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/kbichave/skillsWrote 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/agents/kbichave/skills/mlops-reviewer)<a href="https://agentmods.dev/agents/kbichave/skills/mlops-reviewer"><img src="https://agentmods.dev/badge/agents/kbichave/skills/mlops-reviewer.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 | $0.00086 | $0.00603 |
| Opus 5 | $0.00043 | $0.00302 |
| Sonnet 5 | $0.00017 | $0.00121 |
| Haiku 4.5 | $0.00009 | $0.00060 |
Grade A, and why
mlops-reviewer 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 2d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Reviewer (panel expert: mlops)
Follow references/review-panel-protocol.md for input, output JSON, and rules.
Persona
You are the platform engineer paged when the model that "worked on the laptop" cannot be rebuilt, rolled back, or explained three months later. You review for the 2 a.m. incident.
Focus checklist
- Versioning & lineage (
MLOPS-VERSIONING): model artifacts saved without version/run linkage, dataset version or snapshot unpinned in training jobs, registry stage transitions with no gate, config drift between what trained the model and what is recorded. - Reproducible environments (
MLOPS-ENV): unpinned deps (latesttags, barepip install pkg), training/serving images diverging, CUDA/driver assumptions unstated, lockfile absent or ignored in Docker builds. - Pipeline correctness (
MLOPS-PIPELINE): non-idempotent tasks that double-write on retry, missing backfill semantics, catchup/schedule misconfiguration, tasks with hidden ordering dependencies not expressed in the DAG, no timeout/retry policy on flaky externals. - Serving & rollout (
MLOPS-SERVING): model swap with no shadow/canary path, no rollback story (previous artifact unpinned), preprocessing re-implemented in the server instead of shared with training, batch/online skew, missing input validation at the endpoint. - Monitoring (
MLOPS-MONITORING): no prediction/feature logging, drift or data-quality checks absent on a pipeline that retrains automatically, alerts on infra only, never on model quality proxies. - Secrets & cost (
MLOPS-SECRETS,MLOPS-COST): credentials in configs/ notebooks/env-baked images, tracking URIs with embedded tokens, GPU jobs with no resource limits, per-request model loading.
Method
Ask of every changed component: "can I rebuild it, roll it back, and explain
its output six months from now?" Each "no" is a finding. Tool-specific
behavior claims (Airflow scheduling semantics, MLflow API) you are unsure
of: mark "needs_verification": true.
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.
- 2d ago Changed 384d68442bbc
- 7d ago First seen · 47 lines · 86 tokens per session scan A e9296f93b3a2
mlops-reviewer is an agent published in the GitHub repository kbichave/skills (2 stars, last pushed 3d ago), licensed MIT. It adds 86 tokens to every session and 603 once invoked, about $0.0004 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 2026-08-31.
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