Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/tonyghiani/ai-essentialsnpx agentmods add skills/tonyghiani/ai-essentials/review-pr-commentsWrote 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/tonyghiani/ai-essentials/review-pr-comments)<a href="https://agentmods.dev/skills/tonyghiani/ai-essentials/review-pr-comments"><img src="https://agentmods.dev/badge/skills/tonyghiani/ai-essentials/review-pr-comments/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/tonyghiani/ai-essentials/review-pr-comments"><img src="https://agentmods.dev/badge/skills/tonyghiani/ai-essentials/review-pr-comments.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 | $0.00095 | $0.01093 |
| Opus 5 | $0.00048 | $0.00547 |
| Sonnet 5 | $0.00019 | $0.00219 |
| Haiku 4.5 | $0.00010 | $0.00109 |
Grade A, and why
review-pr-comments 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review PR Comments
Analyze review comments on a GitHub PR and recommend whether to accept or push back on each one, with reasoning grounded in the actual diff.
Path resolution
Paths below are relative to this skill's directory.
Inputs
The user provides:
- One or more GitHub PR comment links (e.g.
https://github.com/org/repo/pull/123#discussion_r456) - Optionally, the PR URL itself
Extract OWNER/REPO, PR_NUMBER, and any comment/discussion IDs from the links.
Workflow
Step 1 — Fetch PR metadata and diff
gh pr view $PR_NUMBER --repo $OWNER/$REPO --json title,body,baseRefName,headRefName,files,additions,deletions
gh pr diff $PR_NUMBER --repo $OWNER/$REPO
Step 2 — Fetch the review comments
gh api repos/$OWNER/$REPO/pulls/$PR_NUMBER/comments
From the full list, filter to the comments the user linked. If the user provided only a PR URL with no specific comment links, fetch all pending review comments and analyze every one.
For each comment, extract:
body(the reviewer's text)pathandline/original_line(file location)diff_hunk(surrounding diff context)user.login(who wrote it)in_reply_to_id(thread context — fetch parent if present)
Step 3 — Gather deeper context (if needed)
If the diff hunk alone is insufficient to reason about a comment:
- Read the full file at the relevant lines (±30 lines around the comment).
- Check git blame or history for context on why the code was written that way.
- Last resort only: if the comment spans multiple files, touches
architecture, or cannot be judged from local context, read and follow
../review-pr/SKILL.mdon the same PR. Do not escalate for single-line style nits or localized logic questions.
Step 4 — Analyze each comment
For every comment, reason through:
- Is the reviewer correct? Does the comment identify a real issue, or is it based on a misunderstanding of the code/context?
- Severity: Is this blocking, a nice-to-have, or purely stylistic?
- Cost vs benefit: How much effort to address vs how much it improves the code? Is the change mechanical or does it require rethinking the approach?
- Alternatives: If the reviewer's suggestion isn't ideal, is there a better middle ground?
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 · 126 lines · 95 tokens per session scan A 284bab87cb9c
review-pr-comments is a skill published in the GitHub repository tonyghiani/ai-essentials (6 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 1,093 once invoked, about $0.0005 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 2026-08-31.
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