What is app-review-prep?

A pre-submission check for Meta App Review, the process Meta uses to approve an app's requested permissions and features.

Use it to prepare an app for review and identify anything that could prevent approval.

It reveals missing requirements, current privileges, review status, and past submission results before you submit.

app-review-prep — Skill for Claude Code and Codex by facebook: A pre-submission check for Meta App Review, the process…

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/facebook/agentic-tools/app-review-prep

Any agent

npx skills add facebook/agentic-tools --skill app-review-prep

Clone the repo

git clone --depth 1 https://github.com/facebook/agentic-tools

Made for: Claude Code, Codex.

Or install devtools, the plugin that ships this one along with the rest of its 9 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/facebook/agentic-tools/app-review-prep.svg)](https://agentmods.dev/skills/facebook/agentic-tools/app-review-prep)

Your own site

<a href="https://agentmods.dev/skills/facebook/agentic-tools/app-review-prep"><img src="https://agentmods.dev/badge/skills/facebook/agentic-tools/app-review-prep.svg" alt="Measured on agentmods" height="20"></a>

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 819 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00035	$0.00819
Opus 5	$0.00017	$0.00409
Sonnet 5	$0.00007	$0.00164
Haiku 4.5	$0.00003	$0.00082

Measured 4d ago against content hash 3407b01772af, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

app-review-prep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/devtools/skills/app-review-prep/SKILL.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

App Review Prep

Check everything needed before submitting a Meta app for App Review.

Workflow

Start tracking. Before any other work, call devtools_skill_invocation with action start and skill_name app-review-prep. Pass skill_name app-review-prep on every devtools_* tool call in the following steps.
Identify the app. Ask the user for the app name or ID. If they give a name (or aren't sure of the ID), call devtools_app_list (action list) and resolve it to an app_id — match the name case-insensitively. If several apps match or it's ambiguous, show the candidates (name, ID, viewer role) and ask the user to pick. If they give a numeric ID, use it directly.
Collect review data in parallel. Run all calls concurrently:
- devtools_app with action basic_settings — app name, category, status for report context
- devtools_app_review with action status — current review state
- devtools_app_review with action requirements — what's needed for approval
- devtools_app_review with action privileges — currently granted permissions/features
- devtools_app_review with action history — past submissions and their outcomes
- devtools_compliance with action status — compliance blockers that could prevent approval
Analyze readiness. Evaluate:
- Are all required items complete?
- Are there compliance violations that would block review?
- Have previous submissions been rejected? If so, what was the reason?
- Which permissions/features are already approved vs. still needed?
Produce a readiness report:

Report Format

Current Status
- Review state (e.g., not submitted, in review, approved, rejected)
- Last submission date and outcome (if any)
Granted Privileges
- List of approved permissions and features
Outstanding Requirements
- Each requirement with its completion status
- Clear description of what's needed to fulfill incomplete items

Read the full file on GitHub · 70 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

4d ago First seen · 70 lines · 35 tokens per session scan A 3407b01772af

Subscribe to this mod's changes

app-review-prep is a skill published in the GitHub repository facebook/agentic-tools (6 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 819 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Code review Planning

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens