ソース情報
- リポジトリ
- ruvnet/ruflo
- ソースの最終更新活動
- 2026年2月7日 17:36
- 検出された SKILL.md の言語
- 英語
- スター
- 69,353
- フォーク
- 8,298
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
メニュー
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/ruvnet/ruflo --skill agent-swarm-prコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか?SkillsMP が現在取得できるファイルをダウンロードできます。
Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.
One-shot chat completion against DeepSeek's `deepseek-chat` model via the OpenAI-compatible /v1/chat/completions endpoint. Reads DEEPSEEK_API_KEY from the environment; degrades gracefully (exit 0 with a JSON status:degraded envelope) when the key is missing or the API is unreachable. Use for non-reasoning tasks — summarization, extraction, quick classification — where deepseek-reasoner would be overkill.
Reasoning-mode completion against DeepSeek's `deepseek-reasoner` model (R1) via /v1/chat/completions. Surfaces the model's chain-of-thought (`reasoning_content`) separately from the final answer (`content`), so callers can display or discard the CoT without re-parsing. Reads DEEPSEEK_API_KEY; degrades gracefully (exit 0 with status:degraded envelope) when unset or the API is unreachable. Ignores temperature/top_p per DeepSeek's spec for reasoner models.
SKILL.md を表示中
SOC 職業分類に基づく
| name | agent-swarm-pr |
| description | Agent skill for swarm-pr - invoke with $agent-swarm-pr |
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:
Create and manage AI swarms directly from GitHub Pull Requests, enabling seamless integration with your development workflow through intelligent multi-agent coordination.
# 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
Execute swarm commands via PR comments:
<!-- In PR comment -->
$swarm init mesh 6
$swarm spawn coder "Implement authentication"
$swarm spawn tester "Write unit tests"
$swarm status
# .github$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
Map PR labels to agent types:
{
"label-mapping": {
"bug": ["debugger", "tester"],
"feature": ["architect", "coder", "tester"],
"refactor": ["analyst", "coder"],
"docs": ["researcher", "writer"],
"performance": ["analyst", "optimizer"]
}
}
# 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
# 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
# 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
# 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 "$BODY"
done
# Coordinate swarms across related PRs
npx ruv-swarm github multi-pr \
--prs "123,124,125" \
--strategy "parallel" \
--share-memory
# Analyze PR dependencies
npx ruv-swarm github pr-deps 123 \
--spawn-agents \
--resolve-conflicts
# Auto-fix PR issues
npx ruv-swarm github pr-fix 123 \
--issues "lint,test-failures" \
--commit-fixes
<!-- .github$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
# Require swarm completion before merge
required_status_checks:
contexts:
- "swarm$tasks-complete"
- "swarm$tests-pass"
- "swarm$review-approved"
# Auto-merge when swarm completes using gh CLI
# Check swarm completion status
SWARM_STATUS=$(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 [[ $REVIEWS -ge 2 ]]; then
# Enable auto-merge
gh pr merge 123 --auto --squash
fi
fi
// webhook-handler.js
const { createServer } = require('http');
const { execSync } = require('child_process');
createServer((req, res) => {
if (req.url === '$github-webhook') {
const event = JSON.parse(body);
if (event.action === 'opened' && event.pull_request) {
execSync(`npx ruv-swarm github pr-init ${event.pull_request.number}`);
}
res.writeHead(200);
res.end('OK');
}
}).listen(3000);
# PR #456: Add user authentication
npx ruv-swarm github pr-init 456 \
--topology hierarchical \
--agents "architect,coder,tester,security" \
--auto-assign-tasks
# PR #789: Fix memory leak
npx ruv-swarm github pr-init 789 \
--topology mesh \
--agents "debugger,analyst,tester" \
--priority high
# PR #321: Update API docs
npx ruv-swarm github pr-init 321 \
--topology ring \
--agents "researcher,writer,reviewer" \
--validate-links
# Generate PR swarm report
npx ruv-swarm github pr-report 123 \
--metrics "completion-time,agent-efficiency,token-usage" \
--format markdown
# Export to GitHub Insights
npx ruv-swarm github export-metrics \
--pr 123 \
--to-insights
When using with Claude Code:
# Initialize PR-specific swarm with intelligent topology selection
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 8 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "PR Coordinator" }
mcp__claude-flow__agent_spawn { type: "reviewer", name: "Code Reviewer" }
mcp__claude-flow__agent_spawn { type: "tester", name: "Test Engineer" }
mcp__claude-flow__agent_spawn { type: "analyst", name: "Impact Analyzer" }
mcp__claude-flow__agent_spawn { type: "optimizer", name: "Performance Optimizer" }
# Store PR context for swarm coordination
mcp__claude-flow__memory_usage {
action: "store",
key: "pr/#{pr_number}$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"]
}
// 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 (results);
(report);
({
: ,
: ,
: {
: results.,
: results.,
: results.,
: results.
}
});
};
# 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