Borrowing it
Nothing to install: this file belongs to zetaalphavector/RAGElo. 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/zetaalphavector/RAGElo/master/.agents/skills/review-pr/SKILL.mdgit clone --depth 1 https://github.com/zetaalphavector/RAGEloWrote 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/zetaalphavector/ragelo/review-pr)<a href="https://agentmods.dev/skills/zetaalphavector/ragelo/review-pr"><img src="https://agentmods.dev/badge/skills/zetaalphavector/ragelo/review-pr/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/zetaalphavector/ragelo/review-pr"><img src="https://agentmods.dev/badge/skills/zetaalphavector/ragelo/review-pr.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 26 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
- medium Excessive Agency · line 485 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00044 | $0.04662 |
| Opus 5.5 | $0.00018 | $0.01865 |
| Sonnet 5.5 | $0.00009 | $0.00932 |
| Haiku 4.5 | $0.00004 | $0.00466 |
Grade A, and why
review-pr 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 2mo 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 — 517 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Review Skill
Overview
RAGElo is a Python library and CLI for evaluating RAG (Retrieval-Augmented Generation) agents using Elo-based tournament ranking. This skill reviews pull requests to ensure they:
- Actually implement what the PR description claims
- Follow existing codebase patterns (the Chameleon Principle)
- Match the code style of the existing codebase
- Align with RAGElo's established architecture (Factory+Registry, async evaluators, Pydantic configs)
- Address security concerns properly
- Introduce no breaking changes to the public API (or have proper migration paths)
- Relate to existing GitHub issues appropriately
The Chameleon Principle
The codebase should feel like it was architected by one mind, not assembled by mercenaries.
Every PR must introduce changes that blend seamlessly with existing patterns. When reviewing:
- Find the existing pattern first - Before accepting any change, search for how similar problems are already solved
- Reject foreign patterns - If the PR introduces a pattern that doesn't exist elsewhere, flag it ruthlessly
- Suggest the existing way - Always recommend the established approach over novel solutions
- Be ruthless - Pattern violations are not style nits; they are architectural debt
This principle applies to everything: architecture, code organization, naming conventions, testing approaches, and code style. AI agents are notorious for ignoring existing patterns and producing the same generic slop everywhere. The reviewer must catch this.
Output Format
The agent must produce:
- Summary - Brief overview of what the PR does
- Implementation Verification - Does the code actually do what the PR claims?
- Breaking Changes - Any backward-incompatible changes to the public API and migration paths
- Pattern & Style Conformance - Does the PR follow existing codebase patterns and style?
- Architecture Alignment - Is the PR consistent with RAGElo's Factory+Registry architecture?
- Code Quality & Testing - Test coverage, test quality, code organization
- Issue Linkage - Related GitHub issues
- Security Concerns - Issues identified against security requirements
- Recommendations - Required changes, suggestions, and approval status
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.
- 2mo ago First seen · 517 lines · 44 tokens per session scan A 741d742df2bc
review-pr is a skill published in the GitHub repository zetaalphavector/RAGElo (133 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 4,662 once invoked, about $0.0002 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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