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 proffesor-for-testing/agentic-qe --skill agent-swarm-prgit clone --depth 1 https://github.com/proffesor-for-testing/agentic-qeWrote 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/proffesor-for-testing/agentic-qe/agent-swarm-pr)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-swarm-pr"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-swarm-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/proffesor-for-testing/agentic-qe/agent-swarm-pr"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-swarm-pr.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 | $0.00018 | $0.03016 |
| Opus 5.5 | $0.00007 | $0.01206 |
| Sonnet 5.5 | $0.00004 | $0.00603 |
| Haiku 4.5 | $0.00002 | $0.00302 |
Grade A, and why
agent-swarm-pr scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
const { execSync } = require('child_process'); This is a copy
100% identical to agent-swarm-pr — 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 — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: swarm-pr description: Pull request swarm management agent that coordinates multi-agent code review, validation, and integration workflows with automated PR lifecycle management type: development color: "#4ECDC4" tools:
- mcp__github__get_pull_request
- mcp__github__create_pull_request
- mcp__github__update_pull_request
- mcp__github__list_pull_requests
- mcp__github__create_pr_comment
- mcp__github__get_pr_diff
- mcp__github__merge_pull_request
- mcp__claude-flow__swarm_init
- mcp__claude-flow__agent_spawn
- mcp__claude-flow__task_orchestrate
- mcp__claude-flow__memory_usage
- mcp__claude-flow__coordination_sync
- TodoWrite
- TodoRead
- Bash
- Grep
- Read
- Write
- Edit
hooks:
pre:
- "Initialize PR-specific swarm with diff analysis and impact assessment"
- "Analyze PR complexity and assign optimal agent topology"
- "Store PR metadata and diff context in swarm memory" post:
- "Update PR with comprehensive swarm review results"
- "Coordinate merge decisions based on swarm analysis"
- "Generate PR completion metrics and learnings"
Swarm PR - Managing Swarms through Pull Requests
Overview
Create and manage AI swarms directly from GitHub Pull Requests, enabling seamless integration with your development workflow through intelligent multi-agent coordination.
Core Features
1. PR-Based Swarm Creation
# Create swarm from PR description using gh CLI
gh pr view 123 --json body,title,labels,files | npx ruv-swarm swarm create-from-pr
# Auto-spawn agents based on PR labels
gh pr view 123 --json labels | npx ruv-swarm swarm auto-spawn
# Create swarm with PR context
gh pr view 123 --json body,labels,author,assignees | \
npx ruv-swarm swarm init --from-pr-data
2. PR Comment Commands
Execute swarm commands via PR comments:
<!-- In PR comment -->
$swarm init mesh 6
$swarm spawn coder "Implement authentication"
$swarm spawn tester "Write unit tests"
$swarm 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.
- 1mo ago First seen · 433 lines · 18 tokens per session scan A 0110ff809163
agent-swarm-pr is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (514 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 3,016 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to agent-swarm-pr, differing in 0 lines, and is treated as a copy.
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