agent-swarm-pr
- Repo stars 54,444
- Author updated Live
- Author repo ruflo
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ruvnet · no license declared
- Token usage
- Moderate
- Setup complexity
- Manual integration
- External API key
- Required · GitHub
- Operating systems
- Linux
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-swarm-pr
description: Agent skill for swarm-pr - invoke with $agent-swarm-pr description: Pull request swarm managemen…
category: ai
runtime: no special runtime
---
# agent-swarm-pr output preview
## PART A: Task fit
- Use case: Agent skill for swarm-pr - invoke with $agent-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" Create and manage AI swarms directly from GitHub Pull Requests, enabling seamless integration with ….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / Core Features / 1. PR-Based Swarm Creation” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Agent skill for swarm-pr - invoke with $agent-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" Create and manage AI swarms directly from GitHub Pull Requests, enabling seamless integration with …”.
- **02** When the source has headings, the agent prioritizes “Overview / Core Features / 1. PR-Based Swarm Creation” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; may access external network resources; requires GitHub API keys.
## Running Rules
- read files, write/modify files, run shell commands; may access external network resources; requires GitHub API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Overview / Core Features / 1. PR-Based Swarm Creation”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-swarm-pr
description: Agent skill for swarm-pr - invoke with $agent-swarm-pr description: Pull request swarm managemen…
category: ai
source: ruvnet/ruflo
---
# agent-swarm-pr
## When to use
- Agent skill for swarm-pr - invoke with $agent-swarm-pr description: Pull request swarm management agent that coordinat…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / Core Features / 1. PR-Based Swarm Creation” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; may access external network resources; requires GitHub API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-swarm-pr" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / Core Features / 1. PR-Based Swarm Creation
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | may access external network resources
guardrails -> requires GitHub API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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"
$swarm status
3. Automated PR Workflows
# .github$workflows$swarm-pr.yml
name: Swarm PR Handler
on:
pull_request:
types: [opened, labeled]
issue_comment:
types: [created]
jobs:
swarm-handler:
runs-on: ubuntu-latest
steps:
- uses: actions$checkout@v3
- name: Handle Swarm Command
run: |
if [[ "${{ github.event.comment.body }}" == $swarm* ]]; then
npx ruv-swarm github handle-comment \
--pr ${{ github.event.pull_request.number }} \
--comment "${{ github.event.comment.body }}"
fi
PR Label Integration
Automatic Agent Assignment
Map PR labels to agent types:
{
"label-mapping": {
"bug": ["debugger", "tester"],
"feature": ["architect", "coder", "tester"],
"refactor": ["analyst", "coder"],
"docs": ["researcher", "writer"],
"performance": ["analyst", "optimizer"]
}
}
Label-Based Topology
# Small PR (< 100 lines): ring topology
# Medium PR (100-500 lines): mesh topology
# Large PR (> 500 lines): hierarchical topology
npx ruv-swarm github pr-topology --pr 123
PR Swarm Commands
Initialize from PR
# Create swarm with PR context using gh CLI
PR_DIFF=$(gh pr diff 123)
PR_INFO=$(gh pr view 123 --json title,body,labels,files,reviews)
npx ruv-swarm github pr-init 123 \
--auto-agents \
--pr-data "$PR_INFO" \
--diff "$PR_DIFF" \
--analyze-impact
Progress Updates
# Post swarm progress to PR using gh CLI
PROGRESS=$(npx ruv-swarm github pr-progress 123 --format markdown)
gh pr comment 123 --body "$PROGRESS"
# Update PR labels based on progress
if [[ $(echo "$PROGRESS" | grep -o '[0-9]\+%' | sed 's/%//') -gt 90 ]]; then
gh pr edit 123 --add-label "ready-for-review"
fi
Code Review Integration
# Create review agents with gh CLI integration
PR_FILES=$(gh pr view 123 --json files --jq '.files[].path')
# Run swarm review
REVIEW_RESULTS=$(npx ruv-swarm github pr-review 123 \
--agents "security,performance,style" \
--files "$PR_FILES")
# Post review comments using gh CLI
echo "$REVIEW_RESULTS" | jq -r '.comments[]' | while read -r comment; do
FILE=$(echo "$comment" | jq -r '.file')
LINE=$(echo "$comment" | jq -r '.line')
BODY=$(echo "$comment" | jq -r '.body')
gh pr review 123 --comment --body "$BODY"
done
Advanced Features
1. Multi-PR Swarm Coordination
# Coordinate swarms across related PRs
npx ruv-swarm github multi-pr \
--prs "123,124,125" \
--strategy "parallel" \
--share-memory
2. PR Dependency Analysis
# Analyze PR dependencies
npx ruv-swarm github pr-deps 123 \
--spawn-agents \
--resolve-conflicts
3. Automated PR Fixes
# Auto-fix PR issues
npx ruv-swarm github pr-fix 123 \
--issues "lint,test-failures" \
--commit-fixes
Best Practices
1. PR Templates
<!-- .github$pull_request_template.md -->
## Swarm Configuration
- Topology: [mesh$hierarchical$ring$star]
- Max Agents: [number]
- Auto-spawn: [yes$no]
- Priority: [high$medium$low]
## Tasks for Swarm
- [ ] Task 1 description
- [ ] Task 2 description
2. Status Checks
