fanout-review

fanout-review is a skill for Claude Code, Codex from nortonx/ai-tooling-free. It costs 62 tokens per session (3,847 once invoked), scanned A, original, MIT.

A code-review tool that asks six reviewers to examine changes from different perspectives, then combines their findings into a merge decision.

In plain words
What is it for?
Use it to review branch, staged, committed, or all current changes and produce a review comment for GitHub, GitLab, or Azure DevOps.
Why use it?
It helps uncover bugs, compatibility risks, and breaking changes before code is merged into the shared project.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to review branch, staged, committed, or all current changes and produce a review comment for GitHub, GitLab, or Azure DevOps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nortonx/ai-tooling-free/fanout-review
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.

Any agent
npx skills add nortonx/ai-tooling-free --skill fanout-review
Clone the repo
git clone --depth 1 https://github.com/nortonx/ai-tooling-free

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 fanout-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/nortonx/ai-tooling-free/fanout-review/github.svg)](https://agentmods.dev/skills/nortonx/ai-tooling-free/fanout-review)
Your own site
<a href="https://agentmods.dev/skills/nortonx/ai-tooling-free/fanout-review"><img src="https://agentmods.dev/badge/skills/nortonx/ai-tooling-free/fanout-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for fanout-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/nortonx/ai-tooling-free/fanout-review"><img src="https://agentmods.dev/badge/skills/nortonx/ai-tooling-free/fanout-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,847 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00062 $0.03847
Opus 5 $0.00031 $0.01924
Sonnet 5 $0.00012 $0.00769
Haiku 4.5 $0.00006 $0.00385

Measured 12d ago against content hash ad9992338576, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fanout-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 12d 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.

skills/fanout-review/SKILL.md · 278 lines

How it starts

The opening of the file, as written. The whole thing — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code review (multi-perspective)

Review the current branch by dispatching 6 parallel review tasks (one mandate each), then synthesize findings into a categorical merge-gate decision (🚫 BLOCK / ⚠️ NEEDS DISCUSSION / ✅ CLEAR) backed by a risk profile and deterministic blocker checks. Output is designed to paste directly into a PR thread (GitHub, GitLab, or Azure DevOps).

Mode detection

Read the user's invocation prompt:

  • Contains quick, shallow, or fastsingle-pass mode (skip fanout, lean output)
  • Diff size below ~50 lines AND mode not explicitly stated → ask the user once whether to do single-pass; default to single-pass if unanswered
  • Otherwise → fanout mode (default)

Scope detection

Read the user's invocation prompt:

  • Mentions committed, this branch, PR, or main..HEAD → scope to committed changes only (git diff main..HEAD)
  • Mentions staged → scope to git diff --cached
  • Otherwise → include all changes (committed + staged + unstaged)

Base branch resolution, in order:

  1. If the user's prompt names a branch (vs feat/X, against develop), use it.
  2. Else git symbolic-ref refs/remotes/origin/HEAD if it resolves.
  3. Else main if it exists locally, else master.
  4. If neither exists, stop and ask the user which base to diff against. Do not silently fall back to HEAD~1.

Fanout mode

Step 1 — Gather diff context

Run (substitute scope from detection above):

git diff <scope> --stat
git diff <scope> --name-status
git log <scope> --no-merges --pretty=format:'%h %s'

Step 2 — Dispatch 6 review tasks in parallel

Use the Agent tool with subagent_type: general-purpose. Send all 6 in a single message with 6 tool calls so they run concurrently. Each subagent receives the diff context, one task description below, and the JSON return contract.

Task descriptions (each subagent receives exactly one):

  • security — Find OWASP Top 10 violations, secrets in code, input-validation gaps, authz/authn gaps, injection (SQL/cmd/path), unsafe deserialization, and weak crypto. Use category: "security" in the return.
  • performance — Find hotspots, N+1 queries, unnecessary work in loops, allocation pressure, blocking I/O on hot paths, and missing memoization. Use category: "performance".
  • breaking-change — For each file in the diff, run git show <base-branch>:<path> and compare against the new version. Flag changed function signatures (added/removed/renamed params, changed return type), removed exports, schema/migration changes, env-var removals, feature-flag removals, behavior changes in public APIs, and removed config keys. Use category: "breaking-change".
  • test-coverage — Find missing tests for new code paths, untested edge cases (empty/null/boundary), redundant tests, and test-quality issues (AAA, FIRST, flakiness, mocks of external deps). Use category: "test".
  • readability — Find naming inconsistencies vs project conventions, unclear variable/function names, missing or excessive comments, magic numbers, and excessive function length/complexity. Use category: "readability".
  • dry-solid — Find DRY violations (3+ similar implementations), SOLID violations, dead code, antipatterns, and leaky abstractions. DRY is project priority — weight findings here higher than readability. Use category: "dry-solid". When a DRY violation spans multiple files, set location to the canonical file (the natural home for the extracted abstraction, else first by path order) and list the remaining occurrences in duplicate_locations — do not concatenate multiple file:lines into one location string.

Read the full file on GitHub · 278 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. 12d ago First seen · 278 lines · 62 tokens per session scan A ad9992338576

Subscribe to this mod's changes

fanout-review is a skill published in the GitHub repository nortonx/ai-tooling-free (1 stars, last pushed 28d ago), licensed MIT. It adds 62 tokens to every session and 3,847 once invoked, about $0.0003 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.

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