ralphctl-code-review-and-quality

ralphctl-code-review-and-quality is a skill for Claude Code, Codex from lukas-grigis/ralphctl. It costs 76 tokens per session (4,617 once invoked), scanned A, original, MIT.

A framework for reviewing code for correctness, architecture, readability, and security. It is especially intended for checking code produced by an AI before approval.

In plain words
What is it for?
Use it during planning, refinement, execution, and final review. It helps decide whether a change improves the codebase and whether its risks are serious enough to block approval.
Why use it?
AI-generated code can look convincing while containing problems. Reviewing changes at their boundaries makes issues cheaper to find than after a large change is complete.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it during planning, refinement, execution, and final review. It helps decide whether a change improves the codebase and whether its risks are serious enough to block approval.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality
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 lukas-grigis/ralphctl --skill ralphctl-code-review-and-quality
Clone the repo
git clone --depth 1 https://github.com/lukas-grigis/ralphctl

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 ralphctl-code-review-and-quality

README.md
[![agentmods](https://agentmods.dev/badge/skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality/github.svg)](https://agentmods.dev/skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality)
Your own site
<a href="https://agentmods.dev/skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality"><img src="https://agentmods.dev/badge/skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality/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 ralphctl-code-review-and-quality

Your own site · 80×15
<a href="https://agentmods.dev/skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality"><img src="https://agentmods.dev/badge/skills/lukas-grigis/ralphctl/ralphctl-code-review-and-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,617 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 warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

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 →

  • medium Prompt Injection · line 220
    Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.
    Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
  • medium Prompt Injection · line 223
    Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.
    Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 $0.00076 $0.04617
Opus 5.5 $0.00030 $0.01847
Sonnet 5.5 $0.00015 $0.00923
Haiku 4.5 $0.00008 $0.00462

Measured 8d ago against content hash 2bf5956c6684, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ralphctl-code-review-and-quality 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 8d 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.

src/integration/ai/skills/bundled/ralphctl-code-review-and-quality/SKILL.md · 369 lines

How it starts

The opening of the file, as written. The whole thing — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Review and Quality

Concept from addyosmani/agent-skills — "Code Review and Quality", MIT License. Adapted for ralphctl's evaluator role and review flow. The smell baseline draws on mattpocock/skills and the concrete-case and expected-load rules on DietrichGebert/ponytail (both MIT).

One-shot generation looks fast and is slow. Catching a correctness, architecture, or security problem at the seam between two changes is cheap; catching it at the end of a 200-line diff — or after the post-task gate fires — is not. This skill applies inside each phase's work, and especially when you are the evaluator scoring a generator's output.

The approval standard: Approve a change when it definitely improves overall code health, even if it is not perfect. Perfect code does not exist — the goal is continuous improvement. Do not block a change because it is not exactly how you would have written it. If it improves the codebase and follows the project's conventions, it is approvable.

AI-written code needs more scrutiny, not less. It is confident and plausible, even when wrong. The rationalisation "it works, that's good enough" is exactly the failure mode this skill exists to counter.

When this applies

  • Refine — rarely the primary frame here, but use the correctness and readability axes to audit acceptance criteria for internal contradictions, missing edge cases, and untestable "should" phrasings.
  • Plan — apply the architecture axis to the generated task list: do dependency directions match the actual data flow? Are any tasks so large they warrant splitting?
  • Execute — the evaluator role uses the full five-axis rubric and severity vocabulary below to score the generator's output and surface findings. The reviewer role (apply-feedback flow) applies the same rubric to human-requested changes.

The Five-Axis Review

Read the full file on GitHub · 369 lines

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. 8d ago Changed · +30 lines 2bf5956c6684
  2. 13d ago Changed · +81 lines acb56ca4de5a
  3. 1mo ago Changed · +4 lines 1eb642015329
  4. 1mo ago Changed · +3 lines 42c881baa3da
  5. 1mo ago First seen · 251 lines · 76 tokens per session scan A dc34a37283c3

Subscribe to this mod's changes

ralphctl-code-review-and-quality is a skill published in the GitHub repository lukas-grigis/ralphctl (24 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 4,617 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.

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