COMMAND_COMPLIANCE_REPโฆ
Reviewed all command files in `.claude/commands/analysis/` directory to ensure proper usage of: - `mcp__claude-flow__*` tools (preferred) - `npx claude-flow`โฆ
Transform GitHub Issues into intelligent swarm tasks, enabling automatic task decomposition and agent coordination.
> /plugin marketplace add spencermarx/open-code-review > /plugin install ocr@aclarify
How it fires
How this command gets triggered: by you, by Claude, or both.
/swarm-issueContext preview
What this command does when you run it.
Transform GitHub Issues into intelligent swarm tasks, enabling automatic task decomposition and agent coordination.
Transform GitHub Issues into intelligent swarm tasks, enabling automatic task decomposition and agent coordination.
# Create swarm from issue using gh CLI # Get issue details ISSUE_DATA=$(gh issue view 456 --json title,body,labels,assignees,comments) # Create swarm from issue npx ruv-swarm github issue-to-swarm 456 \ --issue-data "$ISSUE_DATA" \ --auto-decompose \ --assign-agents # Batch process multiple issues ISSUES=$(gh issue list --label "swarm-ready" --json number,title,body,labels) npx ruv-swarm github issues-batch \ --issues "$ISSUES" \ --parallel # Update issues with swarm status echo "$ISSUES" | jq -r '.[].number' | while read -r num; do gh issue edit $num --add-label "swarm-processing" done
Execute swarm operations via issue comments:
<!-- In issue comment --> /swarm analyze /swarm decompose 5 /swarm assign @agent-coder /swarm estimate /swarm start
<!-- .github/ISSUE_TEMPLATE/swarm-task.yml -->
name: Swarm Task
description: Create a task for AI swarm processing
body:
- type: dropdown
id: topology
attributes:
label: Swarm Topology
options:
- mesh
- hierarchical
- ring
- star
- type: input
id: agents
attributes:
label: Required Agents
placeholder: "coder, tester, analyst"
- type: textarea
id: tasks
attributes:
label: Task Breakdown
placeholder: |
1. Task one description
2. Task two description// .github/swarm-labels.json
{
"rules": [
{
"keywords": ["bug", "error", "broken"],
"labels": ["bug", "swarm-debugger"],
"agents": ["debugger", "tester"]
},
{
"keywords": ["feature", "implement", "add"],
"labels": ["enhancement", "swarm-feature"],
"agents": ["architect", "coder", "tester"]
},
{
"keywords": ["slow", "performance", "optimize"],
"labels": ["performance", "swarm-optimizer"],
"agents": ["analyst", "optimizer"]
}
]
}# Assign agents based on issue content npx ruv-swarm github issue-analyze 456 \ --suggest-agents \ --estimate-complexity \ --create-subtasks
# Create swarm with full issue context using gh CLI
# Get complete issue data
ISSUE=$(gh issue view 456 --json title,body,labels,assignees,comments,projectItems)
# Get referenced issues and PRs
REFERENCES=$(gh issue view 456 --json body --jq '.body' | \
grep -oE '#[0-9]+' | while read -r ref; do
NUM=${ref#\#}
gh issue view $NUM --json number,title,state 2>/dev/null || \
gh pr view $NUM --json number,title,state 2>/dev/null
done | jq -s '.')
# Initialize swarm
npx ruv-swarm github issue-init 456 \
--issue-data "$ISSUE" \
--references "$REFERENCES" \
--load-comments \
--analyze-references \
--auto-topology
# Add swarm initialization comment
gh issue comment 456 --body "๐ Swarm initialized for this issue"# Break down issue into subtasks with gh CLI
# Get issue body
ISSUE_BODY=$(gh issue view 456 --json body --jq '.body')
# Decompose into subtasks
SUBTASKS=$(npx ruv-swarm github issue-decompose 456 \
--body "$ISSUE_BODY" \
--max-subtasks 10 \
--assign-priorities)
# Update issue with checklist
CHECKLIST=$(echo "$SUBTASKS" | jq -r '.tasks[] | "- [ ] " + .description')
UPDATED_BODY="$ISSUE_BODY
## Subtasks
$CHECKLIST"
gh issue edit 456 --body "$UPDATED_BODY"
# Create linked issues for major subtasks
echo "$SUBTASKS" | jq -r '.tasks[] | select(.priority == "high")' | while read -r task; do
TITLE=$(echo "$task" | jq -r '.title')
BODY=$(echo "$task" | jq -r '.description')
gh issue create \
--title "$TITLE" \
--body "$BODY
Parent issue: #456" \
--label "subtask"
done# Update issue with swarm progress using gh CLI
# Get current issue state
CURRENT=$(gh issue view 456 --json body,labels)
# Get swarm progress
PROGRESS=$(npx ruv-swarm github issue-progress 456)
# Update checklist in issue body
UPDATED_BODY=$(echo "$CURRENT" | jq -r '.body' | \
npx ruv-swarm github update-checklist --progress "$PROGRESS")
# Edit issue with updated body
gh issue edit 456 --body "$UPDATED_BODY"
# Post progress summary as comment
SUMMARY=$(echo "$PROGRESS" | jq -r '
"## ๐ Progress Update
**Completion**: \(.completion)%
**ETA**: \(.eta)
### Completed Tasks
\(.completed | map("- โ
" + .) | join("\n"))
### In Progress
\(.in_progress | map("- ๐ " + .) | join("\n"))
### Remaining
\(.remaining | map("- โณ " + .) | join("\n"))
---
๐ค Automated update by swarm agent"')
gh issue comment 456 --body "$SUMMARY"
# Update labels based on progress
if [[ $(echo "$PROGRESS" | jq -r '.completion') -eq 100 ]]; then
gh issue edit 456 --add-label "ready-for-review" --remove-label "in-progress"
fi# Handle issue dependencies npx ruv-swarm github issue-deps 456 \ --resolve-order \ --parallel-safe \ --update-blocking
# Coordinate epic-level swarms npx ruv-swarm github epic-swarm \ --epic 123 \ --child-issues "456,457,458" \ --orchestrate
# Generate issue from swarm analysis npx ruv-swarm github create-issues \ --from-analysis \ --template "bug-report" \ --auto-assign
# .github/workflows/issue-swarm.yml
name: Issue Swarm Handler
on:
issues:
types: [opened, labeled, commented]
jobs:
swarm-process:
runs-on: ubuntu-latest
steps:
- name: Process Issue
uses: ruvnet/swarm-action@v1AI-powered multi-agent code review. Simulates a customizable team of Engineers performing code review with built-in discourse.
Repo: spencermarx/open-code-review
Reviewed all command files in `.claude/commands/analysis/` directory to ensure proper usage of: - `mcp__claude-flow__*` tools (preferred) - `npx claude-flow`โฆ
Analyze performance bottlenecks in swarm operations and suggest optimizations.
Identify and resolve performance bottlenecks in your development workflow.
Generate comprehensive performance reports for swarm operations.
Reduce token consumption while maintaining quality through intelligent coordination.