code-review

code-review is a skill for Codex from jrobelia/inventree-plugin-ai-toolkit. It costs 94 tokens per session (1,510 once invoked), scanned A, a copy of code-review, MIT.

A code-review workflow that compares your current changes with a commit, branch, tag, or other Git reference. It checks both the repository's coding rules and the requested feature specification.

In plain words
What is it for?
Reviewing a Git diff against a chosen starting point, locating the originating issue or product specification, and producing side-by-side standards and specification findings.
Why use it?
It separates code-quality checks from checking whether the requested work was actually implemented. This helps reveal standards problems and missing or incorrect requirements in the same change.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions subagents; installed under .agents/ (shared by several agents).

Good fit Reviewing a Git diff against a chosen starting point, locating the originating issue or product specification, and producing side-by-side standards and specification findings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jrobelia/inventree-plugin-ai-toolkit/code-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 jrobelia/inventree-plugin-ai-toolkit --skill code-review
Clone the repo
git clone --depth 1 https://github.com/jrobelia/inventree-plugin-ai-toolkit

Made for: 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 code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/jrobelia/inventree-plugin-ai-toolkit/code-review/github.svg)](https://agentmods.dev/skills/jrobelia/inventree-plugin-ai-toolkit/code-review)
Your own site
<a href="https://agentmods.dev/skills/jrobelia/inventree-plugin-ai-toolkit/code-review"><img src="https://agentmods.dev/badge/skills/jrobelia/inventree-plugin-ai-toolkit/code-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 code-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/jrobelia/inventree-plugin-ai-toolkit/code-review"><img src="https://agentmods.dev/badge/skills/jrobelia/inventree-plugin-ai-toolkit/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,510 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 88% copy Near-identical to another mod 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 $0.00094 $0.01510
Opus 5.5 $0.00038 $0.00604
Sonnet 5.5 $0.00019 $0.00302
Haiku 4.5 $0.00009 $0.00151

Measured 1mo ago against content hash 47f4e52c2169, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

code-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 1mo 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.

Origin

This is a copy

88% identical to code-review — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/code-review/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Two-axis review of the diff between HEAD and a fixed point the user supplies:

  • Standards: does the code conform to this repo's documented coding standards?
  • Spec: does the code faithfully implement the originating issue / spec?

Both axes run as parallel sub-agents so they don't pollute each other's context, then this skill aggregates their findings.

The issue tracker should have been provided to you. If docs/agents/issue-tracker.md is missing, tell the user to run /setup-matt-pocock-skills.

Process

1. Pin the fixed point

Whatever the user said is the fixed point (a commit SHA, branch name, tag, main, HEAD~5, etc.). If they didn't specify one, ask for it.

Capture the diff command once: git diff <fixed-point>...HEAD (three-dot, so the comparison is against the merge-base). Also note the list of commits via git log <fixed-point>..HEAD --oneline.

Before going further, confirm the fixed point resolves (git rev-parse <fixed-point>) and the diff is non-empty. A bad ref or empty diff should fail here, not inside two parallel sub-agents.

2. Identify the spec source

Look for the originating spec, in this order:

  1. Issue references in the commit messages (#123, Closes #45, GitLab !67, etc.), fetched via the workflow in docs/agents/issue-tracker.md.
  2. A path the user passed as an argument.
  3. A spec file under docs/, specs/, or .scratch/ matching the branch name or feature.
  4. If nothing is found, ask the user where the spec is. If they say there isn't one, the Spec sub-agent will skip and report "no spec available".

3. Identify the standards sources

Anything in the repo that documents how code should be written, such as CODING_STANDARDS.md or CONTRIBUTING.md.

On top of whatever the repo documents, the Standards axis always carries the smell baseline below: a fixed set of Fowler code smells (Refactoring, ch.3) that applies even when a repo documents nothing. Two rules bind it:

  • The repo overrides. A documented repo standard always wins; where it endorses something the baseline would flag, suppress the smell.
  • Always a judgement call. Each smell is a labelled heuristic ("possible Feature Envy"), never a hard violation. Like any standard here, skip anything tooling already enforces.

Read the full file on GitHub · 88 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago Changed · -2 lines · -2 tokens per session 47f4e52c2169
  2. 1mo ago First seen · 90 lines · 96 tokens per session scan A 6a65cc61114f

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository jrobelia/inventree-plugin-ai-toolkit (1 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,510 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to code-review, differing in 8 lines, and is treated as a copy.

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