bradygaster/squad is a tool that creates a human-directed team of AI development agents inside a project repository through GitHub Copilot. Developers use it to delegate work among persistent specialists such as frontend, backend, testing, and lead agents while retaining responsibility for decisions and review. The catalogue entries are the skills, agents, and instructions that define and coordinate those team members.
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 bradygaster/squad --skill pr-review-responsegit clone --depth 1 https://github.com/bradygaster/squadWrote 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/bradygaster/squad/pr-review-response)<a href="https://agentmods.dev/skills/bradygaster/squad/pr-review-response"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/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/bradygaster/squad/pr-review-response"><img src="https://agentmods.dev/badge/skills/bradygaster/squad/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 5.5 | $0.00009 | $0.01030 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo ago First seen · 269 lines · 22 tokens per session scan A fe66f6493cc1
pr-review-response is a skill published in the GitHub repository bradygaster/squad (3,295 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 2,574 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
ponytail-review
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ponytail-audit
Whole-repo audit for over-engineering. Like ponytail-review, but scans the entire codebase instead of a diff: a ranked list of what to delete, simplify, or replace with stdlib/native equivalents. Use when the user says "audit this codebase", "audit for over-engineering", "what can I delete from this repo", "find…
omk-code-review
Review implementation changes against contracts and real failure modes, preserve minimal patches, and verify regressions without confusing test counts, generated code volume or CI claims with production correctness.
ralphctl-code-review-and-quality
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dos-verify-done-claims
Before accepting an agent's 'done / shipped / fixed' claim, verify it against ground truth (git ancestry + the commit's own diff) using the DOS kernel's dos verify and dos commit-audit — never the agent's own narration.
ce-code-review
Structured code review for bugs, regressions, tests, and standards. Use before PRs or when asked for review; interactive mode can fix locally, while mode:agent reports only for pipeline callers.