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 agentmods add skills/notforyou23/home23/code-reviewnpx skills add notforyou23/home23 --skill code-reviewgit clone --depth 1 https://github.com/notforyou23/home23Wrote 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/notforyou23/home23/code-review)<a href="https://agentmods.dev/skills/notforyou23/home23/code-review"><img src="https://agentmods.dev/badge/skills/notforyou23/home23/code-review.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00377 |
| Opus 5 | $0.00013 | $0.00188 |
| Sonnet 5 | $0.00005 | $0.00075 |
| Haiku 4.5 | $0.00003 | $0.00038 |
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 5d 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.
What it actually says
Code Review
Use this skill when the task is to review a change set rather than implement new code.
When to use
Use code-review for:
- pull requests
- diffs and patches
- “find bugs” asks
- regression and test-gap inspection
Workflow
- Read the changed surface first.
- Look for behavioral regressions before style issues.
- Check invariants, data flow, edge cases, and failure handling.
- Note missing tests only when they would likely catch a real bug.
- Output findings first, ordered by severity.
Output
- File or surface reference
- Concrete risk
- Why it matters
- Minimal fix or follow-up
Gotchas
- Do not praise obvious code. Findings come first.
- Do not turn review into a rewrite proposal unless the change is fundamentally wrong.
- If no findings exist, say that plainly and note residual risk or test gaps.
Examples
Review this PR for regressions and missing tests.
Focus on the changed files only.
Call out the highest-severity bugs first with file references.
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.
- 5d ago First seen · 67 lines · 26 tokens per session scan A 3c2e2d474b4d
Code Review is a skill published in the GitHub repository notforyou23/home23 (11 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 377 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.
Other skills, from other repositories
pr-reviewer
AI-powered pull request review and summarization - analyze diffs, detect bugs, security issues, suggest improvements, generate PR summaries, and auto-comment on GitHub PRs.
pr-triage
PR triage: audit open PRs, deep review selected ones, draft and post review comments. Args: "all" to review all, PR numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.
statsmodels
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use…
scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for…
claude_code_delegation
Delegate coding, review, and refactor tasks to Claude Code CLI through Row-Bot's approval-gated shell workflow.
pr-review
Review a GitHub pull request and post one formal review — advance the existing discussion and give precision-first, high-signal feedback. Judgement on the diff, not a build gate — CI validates that it builds, and a targeted probe is allowed as evidence. Use when asked to review a PR or on a cron PR scan.