Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add AppsVortex/arness/plugin install arn-codeWrote 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.
[](https://agentmods.dev/skills/appsvortex/arness/arn-code-review-implementation)<a href="https://agentmods.dev/skills/appsvortex/arness/arn-code-review-implementation"><img src="https://agentmods.dev/badge/skills/appsvortex/arness/arn-code-review-implementation/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.
<a href="https://agentmods.dev/skills/appsvortex/arness/arn-code-review-implementation"><img src="https://agentmods.dev/badge/skills/appsvortex/arness/arn-code-review-implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.1 | $0.00105 | $0.03389 |
| Opus 5.5 | $0.00042 | $0.01356 |
| Sonnet 5.5 | $0.00021 | $0.00678 |
| Haiku 4.5 | $0.00011 | $0.00339 |
Grade A, and why
arn-code-review-implementation 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.
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Review Implementation
Post-execution quality gate that verifies an implementation matches the project plan and follows the stored code patterns, testing patterns, and architecture documentation. Reports issues classified as ERRORS, WARNINGS, and INFO, with a final verdict.
Pipeline position:
arn-code-init -> arn-code-feature-spec / arn-code-bug-spec -> arn-code-plan -> arn-code-save-plan -> arn-code-review-plan -> arn-code-taskify -> arn-code-execute-plan -> **arn-code-review-implementation**
This is a self-contained review -- it does not invoke sub-agents. The review is a structured, checklist-driven process: read code, compare to patterns, classify findings.
When to Use:
- After
arn-code-execute-plancompletes - To verify that implementation matches the plan and follows stored patterns
- Before considering the project done
- Optional -- skip for small/straightforward projects; recommended for 3+ phases, multiple modules, or critical features
Prerequisites
If no ## Arness section exists in the project's CLAUDE.md, inform the user: "Arness is not configured for this project yet. Run /arn-implementing to get started — it will set everything up automatically." Do not proceed without it.
Workflow
Step 1: Load the Project
- Read the project's CLAUDE.md and extract the
## Arnesssection to find:- Plans directory path
- Code patterns directory path
- Ask for
PROJECT_NAMEif not provided in the trigger message - Read all project artifacts:
<project-folder>/INTRODUCTION.md-- codebase patterns that should have been followed- All reports in
<project-folder>/reports/-- what was implemented, tested, bugs fixed - All phase plans in
<project-folder>/plans/-- acceptance criteria, expected files, expected patterns
- Read stored pattern documentation:
<code-patterns-dir>/code-patterns.md<code-patterns-dir>/architecture.md<code-patterns-dir>/testing-patterns.md<code-patterns-dir>/ui-patterns.md(if it exists)<code-patterns-dir>/security-patterns.md(if it exists)
- Build a review checklist from these sources: files that should exist, patterns that should have been applied, features that were planned, tests that should pass
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.
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.
- 1mo ago First seen · 286 lines · 105 tokens per session scan A 94cc32120d0a
arn-code-review-implementation is a skill published in the GitHub repository AppsVortex/arness (33 stars, last pushed 2mo ago), licensed MIT. It adds 105 tokens to every session and 3,389 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
critical-code-reviewer
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
brooks-sweep
Full-sweep mode: runs a unified analysis across all quality dimensions — code decay, architecture, tech debt, and test quality — then applies fixes directly to the codebase. Safe changes are auto-applied; risky changes are confirmed before execution. Drawing on twelve classic engineering books. Triggers when: user…
triage
Triages a PR comment — from a bot (Copilot, CI) or a human reviewer. Routes to the triagehelper.py helper for identity checks, thread snapshotting, isolated-worktree fixes, and publish/reply/resolve mechanics; you classify the finding and apply a justified fix. --dry-run is fully read-only (investigation and a printed…
pr-review
Comprehensive PR review across six dimensions — cost impact, environment drift, ownership gaps, SOC 2 compliance, deprecated API / version hygiene, and rollback feasibility. Each mode inspects the diff and current file state, reports findings with severity, and recommends concrete fixes. Use when preparing a PR for…
genshijin-review
A compact pull-request review format that writes one actionable finding per line in Japanese or English.
pr-review
Use when the user wants to deeply review a pull/merge request or a local diff — "review this PR", "grill the MR", "stress-test this diff", "deep-dive code review", "code review before merge", or when they name a PR/MR URL, number, branch, commit range (A..B/A...B), tag or SHA. Works on GitHub (gh), GitLab (glab), or a…