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 rijuvashisht/Magneto --skill reviewgit clone --depth 1 https://github.com/rijuvashisht/MagnetoWrote 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/rijuvashisht/magneto/review)<a href="https://agentmods.dev/skills/rijuvashisht/magneto/review"><img src="https://agentmods.dev/badge/skills/rijuvashisht/magneto/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/rijuvashisht/magneto/review"><img src="https://agentmods.dev/badge/skills/rijuvashisht/magneto/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 | $0.00000 | $0.01070 |
| Opus 5.5 | $0.00000 | $0.00428 |
| Sonnet 5.5 | $0.00000 | $0.00214 |
| Haiku 4.5 | $0.00000 | $0.00107 |
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 2mo 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/review — Graph-Aware Code Review
Role: Staff Engineer
Inputs: current git diff (uncommitted or branch-diff)
Outputs: review-report.md
Next skill: /ship
Audit actions: review.started, review.findings_recorded, review.completed
Why This Skill Exists
Generic AI code review catches surface bugs and misses pattern violations. Magneto's /review is grounded in:
- Your codebase patterns (from
.magneto/memory/) - Your graph topology (god nodes, communities)
- Your past decisions (from audit log + memory)
gstack's /review asks "any bugs?" Magneto asks "does this match pattern X established in community Y?"
The Protocol
Step 1: Gather the diff
git diff HEAD # uncommitted
git diff main...HEAD # branch diff
Or read files the user specifies.
Step 2: Load codebase context
magneto memory show --tags patterns,anti-patterns
magneto graph communities
Identify which community each changed file belongs to. Note any god nodes touched — those deserve extra scrutiny.
Step 3: Review using categories
Run through this checklist (adapted from gstack's /review with Magneto additions):
Category 1: Pattern Fidelity (Magneto-unique)
- Does this change follow the patterns established in community ?
- Is it consistent with past decisions in
.magneto/memory/? - If it introduces a new pattern, is that intentional and documented?
Category 2: Correctness
- Off-by-one, null checks, error handling gaps
- Race conditions, re-entrancy, concurrent access
- Integer overflow, division by zero, resource leaks
Category 3: Completeness (Karpathy Rule 2)
- Is this the simplest possible implementation?
- Any speculative flexibility that wasn't requested?
- Would a senior engineer call this overcomplicated?
Category 4: Surgical Scope (Karpathy Rule 3)
- Does every changed line trace to the user's request?
- Any drive-by "improvements" to unrelated code?
- Unused imports/variables introduced by this change?
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.
- 2mo ago First seen · 161 lines · 0 tokens per session scan A a5874892ec5b
review is a skill published in the GitHub repository rijuvashisht/Magneto (6 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,070 tokens. 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 2026-08-31.
Other skills, from other repositories
git-github-workflow
A guide for collaborating on code with Git and GitHub. Git tracks changes locally, while GitHub hosts repositories and supports pull requests, reviews, and team workflows.
pr
Draft a PR body from OMC's paper trail — the smallest visual that proves the change, evidence consumed from the verify protocol (never re-collected), reversibility from the ADR test on the diff, glossary language throughout.
merge-batch
Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.
otto-pr-review-loop
Run Polly and OCR on a PR until all findings settle.
engram-branch-pr
PR creation workflow for Engram following the issue-first enforcement system. Trigger: When creating a pull request, opening a PR, or preparing changes for review.
github-contributor
A step-by-step guide for contributing code, tests, documentation, or build changes to open-source projects you do not maintain. It explains how to prepare a pull request that follows the project’s rules and is easier for maintainers to review.