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/code-reviewgit 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.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 | $0.00000 | $0.02418 |
| Opus 5 | $0.00000 | $0.01209 |
| Sonnet 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 today.
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.
- today 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 7mo 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
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.