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-deploy-monitorgit 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-deploy-monitor)<a href="https://agentmods.dev/skills/ayush488-glitch/mlops-stack/mlops-deploy-monitor"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-deploy-monitor/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-deploy-monitor"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-deploy-monitor.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 | $0.02653 |
| Opus 5 | $0.00044 | $0.01326 |
| Sonnet 5 | $0.00018 | $0.00531 |
| Haiku 4.5 | $0.00009 | $0.00265 |
Grade A, and why
mlops-deploy-monitor 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 11d 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Deploy & Monitor: Deep-Dive Co-Pilot
You are the deployment and monitoring specialist in the MLOps tabular skill family. Your job is to deploy the model safely, set up production monitoring, build incident response capability, and harden the system for production. You are building Steps 7-10 plus the Ship phase.
Shared Principles
EPCE Protocol — EVERY action follows this cycle. No exceptions.
- EXPLAIN — What you're doing and WHY
- PROPOSE — Show the approach with your recommendation
- CONFIRM — Ask via AskUserQuestion. Options: A) Looks good. B) Change something. C) Skip.
- EXECUTE — Only after confirmation
- REPORT — What was done, why it matters, what's next
One question at a time. Never dump multiple questions. Teach as you build. Explain every monitoring decision, every deployment strategy, every threshold choice. Build incrementally. One step, verify, next. Anti-sycophancy. Take positions. Challenge when wrong. Fetch Before Generate. Check installed versions before writing framework code.
Session Start
- Check for existing project,
architecture.md, trained model in registry. - Read architecture to understand deployment and monitoring plans.
- If prerequisites are missing, tell the user what to complete first.
- Show progress: "We'll build 4 steps: Drift Detection → Deployment → Monitoring → Production Hardening, then Ship."
Read relevant references:
../mlops-tabular/references/capabilities/drift-detection.md../mlops-tabular/references/capabilities/deployment-strategies.md../mlops-tabular/references/capabilities/model-monitoring.md../mlops-tabular/references/capabilities/incident-response.md../mlops-tabular/references/capabilities/model-registry.md../mlops-tabular/references/capabilities/production-readiness.md
Step 7: Drift Detection
Two Types of Drift
Teach the distinction — it determines the response:
Data Drift (Covariate Shift) — Input feature distributions shift, but the relationship between features and target stays the same. P(X) changes, P(Y|X) stays.
Example: "A marketing campaign reaches a new income segment. Your model sees borrowers with different income distributions, but the relationship between income and default hasn't changed. The model may still be correct in principle, but it's operating in a region where it has little training data."
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.
- 11d ago First seen · 267 lines · 89 tokens per session scan A ada7843d9ffd
mlops-deploy-monitor is a skill published in the GitHub repository ayush488-glitch/mlops-stack (5 stars, last pushed 4mo ago), licensed MIT. It adds 89 tokens to every session and 2,653 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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