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 lukas-grigis/ralphctl --skill ralphctl-code-review-and-qualitygit clone --depth 1 https://github.com/lukas-grigis/ralphctlWrote 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/lukas-grigis/ralphctl/ralphctl-code-review-and-quality)<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.
<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>- NVIDIA SkillSpector warn
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
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 | $0.00076 | $0.02486 |
| Opus 5 | $0.00038 | $0.01243 |
| Sonnet 5 | $0.00015 | $0.00497 |
| Haiku 4.5 | $0.00008 | $0.00249 |
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 9d 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 — 251 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.
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
Every review evaluates code across these dimensions:
1. Correctness
Does the code do what it claims to do?
- Does it match the task's verification criteria?
- Are edge cases handled (null, empty, boundary values)?
- Are error paths handled — not just the happy path?
- Are there off-by-one errors, race conditions, or state inconsistencies?
- Do the tests actually test the right things, not just pass?
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.
- 9d ago First seen · 251 lines · 76 tokens per session scan A dc34a37283c3
ralphctl-code-review-and-quality is a skill published in the GitHub repository lukas-grigis/ralphctl (23 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 2,486 once invoked, about $0.0004 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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address-conductor-comment
A workflow for handling pending Conductor code-review comments, including reading their discussion and assigning changes to subagents when appropriate.
explain-comment
A skill that reads a specified file or code range and adds explanatory comments in Japanese. The comments use a question style and focus on important logic, design choices, and side effects.
code-review
A review process for a pull request, which is a proposed set of code changes. It examines the changed code and its surrounding files for bugs, security problems, design issues, and lint errors.
security-audit
A security review skill for examining code changes in a pull request, which is a proposed set of changes awaiting approval. It reports issues such as injection, exposed secrets, unsafe authentication, weak cryptography, vulnerable dependencies, and infrastructure mistakes.