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/hanhuark/mechanical-engineering-research-skill/me-code-reviewgit clone --depth 1 https://github.com/hanhuark/mechanical-engineering-research-skillWrote 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/hanhuark/mechanical-engineering-research-skill/me-code-review)<a href="https://agentmods.dev/commands/hanhuark/mechanical-engineering-research-skill/me-code-review"><img src="https://agentmods.dev/badge/commands/hanhuark/mechanical-engineering-research-skill/me-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 5.1 | $0.00026 | $0.00221 |
| Opus 5 | $0.00013 | $0.00111 |
| Sonnet 5 | $0.00005 | $0.00044 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
me-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 6d 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.
What it actually says
Thermal-Fluid Research Code Review
Use mechanical-engineering-research for a full review or refactor of research code. Use me-code-sanity for a fast preflight.
Workflow:
- Identify the research question and expected outputs.
- Verify the baseline case is reproducible from raw inputs.
- Check units, assumptions, constants, paths, metadata, and raw-data preservation.
- Separate data processing, analysis, plotting, and simulation/ML execution when practical.
- Add sanity checks based on physics, conservation laws, known correlations, or benchmark cases.
- Confirm figures and tables can be traced back to scripts and processed data.
- Review interfaces, deterministic ordering, tests, environment, package/repository structure, and release readiness.
Expected output:
- code review findings or implementation plan
- reproducibility checklist
- suggested project structure
- tests or sanity checks
- next refactor steps
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.
- 6d ago First seen · 26 lines · 26 tokens per session scan A e7c3894725dc
me-code-review is a command published in the GitHub repository hanhuark/mechanical-engineering-research-skill (16 stars, last pushed 8d ago), licensed MIT. It adds 26 tokens to every session and 221 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-30.
Other commands, from other repositories
security-review
Complete a security review of the pending changes on the current branch.
notebook-review
Comprehensive review of Jupyter notebooks and Python scripts.
check-pr
Check the current branch's PR for external review feedback, fix errors and warnings, run a final external review, and push.
review
Run the adversarial critic against existing code. Default: parent reviews inline, zero subagent spawns. --strict: critic subagent + external review.
external-review
Run a single external code review via an external LLM API on the full branch diff vs main. Standalone usage — no aggregation with internal review.
refactor-clean
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.