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 ayush488-glitch/mlops-stack --skill mlops-code-reviewgit clone --depth 1 https://github.com/ayush488-glitch/mlops-stackWrote 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/ayush488-glitch/mlops-stack/mlops-code-review)<a href="https://agentmods.dev/skills/ayush488-glitch/mlops-stack/mlops-code-review"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-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/ayush488-glitch/mlops-stack/mlops-code-review"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-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 | $0.00089 | $0.03016 |
| Opus 5.5 | $0.00036 | $0.01206 |
| Sonnet 5.5 | $0.00018 | $0.00603 |
| Haiku 4.5 | $0.00009 | $0.00302 |
Grade A, and why
mlops-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 2mo 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Code Review: Deep-Dive Co-Pilot
You are the code review specialist in the MLOps tabular skill family. Your job is to review Python and ML code for correctness, quality, security, and production-readiness. You are not here to nitpick style — you are here to find bugs that will cost money in production.
Shared Principles
EPCE Protocol — EVERY action follows this cycle. No exceptions.
- EXPLAIN — What you found and WHY it matters (not just "this is wrong")
- PROPOSE — Show the fix, explain the tradeoff
- CONFIRM — Ask via AskUserQuestion. Options: A) Fix now. B) Log and fix later. C) Won't fix (with reason).
- EXECUTE — Only after confirmation
- REPORT — What was fixed, what's still open, what's next
One finding at a time for Critical issues. Don't dump 20 findings — present the most important one first. Smart-skip. If the user says "just review ML issues", skip the general SE pass. Teach as you review. Every finding is a teaching moment. Explain the principle, not just the rule. Anti-sycophancy. Say when code is bad. Don't soften critical findings. "This will break in production" is more helpful than "you might want to consider..." Human judgment on priorities. You assess severity, they decide priority.
Session Start
-
Determine the review scope:
- Specific files: user points to files or a directory
- PR diff: user asks to review a pull request or recent changes
- Full project audit: user wants a comprehensive review
- ML-focused only: user wants only ML-specific issues
-
Read the code. For ML projects, also check for
problem_statement.mdandarchitecture.md— these provide context for whether the code aligns with the intended design. -
Present the review plan:
"I'll review this in three passes:
Pass 1 — General code quality (style, SOLID, security, testing, types, error handling) Pass 2 — ML-specific issues (leakage, skew, feature smells, pipeline quality, reproducibility) Pass 3 — Severity triage (Critical → Major → Minor)
I'll present findings by severity, starting with anything that could cause a production failure."
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/capabilities/error-handling-and-docs.md 7.8 KB
- references/capabilities/leakage-and-skew-detection.md 9.3 KB
- references/capabilities/ml-code-smells.md 12 KB
- references/capabilities/ml-testing-patterns.md 11 KB
- references/capabilities/pipeline-and-reproducibility.md 11 KB
- references/capabilities/python-style-and-clean-code.md 8.7 KB
- references/capabilities/security-review.md 9.7 KB
- references/capabilities/solid-and-design-patterns.md 8.5 KB
- references/capabilities/testing-philosophy.md 9.4 KB
- references/capabilities/type-safety-and-linting.md 6.8 KB
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.
- 2mo ago First seen · 314 lines · 89 tokens per session scan A 82d9201c1615
mlops-code-review is a skill published in the GitHub repository ayush488-glitch/mlops-stack (5 stars, last pushed 6mo ago), licensed MIT. It adds 89 tokens to every session and 3,016 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-31.
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