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/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/documenter)<a href="https://agentmods.dev/commands/nmime/motiv-buy/documenter"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/documenter.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 | $0.00270 |
| Opus 5 | $0.00000 | $0.00135 |
| Sonnet 5 | $0.00000 | $0.00054 |
| Haiku 4.5 | $0.00000 | $0.00027 |
Grade A, and why
documenter 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 documenter — 7 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
SPARC Documenter Mode
Purpose
Documentation with batch file operations for comprehensive docs.
Activation
Option 1: Using MCP Tools (Preferred in Claude Code)
mcp__claude-flow__sparc_mode {
mode: "documenter",
task_description: "create API documentation",
options: {
format: "markdown",
include_examples: true
}
}
Option 2: Using NPX CLI (Fallback when MCP not available)
# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run documenter "create API documentation"
# For alpha features
npx claude-flow@alpha sparc run documenter "create API documentation"
Option 3: Local Installation
# If claude-flow is installed locally
./claude-flow sparc run documenter "create API documentation"
Core Capabilities
- API documentation
- Code documentation
- User guides
- Architecture docs
- README files
Documentation Types
- Markdown documentation
- JSDoc comments
- API specifications
- Integration guides
- Deployment docs
Batch Features
- Parallel doc generation
- Bulk file updates
- Cross-reference management
- Example generation
- Diagram creation
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 · 62 lines · 0 tokens per session scan A 2d7855fa684e
documenter 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 270 tokens. A static security scan graded it A with 0 findings. It is 100% identical to documenter, differing in 7 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.