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/researchgit 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/research)<a href="https://agentmods.dev/commands/nmime/motiv-buy/research"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/research.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 | $0.00781 |
| Opus 5 | $0.00000 | $0.00391 |
| Sonnet 5 | $0.00000 | $0.00156 |
| Haiku 4.5 | $0.00000 | $0.00078 |
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 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 research — 179 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.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Swarm Strategy
Purpose
Deep research through parallel information gathering.
Activation
Using MCP Tools
// Initialize research swarm
mcp__claude -
flow__swarm_init({
topology: 'mesh',
maxAgents: 6,
strategy: 'adaptive',
});
// Orchestrate research task
mcp__claude -
flow__task_orchestrate({
task: 'research topic X',
strategy: 'parallel',
priority: 'medium',
});
Using CLI (Fallback)
npx claude-flow swarm "research topic X" --strategy research
Agent Roles
Agent Spawning with MCP
// Spawn research agents
mcp__claude -
flow__agent_spawn({
type: 'researcher',
name: 'Web Researcher',
capabilities: ['web-search', 'content-extraction', 'source-validation'],
});
mcp__claude -
flow__agent_spawn({
type: 'researcher',
name: 'Academic Researcher',
capabilities: ['paper-analysis', 'citation-tracking', 'literature-review'],
});
mcp__claude -
flow__agent_spawn({
type: 'analyst',
name: 'Data Analyst',
capabilities: ['data-processing', 'statistical-analysis', 'visualization'],
});
mcp__claude -
flow__agent_spawn({
type: 'documenter',
name: 'Report Writer',
capabilities: ['synthesis', 'technical-writing', 'formatting'],
});
Research Methods
Information Gathering
// Parallel information collection
mcp__claude -
flow__parallel_execute({
tasks: [
{ id: 'web-search', command: 'search recent publications' },
{ id: 'academic-search', command: 'search academic databases' },
{ id: 'data-collection', command: 'gather relevant datasets' },
],
});
// Store research findings
mcp__claude -
flow__memory_usage({
action: 'store',
key: 'research-findings-' + Date.now(),
value: JSON.stringify(findings),
namespace: 'research',
ttl: 604800, // 7 days
});
Analysis and Validation
// Pattern recognition in findings
mcp__claude -
flow__pattern_recognize({
data: researchData,
patterns: ['trend', 'correlation', 'outlier'],
});
// Cognitive analysis
mcp__claude -
flow__cognitive_analyze({
behavior: 'research-synthesis',
});
// Cross-reference validation
mcp__claude -
flow__quality_assess({
target: 'research-sources',
criteria: ['credibility', 'relevance', 'recency'],
});
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 · 160 lines · 0 tokens per session scan A 6b14511839c4
research 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 781 tokens. A static security scan graded it A with 0 findings. It is 100% identical to research, differing in 179 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.