# Require swarm completion before merge
required_status_checks:
contexts:
- "swarm$tasks-complete"
- "swarm$tests-pass"
- "swarm$review-approved"
3. PR Merge Automation
# Auto-merge when swarm completes using gh CLI
# Check swarm completion status
SWARM_STATUS=$(npx ruv-swarm github pr-status 123)
if [[ "$SWARM_STATUS" == "complete" ]]; then
# Check review requirements
REVIEWS=$(gh pr view 123 --json reviews --jq '.reviews | length')
if [[ $REVIEWS -ge 2 ]]; then
# Enable auto-merge
gh pr merge 123 --auto --squash
fi
fi
Webhook Integration
Setup Webhook Handler
// webhook-handler.js
const { createServer } = require('http');
const { execSync } = require('child_process');
createServer((req, res) => {
if (req.url === '$github-webhook') {
const event = JSON.parse(body);
if (event.action === 'opened' && event.pull_request) {
execSync(`npx ruv-swarm github pr-init ${event.pull_request.number}`);
}
res.writeHead(200);
res.end('OK');
}
}).listen(3000);
Examples
Feature Development PR
# PR #456: Add user authentication
npx ruv-swarm github pr-init 456 \
--topology hierarchical \
--agents "architect,coder,tester,security" \
--auto-assign-tasks
Bug Fix PR
# PR #789: Fix memory leak
npx ruv-swarm github pr-init 789 \
--topology mesh \
--agents "debugger,analyst,tester" \
--priority high
Documentation PR
# PR #321: Update API docs
npx ruv-swarm github pr-init 321 \
--topology ring \
--agents "researcher,writer,reviewer" \
--validate-links
Metrics & Reporting
PR Swarm Analytics
# Generate PR swarm report
npx ruv-swarm github pr-report 123 \
--metrics "completion-time,agent-efficiency,token-usage" \
--format markdown
Dashboard Integration
# Export to GitHub Insights
npx ruv-swarm github export-metrics \
--pr 123 \
--to-insights
Security Considerations
- Token Permissions: Ensure GitHub tokens have appropriate scopes
- Command Validation: Validate all PR comments before execution
- Rate Limiting: Implement rate limits for PR operations
- Audit Trail: Log all swarm operations for compliance
Integration with Claude Code
When using with Claude Code:
- Claude Code reads PR diff and context
- Swarm coordinates approach based on PR type
- Agents work in parallel on different aspects
- Progress updates posted to PR automatically
- Final review performed before marking ready
Advanced Swarm PR Coordination
Multi-Agent PR Analysis
# Initialize PR-specific swarm with intelligent topology selection
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 8 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "PR Coordinator" }
mcp__claude-flow__agent_spawn { type: "reviewer", name: "Code Reviewer" }
mcp__claude-flow__agent_spawn { type: "tester", name: "Test Engineer" }
mcp__claude-flow__agent_spawn { type: "analyst", name: "Impact Analyzer" }
mcp__claude-flow__agent_spawn { type: "optimizer", name: "Performance Optimizer" }
# Store PR context for swarm coordination
mcp__claude-flow__memory_usage {
action: "store",
key: "pr/#{pr_number}$analysis",
value: {
diff: "pr_diff_content",
files_changed: ["file1.js", "file2.py"],
complexity_score: 8.5,
risk_assessment: "medium"
}
}
# Orchestrate comprehensive PR workflow
mcp__claude-flow__task_orchestrate {
task: "Execute multi-agent PR review and validation workflow",
strategy: "parallel",
priority: "high",
dependencies: ["diff_analysis", "test_validation", "security_review"]
}
Swarm-Coordinated PR Lifecycle
// Pre-hook: PR Initialization and Swarm Setup
const prPreHook = async (prData) => {
// Analyze PR complexity for optimal swarm configuration
const complexity = await analyzePRComplexity(prData);
const topology = complexity > 7 ? "hierarchical" : "mesh";
// Initialize swarm with PR-specific configuration
await mcp__claude_flow__swarm_init({ topology, maxAgents: 8 });
// Store comprehensive PR context
await mcp__claude_flow__memory_usage({
action: "store",
key: `pr/${prData.number}$context`,
value: {
pr: prData,
complexity,
agents_assigned: await getOptimalAgents(prData),
timeline: generateTimeline(prData)
}
});
// Coordinate initial agent synchronization
await mcp__claude_flow__coordination_sync({ swarmId: "current" });
};
// Post-hook: PR Completion and Metrics
const prPostHook = async (results) => {
// Generate comprehensive PR completion report
const report = await generatePRReport(results);
// Update PR with final swarm analysis
await updatePRWithResults(report);
// Store completion metrics for future optimization
await mcp__claude_flow__memory_usage({
action: "store",
key: `pr/${results.number}$completion`,
value: {
completion_time: results.duration,
agent_efficiency: results.agentMetrics,
quality_score: results.qualityAssessment,
lessons_learned: results.insights
}
});
};
Intelligent PR Merge Coordination
# Coordinate merge decision with swarm consensus
mcp__claude-flow__coordination_sync { swarmId: "pr-review-swarm" }
# Analyze merge readiness with multiple agents
mcp__claude-flow__task_orchestrate {
task: "Evaluate PR merge readiness with comprehensive validation",
strategy: "sequential",
priority: "critical"
}
# Store merge decision context
mcp__claude-flow__memory_usage {
action: "store",
key: "pr$merge_decisions/#{pr_number}",
value: {
ready_to_merge: true,
validation_passed: true,
agent_consensus: "approved",
final_review_score: 9.2
}
}
See also: swarm-issue.md, sync-coordinator.md, workflow-automation.md
Decide Fit First
Design Intent
How To Use It
Boundaries And Review