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 jsmastery-pro/jsm-agent-skill --skill reviewgit clone --depth 1 https://github.com/jsmastery-pro/jsm-agent-skillWrote 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/jsmastery-pro/jsm-agent-skill/review)<a href="https://agentmods.dev/skills/jsmastery-pro/jsm-agent-skill/review"><img src="https://agentmods.dev/badge/skills/jsmastery-pro/jsm-agent-skill/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/jsmastery-pro/jsm-agent-skill/review"><img src="https://agentmods.dev/badge/skills/jsmastery-pro/jsm-agent-skill/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00039 | $0.01054 |
| Opus 5 | $0.00019 | $0.00527 |
| Sonnet 5 | $0.00008 | $0.00211 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building is not done when the code runs. It is done when the code is correct.
AI moves fast. Fast means things get built that work on the surface but drift from the architecture, violate the design system, or miss edge cases that matter. This skill catches those things before they compound into bigger problems.
Run this after every feature. Before you move on.
What This Skill Does Not Do
It does not fix anything. It reports what it finds and lets the developer decide what matters and what to do about it. Fixing without understanding is how problems get buried, not solved.
Step 1 — Understand What Should Have Been Built
Before reviewing anything, establish the benchmark.
Read in this order:
- The implementation plan from
/architectif one exists - The feature description or task that was given
- Any relevant context files — architecture boundaries, code standards, design rules
If no plan exists, ask the developer to describe what the feature was supposed to do before reviewing. You cannot verify correctness without knowing what correct looks like.
Step 2 — Review in Three Layers
Layer 1 — Does it match the plan?
Compare what was built against what was planned.
Check:
- Every part of the feature description — is it all there?
- The decisions made during planning — are they reflected in the code?
- The scope — did the implementation stay within bounds or add things that were not asked for?
Flag anything that was planned but missing. Flag anything that was built but not planned.
Layer 2 — Does it respect the system?
This is where AI drift most commonly happens. The feature works, but it violates rules that the project depends on.
Check:
- Architecture boundaries — does code in the right place own the right responsibilities? No UI logic in API routes. No DB calls in components. Whatever the project's boundaries are — are they respected?
- Design system — are the correct tokens, classes, and patterns used? Any hardcoded values that should be variables? Any raw color classes that should use the design system?
- Code standards — naming conventions, file organisation, TypeScript strictness, error handling patterns — do they match what the project established?
- Existing patterns — does this feature introduce a new pattern when an existing one should have been used?
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.
- 9d ago First seen · 143 lines · 39 tokens per session scan A 8feb4fcccd5c
review is a skill published in the GitHub repository jsmastery-pro/jsm-agent-skill (170 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 1,054 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 2026-08-30.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…