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 cosmix/loom --skill loom-code-reviewgit clone --depth 1 https://github.com/cosmix/loomWrote 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/cosmix/loom/loom-code-review)<a href="https://agentmods.dev/skills/cosmix/loom/loom-code-review"><img src="https://agentmods.dev/badge/skills/cosmix/loom/loom-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/cosmix/loom/loom-code-review"><img src="https://agentmods.dev/badge/skills/cosmix/loom/loom-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00022 | $0.01098 |
| Opus 5.5 | $0.00009 | $0.00439 |
| Sonnet 5.5 | $0.00004 | $0.00220 |
| Haiku 4.5 | $0.00002 | $0.00110 |
Grade A, and why
loom-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.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
Review the requested change and its affected behavior. Prioritize correctness, security, expected load, useful tests and maintainability. Give concrete findings and the smallest adequate repairs; disclose consequential gaps in what you checked.
Method
- Read intent and locate the affected flow: entry points, callers/callees, state, tests, fixtures and config. Use source-graph queries where available, then targeted reads.
- Trace success and failure paths beyond the diff. Confirm new preconditions are enforced and changed defaults, signatures or formats reach every consumer.
- Establish expected load from repository/deployment evidence. Check concurrency and resource growth against it; state assumptions that affect a finding.
- Apply the four lenses below, including missing tests and error handling.
- Reconfirm each finding: the caller exists, the input/state is possible, and the cited code produces the consequence. Include a concrete scenario or violated requirement and a repair.
For a requested repository audit, map entry points and data flows first, then inspect the highest-risk paths deeply. Report unexamined areas; do not page every file.
Four lenses
- Correctness: boundary values, parsing, cleanup on early returns, idempotency, retry safety, races, lock ordering and compatibility. Follow errors through callers.
- Security: validation at trust boundaries, authorization, injection, secret exposure and
data-loss prevention. Preserve these and accessibility when simplifying. For substantive
auth/crypto/payment or trust-boundary changes, use
loom-security-auditorloom-threat-modelthroughloom-skills; a routine review does not establish security assurance. - Performance: unbounded state, query-per-item patterns, repeated work and allocation on hot paths. Ground scale claims in expected inputs and deployment; speculative scale is a suggestion.
- Maintainability: remove unnecessary options, wrappers and duplication. Prefer an existing project facility, then standard/platform facilities or installed dependencies. Name the reuse target and verify its contract. Search callers, config and dynamic references before declaring code unused. Preserve project boundaries; avoid abstraction for hypothetical consumers.
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 Changed · +1 lines f0bdce4eaf01
- 11d ago Changed · -51 lines 466e8c802aa7
- 1mo ago Changed · -20 tokens per session e88493bd6892
- 1mo ago First seen · 161 lines · 42 tokens per session scan A 580d4857b1fc
loom-code-review is a skill published in the GitHub repository cosmix/loom (64 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 1,098 once invoked, about $0.0001 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 2026-08-30.
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