Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/natea/exomind/smart-agentsgit clone --depth 1 https://github.com/natea/ExoMindWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00393 |
| Opus 5 | $0.00000 | $0.00197 |
| Sonnet 5 | $0.00000 | $0.00079 |
| Haiku 4.5 | $0.00000 | $0.00039 |
Grade A, and why
smart-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to smart-agents — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Smart Agent Auto-Spawning
Purpose
Automatically spawn the right agents at the right time without manual intervention.
Auto-Spawning Triggers
1. File Type Detection
When editing files, agents auto-spawn:
- JavaScript/TypeScript: Coder agent
- Markdown: Researcher agent
- JSON/YAML: Analyst agent
- Multiple files: Coordinator agent
2. Task Complexity
Simple task: "Fix typo"
→ Single coordinator agent
Complex task: "Implement OAuth with Google"
→ Architect + Coder + Tester + Researcher
3. Dynamic Scaling
The system monitors workload and spawns additional agents when:
- Task queue grows
- Complexity increases
- Parallel opportunities exist
Status Monitoring:
// Check swarm health
mcp__claude-flow__swarm_status({
"swarmId": "current"
})
// Monitor agent performance
mcp__claude-flow__agent_metrics({
"agentId": "agent-123"
})
Configuration
MCP Tool Integration
Uses Claude Flow MCP tools for agent coordination:
// Initialize swarm with appropriate topology
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 8,
"strategy": "auto"
})
// Spawn agents based on file type
mcp__claude-flow__agent_spawn({
"type": "coder",
"name": "JavaScript Handler",
"capabilities": ["javascript", "typescript"]
})
Fallback Configuration
If MCP tools are unavailable:
npx claude-flow hook pre-task --auto-spawn-agents
Benefits
- 🤖 Zero manual agent management
- 🎯 Perfect agent selection
- 📈 Dynamic scaling
- 💾 Resource efficiency
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 73 lines · 0 tokens per session scan A 238825c0f83e
smart-agents is a command published in the GitHub repository natea/ExoMind (21 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 393 tokens. A static security scan graded it A with 0 findings. It is 100% identical to smart-agents, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.