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 wangke19/gemini-ai-helpers --skill classify-review-commentgit clone --depth 1 https://github.com/wangke19/gemini-ai-helpersWrote 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/wangke19/gemini-ai-helpers/classify-review-comment)<a href="https://agentmods.dev/skills/wangke19/gemini-ai-helpers/classify-review-comment"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/classify-review-comment/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/wangke19/gemini-ai-helpers/classify-review-comment"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/classify-review-comment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00087 | $0.02809 |
| Opus 5 | $0.00044 | $0.01404 |
| Sonnet 5 | $0.00017 | $0.00562 |
| Haiku 4.5 | $0.00009 | $0.00281 |
Grade A, and why
classify-review-comment 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 10d 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.
This is a copy
100% identical to classify-review-comment — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Classify Review Comments
Classify GitHub pull request review comments into severity and topic categories. Works with a single comment (text), a GitHub comment URL, or an entire PR (classifies all comments).
This enables tracking review feedback patterns: what kinds of issues reviewers catch, how severe they are, and where AI-generated code needs the most improvement.
Labels
Read the labels file before classifying any comments:
config.json (in the same directory as this skill)
The labels file defines the exact set of valid values for severity and topic. You MUST select from these values — do not invent new labels. Each label includes a description and signal words or examples to guide your selection.
Classification rule: For each comment, find the single best-matching severity and single best-matching topic from the labels file. Match based on the label's description, signals/examples, and the comment content. Use unclassified only when no other label fits.
Input Modes
1. Single Comment (text)
Classify a comment provided directly as text.
Input: The raw comment body. Output: A single classification object.
2. Comment URL
Fetch a specific comment by its GitHub URL and classify it.
URL formats supported:
https://github.com/{owner}/{repo}/pull/{number}#issuecomment-{id}https://github.com/{owner}/{repo}/pull/{number}#discussion_r{id}https://github.com/{owner}/{repo}/pull/{number}#pullrequestreview-{id}
Fetch with:
# Issue comment
gh api repos/{owner}/{repo}/issues/comments/{id} --jq '{author: .user.login, body: .body}'
# Review comment (discussion)
gh api repos/{owner}/{repo}/pulls/comments/{id} --jq '{author: .user.login, body: .body}'
# Review body comment
gh api repos/{owner}/{repo}/pulls/{number}/reviews/{id} --jq '{author: .user.login, body: .body}'
3. Full PR
Fetch all comments on a PR, filter out noise, and classify each one.
URL format: https://github.com/{owner}/{repo}/pull/{number}
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.
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.
- 10d ago First seen · 228 lines · 87 tokens per session scan A 2cfd1293cdc7
classify-review-comment is a skill published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 87 tokens to every session and 2,809 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to classify-review-comment, differing in 0 lines, and is treated as a copy.
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