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/readme)<a href="https://agentmods.dev/commands/nmime/motiv-buy/readme"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/readme/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/readme"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/readme.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 | $0.01358 |
| Opus 5.5 | $0.00000 | $0.00543 |
| Sonnet 5.5 | $0.00000 | $0.00272 |
| Haiku 4.5 | $0.00000 | $0.00136 |
Grade A, and why
README 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔧 Command Templates
Orchestration templates that enable Claude Code to coordinate multi-agent workflows for different development tasks.
Overview
After reading the main kit documentation, you'll understand how these commands fit into the integrated system. Each command:
- Auto-loads the appropriate documentation tier for its task
- Spawns specialized agents based on complexity
- Integrates MCP servers when external expertise helps
- Maintains documentation to keep AI context current
🚀 Automatic Context Injection
All commands benefit from automatic context injection via the subagent-context-injector.sh hook:
- Core documentation auto-loaded: Every command and sub-agent automatically receives
@/docs/CLAUDE.md,@/docs/ai-context/project-structure.md, and@/docs/ai-context/docs-overview.md - No manual context loading: Sub-agents spawned by commands automatically have access to essential project documentation
- Consistent knowledge: All agents start with the same foundational understanding
Available Commands
📊 /full-context
Purpose: Comprehensive context gathering and analysis when you need deep understanding or plan to execute code changes.
When to use:
- Starting work on a new feature or bug
- Need to understand how systems interconnect
- Planning architectural changes
- Any task requiring thorough analysis before implementation
How it works: Adaptively scales from direct analysis to multi-agent orchestration based on request complexity. Agents read documentation, analyze code, map dependencies, and consult MCP servers as needed.
🔍 /code-review
Purpose: Get multiple expert perspectives on code quality, focusing on high-impact findings rather than nitpicks.
When to use:
- After implementing new features
- Before merging important changes
- When you want security, performance, and architecture insights
- Need confidence in code quality
How it works: Spawns specialized agents (security, performance, architecture) that analyze in parallel. Each agent focuses on critical issues that matter for production code.
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 · 182 lines · 0 tokens per session scan A 39f774fa7657
README 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 1,358 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.