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.
git clone --depth 1 https://github.com/ruvnet/ruv-FANNWrote 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/ruvnet/ruv-fann/usage)<a href="https://agentmods.dev/commands/ruvnet/ruv-fann/usage"><img src="https://agentmods.dev/badge/commands/ruvnet/ruv-fann/usage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/ruvnet/ruv-fann/usage"><img src="https://agentmods.dev/badge/commands/ruvnet/ruv-fann/usage.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00249 |
| Opus 5 | $0.00000 | $0.00125 |
| Sonnet 5 | $0.00000 | $0.00050 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
usage 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 8d 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.
What it actually says
Memory Management
🎯 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__ruv-swarm__memory_usage
Parameters
{"detail": "detailed"}
Description
Track persistent memory usage across Claude Code sessions
Details
Memory helps Claude Code:
- Maintain context between sessions
- Remember project decisions
- Track implementation patterns
- Store coordination strategies that worked well
Example Usage
In Claude Code:
- Use the tool:
mcp__ruv-swarm__memory_usage - With parameters:
{"detail": "detailed"} - Claude Code then executes the coordinated plan using its native tools
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.
- 8d ago First seen · 43 lines · 0 tokens per session scan A 0edd74c229d5
usage is a command published in the GitHub repository ruvnet/ruv-FANN (380 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 249 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.