agentic-code-review

agentic-code-review is a skill for Claude Code, Codex from DominikTobureto/awesome-grok-build. It costs 38 tokens per session (642 once invoked), scanned A, original, MIT.

A read-only review guide for code changes such as diffs, pull requests, branches, or AI-generated code. It checks behavior, security, regressions, tests, performance, and maintainability before merging.

In plain words
What is it for?
Use it to review correctness and edge cases, authentication and permissions, secrets or injection risks, missing regression tests, database or caching changes, and maintainability concerns.
Why use it?
It helps find concrete failure paths and risks before code reaches users, while keeping fixes separate until someone approves them.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to review correctness and edge cases, authentication and permissions, secrets or injection risks, missing regression tests, database or caching changes, and maintainability concerns.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dominiktobureto/awesome-grok-build/agentic-code-review
Install

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.

Any agent
npx skills add DominikTobureto/awesome-grok-build --skill agentic-code-review
Clone the repo
git clone --depth 1 https://github.com/DominikTobureto/awesome-grok-build

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agentic-code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/dominiktobureto/awesome-grok-build/agentic-code-review/github.svg)](https://agentmods.dev/skills/dominiktobureto/awesome-grok-build/agentic-code-review)
Your own site
<a href="https://agentmods.dev/skills/dominiktobureto/awesome-grok-build/agentic-code-review"><img src="https://agentmods.dev/badge/skills/dominiktobureto/awesome-grok-build/agentic-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.

agentmods 80×15 button for agentic-code-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/dominiktobureto/awesome-grok-build/agentic-code-review"><img src="https://agentmods.dev/badge/skills/dominiktobureto/awesome-grok-build/agentic-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 642 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.00642
Opus 5 $0.00019 $0.00321
Sonnet 5 $0.00008 $0.00128
Haiku 4.5 $0.00004 $0.00064

Measured 12d ago against content hash 1e379586d711, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

agentic-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 12d 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.

.grok/skills/agentic-code-review/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agentic Code Review

Review like a senior engineer who wants the change to ship safely.

Grok Build Mode

  • Use Plan Mode only if the review requires a fix plan. Pure review is read-only.
  • Use subagents for independent passes on risky diffs:
    • correctness: behavior and edge cases.
    • security: auth, permissions, secrets, injection, dependency risk.
    • tests: coverage and missing regression tests.
    • performance: complexity, queries, caching, bundle size.
  • Arena-style review: if Arena Mode is available, compare independent review outputs and keep only findings with concrete failure paths. Otherwise synthesize subagent findings and dedupe.
  • Human-in-the-loop: never auto-apply fixes unless the user explicitly asks. Findings first, fixes after approval.

Review Priority

  1. Correctness bugs.
  2. Security and privacy risks.
  3. Data loss, migrations, auth, payments, permissions, and concurrency.
  4. Missing or weak tests.
  5. Performance regressions.
  6. Developer experience and maintainability.
  7. Style only when it blocks comprehension or violates repo conventions.

Process

  1. Read the diff and the surrounding code.
  2. Identify the intended behavior from the issue, README, tests, or prompt.
  3. Check whether the implementation actually satisfies that behavior.
  4. Look for edge cases and failure modes.
  5. Verify test coverage maps to the risk.
  6. Run narrow verification only if the user asked for fix validation or the repo has cheap checks.
  7. Produce findings first. Keep summary short.

Finding Format

Use this format:

[P1] Title
File: path/to/file.ext:line
Problem: ...
Why it matters: ...
Suggested fix: ...

Severity:

  • P0: must fix before merge, production-breaking or data/security critical.
  • P1: should fix before merge.
  • P2: useful fix, not necessarily blocking.
  • P3: polish or follow-up.

Example Prompts

Use agentic-code-review on the current diff. Findings first. Ignore style unless it creates real risk.

Read the full file on GitHub · 85 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 12d ago First seen · 85 lines · 38 tokens per session scan A 1e379586d711

Subscribe to this mod's changes

agentic-code-review is a skill published in the GitHub repository DominikTobureto/awesome-grok-build (61 stars, last pushed 19d ago), licensed MIT. It adds 38 tokens to every session and 642 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.

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