total prompt bytes. Use --max-review-passes N (or AUTOREVIEW_MAX_REVIEW_PASSES) to reject the whole plan before any reviewer starts when it exceeds an explicit campaign budget. There is no default pass ceiling. --engine-timeout-seconds remains an optional deadline per process attempt. Pass counts, prompt bytes, and deadlines are not token hard caps; they do not bound model reasoning or tool use.

Diagnostics and results

Follow the diagnostic and result guidance for local stage observation, output paths, exit codes, status, and usage.

© udecode, 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 23 other files (scripts, references) in .agents/skills/autoreview of udecode/plate-playground-template.

  • SKILL.md
  • AGENTS.md
  • references/diagnostics-and-results.md
  • references/repository-entrypoint.md
  • scripts/autoreview
  • scripts/autoreview.ps1
  • scripts/autoreview_test.py
  • scripts/test-review-harness
  • scripts/test-review-harness.ps1
  • scripts/test-review-harness.py
  • tests/fixtures/swift-benign-status-literals.swift
  • tests/fixtures/typescript-benign-config-path-references.ts
  • tests/fixtures/typescript-benign-references.ts
  • tests/fixtures/typescript-sensitive-literals.ts
  • tests/test_autoreview_hardening.py
  • tests/test_codex_inference_route.py
  • tests/test_codex_sandbox.py
  • … and 7 more

Open the folder on GitHubat commit 324070e

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in udecode/plate-playground-template, which our catalogue first saw on October 7, 2026.

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 skilludecode/plate-playground-template2401 repos~5.3kAutomated safety check: PassMIT
Vercel Composition Patternssupabase/supabase111k59 repos~726Automated safety check: PassMIT
Finishing a Development Branchobra/superpowers296k5 repos~1.9kAutomated safety check: PassMIT
Typescript Advanced Typesrolling-scopes/rsschool-app10k25 repos~4.2kAutomated safety check: PassMPL-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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Categories

Questions about Autoreview

What does Autoreview do?

Structured code review when explicitly requested, preferring OpenAI/Codex before Claude. Autoreview is an agent skill from udecode/plate-playground-template. Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

When should I use Autoreview?

Autoreview fits situations like: development work in your project.

How do I install Autoreview in Claude Code?

Run `npx skills add udecode/plate-playground-template --skill autoreview -a claude-code`. Or copy the skill folder (.agents/skills/autoreview in udecode/plate-playground-template) 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 udecode/plate-playground-template --skill autoreview -a codex`. Or copy the skill folder (.agents/skills/autoreview in udecode/plate-playground-template) 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 udecode/plate-playground-template --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 Python, TypeScript, PowerShell and Swift for the scripts in its folder, the command-line tools its instructions call (codex, python3, gh, git and python) and credentials named AMP_API_KEY. Our summary lists: Python 3; Node.js; PowerShell.

Does Autoreview access the network?

SKILL.md names 4 domains. In commands or code: api.openai.com; the agent is likely to contact it when it follows the instructions. As links in the text: developers.openai.com, learn.chatgpt.com and github.com. 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 5.3k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.2k tokens, read only when the agent opens those files.

What are the alternatives to Autoreview?

Skills that share tags, products or a category with Autoreview: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autoreview?

udecode (a GitHub organization) maintains it in udecode/plate-playground-template, which has 240 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on October 7, 2026.

Source: udecode/plate-playground-template on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.