OpenGauss is a project-scoped Lean workflow orchestrator that gives coding agents a command-line interface for managing formal proof and formalization tasks. It is used with Lean projects to coordinate agents, tooling, backend sessions, and workflows supplied by lean4-skills. The catalogue add-ons operate these Gauss-native workflows.
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 skills add math-inc/OpenGauss --skill requesting-code-reviewgit clone --depth 1 https://github.com/math-inc/OpenGaussWrote 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/skills/math-inc/opengauss/requesting-code-review)<a href="https://agentmods.dev/skills/math-inc/opengauss/requesting-code-review"><img src="https://agentmods.dev/badge/skills/math-inc/opengauss/requesting-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/skills/math-inc/opengauss/requesting-code-review"><img src="https://agentmods.dev/badge/skills/math-inc/opengauss/requesting-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01458 |
| Opus 5 | $0.00014 | $0.00729 |
| Sonnet 5 | $0.00006 | $0.00292 |
| Haiku 4.5 | $0.00003 | $0.00146 |
Grade A, and why
requesting-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.
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requesting Code Review
Overview
Dispatch a reviewer subagent to catch issues before they cascade. Review early, review often.
Core principle: Fresh perspective finds issues you'll miss.
When to Request Review
Mandatory:
- After each task in subagent-driven development
- After completing a major feature
- Before merge to main
- After bug fixes
Optional but valuable:
- When stuck (fresh perspective)
- Before refactoring (baseline check)
- After complex logic implementation
- When touching critical code (auth, payments, data)
Never skip because:
- "It's simple" — simple bugs compound
- "I'm in a hurry" — reviews save time
- "I tested it" — you have blind spots
Review Process
Step 1: Self-Review First
Before dispatching a reviewer, check yourself:
- Code follows project conventions
- All tests pass
- No debug print statements left
- No hardcoded secrets or credentials
- Error handling in place
- Commit messages are clear
# Run full test suite
pytest tests/ -q
# Check for debug code
search_files("print(", path="src/", file_glob="*.py")
search_files("console.log", path="src/", file_glob="*.js")
# Check for TODOs
search_files("TODO|FIXME|HACK", path="src/")
Step 2: Gather Context
# Changed files
git diff --name-only HEAD~1
# Diff summary
git diff --stat HEAD~1
# Recent commits
git log --oneline -5
Step 3: Dispatch Reviewer Subagent
Use delegate_task to dispatch a focused reviewer:
delegate_task(
goal="Review implementation for correctness and quality",
context="""
WHAT WAS IMPLEMENTED:
[Brief description of the feature/fix]
ORIGINAL REQUIREMENTS:
[From plan, issue, or user request]
FILES CHANGED:
- src/models/user.py (added User class)
- src/auth/login.py (added login endpoint)
- tests/test_auth.py (added 8 tests)
REVIEW CHECKLIST:
- [ ] Correctness: Does it do what it should?
- [ ] Edge cases: Are they handled?
- [ ] Error handling: Is it adequate?
- [ ] Code quality: Clear names, good structure?
- [ ] Test coverage: Are tests meaningful?
- [ ] Security: Any vulnerabilities?
- [ ] Performance: Any obvious issues?
OUTPUT FORMAT:
- Summary: [brief assessment]
- Critical Issues: [must fix — blocks merge]
- Important Issues: [should fix before merge]
- Minor Issues: [nice to have]
- Strengths: [what was done well]
- Verdict: APPROVE / REQUEST_CHANGES
""",
toolsets=['file']
)
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 · 270 lines · 28 tokens per session scan A 923203c930fa
requesting-code-review is a skill published in the GitHub repository math-inc/OpenGauss (1,260 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 1,458 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 2026-09-03.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.