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/code-review)<a href="https://agentmods.dev/commands/nmime/motiv-buy/code-review"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/code-review/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/code-review"><img src="https://agentmods.dev/badge/commands/nmime/motiv-buy/code-review.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 | $0.02418 |
| Opus 5.5 | $0.00000 | $0.00967 |
| Sonnet 5.5 | $0.00000 | $0.00484 |
| Haiku 4.5 | $0.00000 | $0.00242 |
Grade A, and why
code-review 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 — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/code-review
Performs focused multi-agent code review that surfaces only critical, high-impact findings for solo developers using AI tools.
Core Philosophy
This command prioritizes needle-moving discoveries over exhaustive lists. Every finding must demonstrate significant impact on:
- System reliability & stability
- Security vulnerabilities with real exploitation risk
- Performance bottlenecks affecting user experience
- Architectural decisions blocking future scalability
- Critical technical debt threatening maintainability
🚨 Critical Findings Only
Issues that could cause production failures, security breaches, or severe user impact within 48 hours.
🔥 High-Value Improvements
Changes that unlock new capabilities, remove significant constraints, or improve metrics by >25%.
❌ Excluded from Reports
Minor style issues, micro-optimizations (<10%), theoretical best practices, edge cases affecting <1% of users.
Auto-Loaded Project Context:
@/CLAUDE.md @/docs/ai-context/project-structure.md @/docs/ai-context/docs-overview.md
Command Execution
User provided context: "$ARGUMENTS"
Step 1: Understand User Intent & Gather Context
Parse the Request
Analyze the natural language input to determine:
- What to review: Parse file paths, component names, feature descriptions, or commit references
- Review focus: Identify any specific concerns mentioned (security, performance, etc.)
- Scope inference: Intelligently determine the breadth of review needed
Examples of intent parsing:
- "the authentication flow" → Find all files related to auth across the codebase
- "voice pipeline implementation" → Locate voice processing components
- "recent changes" → Parse git history for relevant commits
- "the API routes" → Identify all API endpoint files
Read Relevant Documentation
Before allocating agents, read the documentation to understand:
- Use
/docs/ai-context/docs-overview.mdto identify relevant docs - Read documentation related to the code being reviewed:
- Architecture docs for subsystem understanding
- API documentation for integration points
- Security guidelines for sensitive areas
- Performance considerations for critical paths
- Build a mental model of risks, constraints, and priorities
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 · 352 lines · 0 tokens per session scan A 197d42854c8b
code-review 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 2,418 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
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
refactor
Systematic code refactoring with smart scope detection.
init
Install the formatters this repository needs, with every command visible before it runs.
spec-trace-requirements
You are verifying that implementation changes trace back to specification requirements.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).