Minions Hermes Mission Control

Minions Hermes Mission Control

Skill by ara.so — Hermes Skills collection

Minions is a web-based mission control interface for managing Hermes Agent tasks. It provides a Kanban board to create, supervise, and review autonomous agent work, with features like live streaming, completion judging, and human-in-the-loop verification.

What It Does

• Kanban Board: Visual task management with columns for in-progress, review, and done
• Autonomous Execution: Agents work independently on tasks you define
• Completion Judge: Lightweight LLM evaluates task completion after each turn
• Live Streaming: Real-time view of tool calls, reasoning, and responses
• Human Verification: Tasks require your sign-off before moving to done
• Routines: Manage recurring Hermes jobs with history and output tracking
• File Browser: View agent-created workspace files
• Local-First: Self-hosted with SQLite, no cloud dependency

Installation

Prerequisites: Node.js 18+ and Hermes Agent installed

Quick Start

npx minionsai

The server starts at http://localhost:6969

Check Version

minions --version
npm view minionsai version

Data Storage

• Local SQLite database created on first run
• All state stored in ~/.minions/
• Chat transcripts in Hermes session database
• Task metadata in Minions SQLite

Key Concepts

Tasks

Each task is a persistent Hermes root session. Tasks move through Kanban columns:

• Backlog: Created but not started
• In Progress: Agent actively working
• In Review: Agent proposed completion
• Done: Human verified and closed

Routines

Recurring Hermes jobs that can be scheduled and monitored. Minions tracks:

• Execution history
• Output files
• Success/failure status

Per-Task Configuration

Each task can override:

• Model selection
• Reasoning effort level
• Workspace directory

Configuration

Environment Variables

# API keys for LLM providers (if using completion judge)
export OPENAI_API_KEY=your_openai_key
export ANTHROPIC_API_KEY=your_anthropic_key

# Custom port (default: 6969)
export MINIONS_PORT=8080

# Custom data directory (default: ~/.minions/)
export MINIONS_DATA_DIR=/path/to/data

Settings Page

Access via web UI to configure:

• Default model for new tasks
• Default reasoning effort
• Completion judge settings
• Workspace paths

Usage Patterns

Creating a Task

1. Open Minions at http://localhost:6969
2. Click "New Task" or use the chat interface
3. Describe what you want the agent to do
4. Agent begins autonomous execution

Monitoring Tasks

// Tasks are displayed in real-time Kanban columns
// Watch live streaming output in the task detail view
// Completion judge runs after each agent turn

Reviewing Completions

1. Agent moves task to "In Review" when it believes work is done
2. Review the chat transcript and outputs
3. Either:
   • Approve and move to "Done"
   • Send back with feedback for more work

Creating Routines

// Example: Daily website monitoring routine
{
  name: "Check competitor pricing",
  schedule: "0 9 * * *", // 9 AM daily
  prompt: "Visit competitor.com and extract current pricing, compare to yesterday",
  workspace: "./data/competitive-intel"
}

Practical Examples

Research Task

// Task prompt:
"Research the top 10 AI coding assistants launched in 2025. 
Create a comparison table with: name, pricing, key features, GitHub stars.
Save as markdown in research/ai-coding-tools.md"

// Agent will:
// 1. Search for AI coding assistants
// 2. Visit documentation sites
// 3. Gather pricing and feature data
// 4. Format as markdown table
// 5. Save file and propose completion

Data Collection Routine

// Routine configuration:
{
  name: "Daily HackerNews trending",
  schedule: "0 */6 * * *", // Every 6 hours
  prompt: `
    1. Fetch top 30 HackerNews stories
    2. Extract: title, points, comments, URL
    3. Append to data/hn-trending.jsonl with timestamp
    4. If any story mentions 'AI agents', save separately to data/hn-ai-agents.jsonl
  `,
  workspace: "./scraped-data"
}

Code Generation Task

// Task prompt:
"Create a TypeScript function that:
- Takes an array of URLs
- Fetches each in parallel with retry logic
- Returns success/failure status for each
- Includes proper error handling and types
Save to src/utils/fetch-urls.ts with tests"

