review

review is a skill for Claude Code, Codex from fall-out-bug/sdp. It costs 25 tokens per session (2,555 once invoked), scanned A, original, MIT.

A code review that asks separate specialists to check quality, security, deployment, reliability, technical design, documentation, and prompts.

In plain words
What is it for?
It helps review code changes, especially files involving authentication, encryption, automated builds, databases, or migrations.
Why use it?
It brings several kinds of review into one assessment and adjusts the depth of checking to the file size and risk.

Skill for Claude CodeCodex

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/fall-out-bug/sdp/review
Any agent
npx skills add fall-out-bug/sdp --skill review
Clone the repo
git clone --depth 1 https://github.com/fall-out-bug/sdp

Made for: Claude Code, Codex.

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.

agentmods badge for review

README.md
[![agentmods](https://agentmods.dev/badge/skills/fall-out-bug/sdp/review.svg)](https://agentmods.dev/skills/fall-out-bug/sdp/review)
Your own site
<a href="https://agentmods.dev/skills/fall-out-bug/sdp/review"><img src="https://agentmods.dev/badge/skills/fall-out-bug/sdp/review.svg" alt="Measured on agentmods" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,555 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.

ModelPer sessionOnce invoked
Fable 5 $0.00025 $0.02555
Opus 5 $0.00013 $0.01277
Sonnet 5 $0.00005 $0.00511
Haiku 4.5 $0.00003 $0.00255

Measured 3d ago against content hash 504da7179d99, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

review 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 3d 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.

prompts/skills/review/SKILL.md · 216 lines

How it starts

The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.

review

CLI: sdp quality all | LLM: Spawn all 7 specialist subagents with risk-based depth allocation

Comprehensive multi-agent quality review. All 7 reviewers always spawned; risk patterns determine depth, not presence.


All 7 Reviewers Always Spawned

Base contract: qa, security, devops, sre, techlead, docs, promptops — always present in verdict JSON.

Risk patterns determine review depth, not reviewer presence.

Risk-Based Depth Allocation

LOC tiers set baseline depth; risk patterns override for specific files:

LOC tiers (baseline):

LOC Range Deep Reviewers
< 50 qa, techlead
50–200 qa, security, techlead
> 200 all 7

Risk patterns (additive override by file path):

Pattern Extra Deep Reviewers
**/auth/**, **/crypto/** security, qa
**/.github/workflows/**, **/ci/** devops, sre
**/migrations/**, **/db/** sre, security

Full config: .sdp/config.yml under review section.

Flag Overrides

Flag Behavior
--full All 7 reviewers with full depth
--quick All 7 reviewers, but only 2-3 do deep review (rest rubber-stamp)

EXECUTE THIS NOW

When user invokes @review F{XX}:

  1. Run CLI: sdp quality all
  2. Determine depth: Match risk patterns to identify which reviewers get deep focus (rest get rubber-stamp).
  3. Spawn all 7 subagents IN PARALLEL (use your platform's subagent spawn). DO NOT skip.

All 7 roles always spawned: qa, security, devops, sre, techlead, docs, promptops

Per-subagent task template (replace F{XX}, round-N, {role}):

5-step evaluation structure:

  1. SCOPE: What files/packages does this feature touch?
  2. RISK MAP: Top 3 risk areas for your domain ({role}) in this scope?
  3. EVIDENCE: For each risk, what did you find? (file:line, test name, config entry)
  4. SEVERITY: P0 = exploitable in production. P1 = breaks on edge case. P2 = maintenance debt. P3 = style.
  5. VERDICT: PASS if max severity ≤ P2. FAIL if any P0/P1.

Read the full file on GitHub · 216 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.

  1. 3d ago First seen · 216 lines · 25 tokens per session scan A 504da7179d99

Subscribe to this mod's changes

review is a skill published in the GitHub repository fall-out-bug/sdp (19 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 2,555 once invoked, about $0.0001 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 2026-08-30.