Borrowing it
Nothing to install: this file belongs to pavel-molyanov/molyanov-ai-dev. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pavel-molyanov/molyanov-ai-dev/main/.agents/skills/code-reviewing/SKILL.mdgit clone --depth 1 https://github.com/pavel-molyanov/molyanov-ai-devWrote 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/pavel-molyanov/molyanov-ai-dev/code-reviewing)<a href="https://agentmods.dev/skills/pavel-molyanov/molyanov-ai-dev/code-reviewing"><img src="https://agentmods.dev/badge/skills/pavel-molyanov/molyanov-ai-dev/code-reviewing/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/pavel-molyanov/molyanov-ai-dev/code-reviewing"><img src="https://agentmods.dev/badge/skills/pavel-molyanov/molyanov-ai-dev/code-reviewing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 127 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00067 | $0.01682 |
| Opus 5.5 | $0.00027 | $0.00673 |
| Sonnet 5.5 | $0.00013 | $0.00336 |
| Haiku 4.5 | $0.00007 | $0.00168 |
Grade A, and why
code-reviewing 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 6d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewing
Function length, nesting, broad types, hardcoded values, repeated resource construction, or multiple mocks are signals to investigate; they are not defects by themselves.
Within this lane, the reviewer may challenge correctness, complexity, architecture, or the chosen approach and propose a useful simpler or stronger alternative. Ground findings and proposals in the request, project evidence, or a concrete consequence; unsupported preference is not useful, and a clean result is complete.
Contents
Always Review
Requirements and Correctness
- Trace the changed behavior to the user request or user-spec.
- Check happy paths, specified edge cases, failures, and state changes that the change owns.
- Identify behavior that was added accidentally or requested behavior that is missing.
- Separate new regressions from unrelated pre-existing problems.
Scope, Necessity, and Simplicity
- Trace each added behavior, validation, fallback, branch, state, abstraction, dependency, and configuration option to a current requirement, project contract, or realistic condition in the present codebase.
- Report unrequested machinery only when it expands behavior or creates concrete maintenance, correctness, performance, or testing cost. Fewer lines are not automatically simpler; compare responsibilities, states, branches, dependencies, and concepts the solution introduces.
- Check whether the project already has a direct capability that satisfies the requirement. An abstraction used once is acceptable when it expresses a real boundary; it is a problem when it adds indirection or generality without a current use.
- Evaluate the chosen algorithm against realistic input size and project constraints. Report avoidable complexity only when an existing project capability or direct requirement proves it unnecessary and the current choice has a demonstrable consequence. When useful, describe a simpler or stronger alternative without turning speculative optimization into a finding.
- Treat handling for extremely unlikely cases as a defect only when no requirement or realistic path justifies it and the extra handling materially complicates the normal path.
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.
- 6d ago Changed · +7 lines · +15 tokens per session 7a852c6905d6
- 1mo ago First seen · 157 lines · 52 tokens per session scan A f0ab362206ee
code-reviewing is a skill published in the GitHub repository pavel-molyanov/molyanov-ai-dev (300 stars, last pushed 6d ago), licensed MIT. It adds 67 tokens to every session and 1,682 once invoked, about $0.0003 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.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
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"…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…