// Monitor live as agent:
// - Creates the implementation
// - Adds TypeScript types
// - Writes tests
// - Runs them to verify

Content Pipeline

// Multi-step task:
"1. Read all markdown files in content/drafts/
2. For each draft:
   - Check grammar with language tool
   - Generate 3 title alternatives
   - Create social media preview text
   - Move to content/ready/ when done
3. Create summary report in content/processing-report.md"

CLI Commands

# Start Minions server
npx minionsai

# Start on custom port
MINIONS_PORT=3000 npx minionsai

# Check version
minions --version

# Clear all data (reset)
rm -rf ~/.minions/

Integration with Hermes

Minions connects to your local Hermes Agent installation:

// Minions creates Hermes sessions via API
// Each task = one persistent Hermes root session
// Sessions persist across Minions restarts
// Chat history stored in Hermes's database

Hermes Session Mapping

// Task metadata (Minions SQLite):
{
  id: "task_123",
  title: "Research AI tools",
  status: "in_progress",
  hermesSessionId: "sess_abc", // Reference to Hermes
  model: "claude-3-5-sonnet",
  reasoningEffort: "medium"
}

// Chat transcript: stored in Hermes session database
// File outputs: in workspace directory

File Browser

View agent-created files:

# Files appear in the Minions UI file browser
# Organized by task workspace directory
# Download or view directly from UI

Example workspace structure:

~/.minions/
├── db.sqlite                 # Minions task metadata
└── workspaces/
    ├── task_123/             # Per-task workspace
    │   ├── output.json
    │   └── report.md
    └── task_456/
        └── data.csv

Troubleshooting

Minions won't start

# Check Node.js version
node --version  # Should be 18+

# Check if port is in use
lsof -i :6969

# Use different port
MINIONS_PORT=8080 npx minionsai

Can't connect to Hermes

# Verify Hermes is running
# Check Hermes installation at https://hermes-agent.nousresearch.com

# Hermes typically runs on localhost
# Ensure firewall allows local connections

Task stuck in progress

// Use the UI to:
// 1. View live streaming output
// 2. Check if agent is waiting for input
// 3. Manually intervene via chat
// 4. Or cancel and restart the task

Completion judge not working

// Ensure LLM API key is set:
// export OPENAI_API_KEY=your_key

// Check Settings page for completion judge configuration
// Verify API quota/rate limits

Database corruption

# Backup current data
cp -r ~/.minions/ ~/.minions.backup/

# Reset database
rm ~/.minions/db.sqlite

# Restart Minions (creates fresh database)
npx minionsai

# Note: Hermes session data is preserved

Best Practices

Task Prompts

// ✅ Good: Specific, measurable outcomes
"Scrape product prices from 5 e-commerce sites, 
save to products.json with timestamp, 
compare to last week's data in products-history.json"

// ❌ Bad: Vague, no clear completion criteria
"Research some products"

Workspace Organization

# Use task-specific workspaces
task_research/     # Research outputs
task_scraping/     # Scraped data
task_analysis/     # Analysis reports

# Reference files from prompts:
"Save output to ./results/summary-{date}.md"

Routine Scheduling

// Use cron syntax for schedules
"0 9 * * 1-5"   // 9 AM weekdays
"*/30 * * * *"  // Every 30 minutes
"0 0 * * 0"     // Weekly on Sunday

// Build in error recovery:
"If website is down, retry 3 times with 5-minute delays.
Log failures to errors.log"

Advanced Usage

Custom Completion Criteria

// In task prompt, specify completion:
"Task is ONLY complete when:
1. All 50 URLs have been processed
2. results.json exists and is valid JSON
3. error_count in summary.md is 0
4. All output files are committed to git"

Multi-Agent Workflows

// Create dependent tasks:
// Task 1: "Scrape data, save to data/raw.json"
// Task 2: "Read data/raw.json, analyze, create report.md"
// Task 3: "Read report.md, generate executive summary"

// Monitor all on Kanban board
// Each progresses independently