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/nmime/motiv-buy/agentsgit clone --depth 1 https://github.com/nmime/motiv-buyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/nmime/motiv-buy/agents)<a href="https://agentmods.dev/commands/nmime/motiv-buy/agents"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/agents.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00000 | $0.00305 |
| Opus 5 | $0.00000 | $0.00152 |
| Sonnet 5 | $0.00000 | $0.00061 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
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 yesterday.
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 agents — 8 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
List Active Patterns
🎯 Key Principle
This tool coordinates Claude Code's actions. It does NOT write code or create content.
MCP Tool Usage in Claude Code
Tool: mcp__claude-flow__agent_list
Parameters
{
"swarmId": "current"
}
Description
View all active cognitive patterns and their current focus areas
Details
Filters:
- all: Show all defined patterns
- active: Currently engaged patterns
- idle: Available but unused patterns
- busy: Patterns actively coordinating tasks
Example Usage
In Claude Code:
- List all agents: Use tool
mcp__claude-flow__agent_list - Get specific agent metrics: Use tool
mcp__claude-flow__agent_metricswith parameters{"agentId": "coder-123"} - Monitor agent performance: Use tool
mcp__claude-flow__swarm_monitorwith parameters{"interval": 2000}
Important Reminders
- ✅ This tool provides coordination and structure
- ✅ Claude Code performs all actual implementation
- ❌ The tool does NOT write code
- ❌ The tool does NOT access files directly
- ❌ The tool does NOT execute commands
See Also
- Main documentation: /CLAUDE.md
- Other commands in this category
- Workflow examples in /workflows/
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.
- yesterday First seen · 53 lines · 0 tokens per session scan A 854a09acd47b
agents is a command published in the GitHub repository nmime/motiv-buy (0 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 305 tokens. A static security scan graded it A with 0 findings. It is 100% identical to agents, differing in 8 lines, and is treated as a copy.
Other commands, from other repositories
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.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.