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/natea/fitfinderWrote 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/natea/fitfinder/research)<a href="https://agentmods.dev/commands/natea/fitfinder/research"><img src="https://agentmods.dev/badge/commands/natea/fitfinder/research/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/natea/fitfinder/research"><img src="https://agentmods.dev/badge/commands/natea/fitfinder/research.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 | $0.00405 |
| Opus 5 | $0.00000 | $0.00202 |
| Sonnet 5 | $0.00000 | $0.00081 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
research 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 6d 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
Research Workflow Coordination
Purpose
Coordinate Claude Code's research activities for comprehensive, systematic exploration.
Step-by-Step Coordination
1. Initialize Research Framework
Tool: mcp__claude-flow__swarm_init
Parameters: {"topology": "mesh", "maxAgents": 5, "strategy": "balanced"}
Creates a mesh topology for comprehensive exploration from multiple angles.
2. Define Research Perspectives
Tool: mcp__claude-flow__agent_spawn
Parameters: {"type": "researcher", "name": "Literature Review"}
Tool: mcp__claude-flow__agent_spawn
Parameters: {"type": "analyst", "name": "Data Analysis"}
Sets up different analytical approaches for Claude Code to use.
3. Execute Coordinated Research
Tool: mcp__claude-flow__task_orchestrate
Parameters: {
"task": "Research modern web frameworks performance",
"strategy": "adaptive",
"priority": "medium"
}
4. Store Research Findings
Tool: mcp__claude-flow__memory_usage
Parameters: {
"action": "store",
"key": "research_findings",
"value": "framework performance analysis results",
"namespace": "research"
}
What Claude Code Actually Does
- Uses WebSearch tool for finding resources
- Uses Read tool for analyzing documentation
- Uses Task tool for parallel exploration
- Synthesizes findings using coordination patterns
- Stores insights in memory for future reference
Remember: The swarm coordinates HOW Claude Code researches, not WHAT it finds.
CLI Usage
# Start research workflow via CLI
npx claude-flow workflow research "modern web frameworks"
# Export research workflow
npx claude-flow workflow export research --format json
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.
- 6d ago First seen · 63 lines · 0 tokens per session scan A 37c2bedad99e
research is a command published in the GitHub repository natea/fitfinder (4 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 405 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
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.