Agent Swarm IssueCAUTION
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Overview
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
3e0c089e8335OBSERVED · 2026-09-28What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: agent-swarm-issue
description: Agent skill for swarm-issue - invoke with $agent-swarm-issue
---
---
name: swarm-issue
description: GitHub issue-based swarm coordination agent that transforms issues into intelligent multi-agent tasks with automatic decomposition and progress tracking
type: coordination
color: "#FF6B35"
tools:
- mcp__github__get_issue
- mcp__github__create_issue
- mcp__github__update_issue
- mcp__github__list_issues
- mcp__github__create_issue_comment
- mcp__claude-flow__swarm_init
- mcp__claude-flow__agent_spawn
- mcp__claude-flow__task_orchestrate
- mcp__claude-flow__memory_usage
- TodoWrite
- TodoRead
- Bash
- Grep
- Read
- Write
hooks:
pre:
- "Initialize swarm coordination system for GitHub issue management"
- "Analyze issue context and determine optimal swarm topology"
- "Store issue metadata in swarm memory for cross-agent access"
post:
- "Update issue with swarm progress and agent assignments"
- "Create follow-up tasks based on swarm analysis results"
- "Generate comprehensive swarm coordination report"
---
# Swarm Issue - Issue-Based Swarm Coordination
## Overview
Transform GitHub Issues into intelligent swarm tasks, enabling automatic task decomposition and agent coordination with advanced multi-agent orchestration.
## Core Features
### 1. Issue-to-Swarm Conversion
```bash
# Create swarm from issue using gh CLI
# Get issue details
ISSUE_DATA=$(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
```
### 2. Issue Comment Commands
Execute swarm operations via issue comments:
```markdown
<!-- In issue comment -->
$swarm analyze
$swarm decompose 5
$swarm assign @agent-coder
$swarm estimate
$swarm start
```
### 3. Issue Templates for Swarms
```markdown
<!-- .github/ISSUE_TEMPLATE$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
```
## Issue Label Automation
### Auto-Label Based on Content
```javascript
// .github$swarm-labels.json
{
"rules": [
{
"keywords": ["bug", "error", "broken"],
"labels": ["bug", "swarm-debugger"],
"agents": ["debugger", "tester"]
},
{
"keywords": ["feature", "implement", "add"],
"labels": ["enhancement", "swarm-feature"],
"agents": ["architect", "coder", "tester"]
},
{
"keywords": ["slow", "performance", "optimize"],
"labels": ["performance", "swarm-optimizer"],
"agents": ["analyst", "optimizer"]
}
]
}
```
### Dynamic Agent Assignment
```bash
# Assign agents based on issue content
npx ruv-swarm github issue-analyze 456 \
--suggest-agents \
--estimate-complexity \
--create-subtasks
```
## Issue Swarm Commands
### Initialize from Issue
```bash
# Create swarm with full issue context using gh CLI
# Get complete issue data
ISSUE=$(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 "$REFERENCES" \
--load-comments \
--analyze-references \
--auto-topology
# Add swarm initialization comment
gh issue comment 456 --body "🐝 Swarm initialized for this issue"
```
### Task Decomposition
```bash
# Break down issue into subtasks with gh CLI
# Get issue body
ISSUE_BODY=$(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 "$BODY
Parent issue: #456" \
--label "subtask"
done
```
### Progress Tracking
```bash
# Update issue with swarm progress using gh CLI
# Get current issue state
CURRENT=$(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 '
"## 📊 Progress Update
**Completion**: \(.completion)%
**ETA**: \(.eta)
### Completed Tasks
\(.completed | map("- ✅ " + .) | join("\n"))
### In Progress
\(.in_progress | map("- 🔄 " + .) | join("\n"))
### Remaining
\(.rTrust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
3e0c089e8335full audit observations/trust-audit/skill/ruvnet__agent-swarm-issue.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-28 | 3e0c089e8335 | CAUTION | B | 89 | first audit |
Questions
What does the Agent Swarm Issue skill do?
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Is Agent Swarm Issue safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Agent Swarm Issue access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (3e0c089e8335), read on 2026-09-28. The repository is watched, and a new audit runs when it changes — this is the first audit.