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/create-docs)<a href="https://agentmods.dev/commands/nmime/motiv-buy/create-docs"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/create-docs/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/nmime/motiv-buy/create-docs"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/create-docs.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 | $0.03011 |
| Opus 5.5 | $0.00000 | $0.01204 |
| Sonnet 5.5 | $0.00000 | $0.00602 |
| Haiku 4.5 | $0.00000 | $0.00301 |
Grade A, and why
create-docs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are working on the current project. The user has requested to create or regenerate documentation with the arguments: "$ARGUMENTS"
Auto-Loaded Project Context:
@/CLAUDE.md @/docs/ai-context/project-structure.md @/docs/ai-context/docs-overview.md
CRITICAL: AI-Optimized Documentation Principles
All documentation must be optimized for AI consumption and future-proofing:
- Structured & Concise: Use clear sections, lists, and hierarchies. Provide essential information only.
- Contextually Complete: Include necessary context, decision rationale ("why"), and cross-references.
- Pattern-Oriented: Make architectural patterns, conventions, and data flow explicit.
- Modular & Scalable: Structure for partial updates and project growth.
- Cross-references: Link related concepts with file paths, function names, and stable identifiers
Step 1: Analyze & Strategize
Using the auto-loaded project context, analyze the user's request and determine the optimal documentation strategy.
1.1. Parse Target & Assess Complexity
Action: Analyze $ARGUMENTS to identify the target_path and its documentation tier.
Target Classification:
- Tier 3 (Feature-Specific): Paths containing
/src/and ending in/CONTEXT.md - Tier 2 (Component-Level): Paths ending in component root
/CONTEXT.md
Complexity Assessment Criteria:
- Codebase Size: File count and lines of code in target directory
- Technology Mix: Diversity of languages and frameworks (Python, TypeScript, etc.)
- Architectural Complexity: Dependency graph and cross-component imports
- Existing Documentation: Presence and state of any CLAUDE.md files in the area
1.2. Select Strategy
Think deeply about this documentation generation task and strategy based on the auto-loaded project context. Based on the assessment, select and announce the strategy.
Strategy Logic:
- Direct Creation: Simple targets (< 15 files, single tech, standard patterns)
- Focused Analysis: Moderate complexity (15-75 files, 2-3 techs, some novel patterns)
- Comprehensive Analysis: High complexity (> 75 files, 3+ techs, significant architectural depth)
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.
- 1mo ago First seen · 364 lines · 0 tokens per session scan A 533e592cd3b0
create-docs is a command published in the GitHub repository nmime/motiv-buy (0 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,011 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 2026-09-04.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.