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 DanWahlin/ai-agent-board --skill pr-review-responsegit clone --depth 1 https://github.com/DanWahlin/ai-agent-boardWrote 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/danwahlin/ai-agent-board/pr-review-response)<a href="https://agentmods.dev/skills/danwahlin/ai-agent-board/pr-review-response"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/pr-review-response/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/danwahlin/ai-agent-board/pr-review-response"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/pr-review-response.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 | $0.00022 | $0.02574 |
| Opus 5 | $0.00011 | $0.01287 |
| Sonnet 5 | $0.00004 | $0.00515 |
| Haiku 4.5 | $0.00002 | $0.00257 |
Grade A, and why
pr-review-response 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 11d 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 pr-review-response — 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
When an agent fixes code in response to PR review comments (from Copilot, a human reviewer, or any GitHub reviewer), the fix alone is not enough. The reviewer needs to see — on the PR thread itself — which comments were addressed and how. Without replies, comments stay visually unresolved, reviewers must re-read the entire diff to verify fixes, and there's no traceable link between feedback and resolution.
Use this skill whenever:
- You are fixing code based on PR review feedback
- You are addressing Copilot review suggestions
- You are responding to reviewer-requested changes on a PR
- A squad member hands you review comments to resolve
SCOPE
✅ THIS SKILL PRODUCES:
- Reply comments on each review thread explaining the fix
- Optionally resolved threads (via GraphQL when appropriate)
- Commit messages that reference the PR and review context
❌ THIS SKILL DOES NOT PRODUCE:
- The code fixes themselves (that's the agent's domain work)
- New review comments or reviews
- PR descriptions or summaries
Patterns
Step 1: Read the review comments
Using MCP tools (preferred when available):
github-mcp-server-pull_request_read
method: "get_review_comments"
owner: "{owner}"
repo: "{repo}"
pullNumber: {pr_number}
This returns review threads with metadata: isResolved, isOutdated, isCollapsed, and their associated comments. Each comment has an id you'll need for replies.
Using gh CLI (fallback):
gh api repos/{owner}/{repo}/pulls/{pr_number}/comments --paginate
Each comment object contains id, body, path, line, and in_reply_to_id. Top-level comments have no in_reply_to_id — those are the ones you reply to.
Step 2: Fix the code
Make the actual code changes. This is your normal domain work — the skill doesn't prescribe how to fix, only how to communicate the fix.
Track what you changed. For each review comment, note:
- The comment
id(top-level, not a reply) - The file and line referenced
- What you actually changed (brief description)
- The commit SHA after pushing (if available)
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.
- 11d ago First seen · 269 lines · 22 tokens per session scan A fe66f6493cc1
pr-review-response is a skill published in the GitHub repository DanWahlin/ai-agent-board (58 stars, last pushed 16d ago), licensed MIT. It adds 22 tokens to every session and 2,574 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pr-review-response, differing in 0 lines, and is treated as a copy.
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