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.
npx skills add joaquimscosta/arkhe-claude-plugins --skill code-reviewgit clone --depth 1 https://github.com/joaquimscosta/arkhe-claude-pluginsWrote 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/joaquimscosta/arkhe-claude-plugins/code-review)<a href="https://agentmods.dev/skills/joaquimscosta/arkhe-claude-plugins/code-review"><img src="https://agentmods.dev/badge/skills/joaquimscosta/arkhe-claude-plugins/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.
<a href="https://agentmods.dev/skills/joaquimscosta/arkhe-claude-plugins/code-review"><img src="https://agentmods.dev/badge/skills/joaquimscosta/arkhe-claude-plugins/code-review.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 | $0.00085 | $0.01696 |
| Opus 5.5 | $0.00034 | $0.00678 |
| Sonnet 5.5 | $0.00017 | $0.00339 |
| Haiku 4.5 | $0.00009 | $0.00170 |
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 yesterday.
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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Code Review
Pragmatic Quality framework — orchestrate parallel review agents for high-signal findings.
Parse Arguments
- If
$ARGUMENTScontains--post-to-pr: enable GitHub PR posting (Phase 5) - Remaining non-flag arguments: use as output directory (default:
./reviews/code/)
Example usage:
/review:code-review— local report to./reviews/code//review:code-review custom/dir— local report tocustom/dir//review:code-review --post-to-pr— local report + post to GitHub PR/review:code-review custom/dir --post-to-pr— both
Git Analysis
Analyze these outputs to understand the scope and content of the changes.
GIT STATUS:
!`git status`
FILES MODIFIED:
!`git diff --name-only origin/HEAD...`
COMMITS:
!`git log --no-decorate origin/HEAD...`
DIFF CONTENT:
!`git diff --merge-base origin/HEAD`
Phase 1 — Context Gathering
Launch 2 parallel Haiku agents:
Agent A — CLAUDE.md Discovery: Find all CLAUDE.md files in the repo (root + directories modified by the changes). Return file paths and brief content summaries of each.
Agent B — Change Summary: Analyze the diff above. Return: files changed count, primary areas affected, change type (feature/bugfix/refactor/config/test/docs), estimated risk level (Low/Medium/High/Critical).
Phase 2 — Parallel Review
Launch 4-5 Sonnet agents simultaneously. Provide each with: the full diff content, the CLAUDE.md summaries from Phase 1, and the change summary. Each agent returns findings in this format:
Finding: {description}
File: {path}:{line}
Category: {CLAUDE.md | Bug | History | Security | Comments}
Reason: {why this was flagged}
Suggested fix: {code snippet, if applicable}
Reviewer 1 — CLAUDE.md Compliance
Audit changes against all discovered CLAUDE.md rules. Only flag items specifically called out in a CLAUDE.md. Double-check that the CLAUDE.md actually requires what is being flagged. Ignore silenced rules (lint-ignore comments).
What ships with it
3 files 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.
- yesterday First seen · 161 lines · 85 tokens per session scan A a90b9a12f55e
code-review is a skill published in the GitHub repository joaquimscosta/arkhe-claude-plugins (21 stars, last pushed yesterday), licensed MIT. It adds 85 tokens to every session and 1,696 once invoked, about $0.0003 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 2026-10-16.
Other skills, from other repositories
pr-creator
Create well-structured pull requests with proper descriptions and labels.
dev-rc
The last gate before merge — QA, security audit, review and changelog, with objective pass conditions.
pr-autofix
When the user has PR review feedback to address, CI failures to fix, or wants to automatically resolve common PR issues. Use when the user says "fix the PR," "address review comments," "CI is failing," "fix the build," "autofix," "pr feedback," "resolve comments," "fix merge conflicts," or when a PR needs updates…
babysit
Watch a pull request or review cycle until it is ready to merge. Use when asked to babysit, monitor, or keep checking PR comments, reviews, and CI until all actionable issues are resolved.
github
GitHub via gh CLI: PRs, issues, reviews, repos, auth.
feedback-builder
Point Aeon at a service's /feedback endpoint - pull what agents reported (bugs, missing features), triage and cluster it, then build the best accepted request as a PR for a human to approve.