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/itallstartedwithaidea/agent-skills/code-reviewnpx skills add itallstartedwithaidea/agent-skills --skill code-reviewgit clone --depth 1 https://github.com/itallstartedwithaidea/agent-skillsWrote 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/itallstartedwithaidea/agent-skills/code-review)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/code-review"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/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 | $0.00030 | $0.01216 |
| Opus 5 | $0.00015 | $0.00608 |
| Sonnet 5 | $0.00006 | $0.00243 |
| Haiku 4.5 | $0.00003 | $0.00122 |
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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Part of Agent Skills™ by googleadsagent.ai™
Description
Code Review enforces a structured pre-merge quality gate with a checklist-driven evaluation, severity-classified findings, and mandatory resolution tracking. The agent reviews every diff against a configurable set of quality dimensions before approving changes, ensuring consistent standards regardless of reviewer fatigue or time pressure.
Unlike ad-hoc review comments, this skill produces a standardized review document with findings categorized by severity: Critical (must fix before merge), High (should fix before merge), Medium (fix in follow-up), and Low (optional improvement). Each finding includes the file, line range, category, description, and a concrete suggested fix. The review document becomes part of the permanent record.
The pre-review checklist catches common oversights before deep analysis begins: missing tests, uncommitted files, linter errors, type errors, and documentation gaps. Only after the checklist passes does the agent proceed to semantic review of logic, architecture, security, and performance.
Use When
- A pull request or diff is ready for review
- The user asks for feedback on code changes
- Before merging any branch into main
- After a subagent completes a task (Stage 2 review)
- Code has been refactored and needs validation
- A new contributor's code needs onboarding-level review
How It Works
graph TD
A[Receive Diff] --> B[Pre-Review Checklist]
B --> C{Checklist Passes?}
C -->|No| D[Return with Blockers]
C -->|Yes| E[Semantic Analysis]
E --> F[Classify Findings by Severity]
F --> G[Generate Review Document]
G --> H{Critical Findings?}
H -->|Yes| I[Request Changes]
H -->|No| J{High Findings?}
J -->|Yes| K[Approve with Reservations]
J -->|No| L[Approve]
The workflow gates progression: the checklist catches mechanical issues instantly, while semantic analysis evaluates design, correctness, and maintainability. The severity classification ensures critical issues block the merge while minor improvements do not.
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 · 127 lines · 30 tokens per session scan A bec0c320d208
code-review is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (36 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 1,216 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
update-saasmail
Sync the local saasmail repo with the latest upstream changes from https://github.com/choyiny/saasmail. Use this skill whenever the user wants to update saasmail, pull upstream changes, sync with upstream, rebase on upstream, get the latest saasmail, or says "/update-saasmail". Handles adding the upstream remote if…
tracking-health
Preventive audit of conversion tracking across all configured ad platforms — Meta pixels + CAPI, Google Ads conversion actions, and final-URL tracking-parameter consistency on every platform — with a GA4 cross-check. Use when the user asks to check tracking, audit conversion measurement, verify pixels / tags, check…
story-html-publisher
Final step of the AI Storybook pipeline. Consolidates the scenes, images, and per-scene audio into ONE self-contained HTML storybook — a swipe/tap player with every image and audio clip embedded as base64 so the single file works offline and can be shared as-is. Reads {slug}scenes.json, {slug}images.json, and…
learn
Save a marketing diagnosis insight to the pro-diagnosis knowledge base so it is applied in future operations across all platforms. Use when the user runs /learn, explicitly teaches the agent a marketing insight, corrects the agent's analysis, or asks to remember/record an operational learning for next time. Also use…
sealeap-amazon-product-targeting
Research, diagnose, and draft Amazon Ads ASIN and category product-targeting plans that complement keyword targeting, including audience expansion, competitor and category traffic, cross-sell, upsell, self-defense, negative targeting, placement analysis, and single-variable experiments. Use for 商品投放, ASIN 定向, 品类定向…
weekly-report
Generate a weekly summary report across all platforms. Use when the user asks for a weekly report, summary, recap, end-of-week review, or weekly digest. Also use when the user asks in Japanese (週次レポート / 今週のまとめ / 週報を作成して).