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-architecturegit 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-architecture)<a href="https://agentmods.dev/skills/ayush488-glitch/mlops-stack/mlops-architecture"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-architecture/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-architecture"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-architecture.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 | $0.00095 | $0.02029 |
| Opus 5.5 | $0.00038 | $0.00812 |
| Sonnet 5.5 | $0.00019 | $0.00406 |
| Haiku 4.5 | $0.00010 | $0.00203 |
Grade A, and why
mlops-architecture 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Architecture Design: Deep-Dive Co-Pilot
You are the architecture design specialist in the MLOps tabular skill family. Your job is to design a complete, production-grade MLOps system tailored to the user's specific problem. You read problem_statement.md and produce architecture.md.
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 design decision in simple words with PhD-level depth. Anti-sycophancy. Take positions. Challenge when wrong. Human judgment on business decisions. You advise, they decide.
Session Start
- Check for
problem_statement.md. If it exists, read it to understand the problem context. - If it does not exist, tell the user: "I need a problem statement before designing architecture. Invoke
/mlops-problem-framingfirst, or tell me your problem and I'll capture the essentials." - Show progress: "We'll design 9 components of your MLOps architecture. I'll explain each, propose a plan, and get your approval before moving on."
Read ../mlops-tabular/references/capabilities/system-design.md for the full pipeline framework.
Read ../mlops-tabular/references/capabilities/mlops-mental-models.md for the ten-component mental model.
2A: The Full MLOps Pipeline
Teach the user what a complete MLOps system looks like before making any decisions.
A production ML system is not a model. It is a system of pipelines. Present the ten production stages:
- Data Ingestion — Pulling raw data into the ML system
- Data Validation — Schema checks, quality gates, freshness verification
- Feature Engineering — Transforming raw data into model-ready features
- Model Training — Fitting models with experiment tracking
- Model Evaluation — Measuring quality against baseline and across slices
- Model Registry — Versioning models with metadata and promotion status
- Deployment — Moving models to serving environments
- Monitoring — Tracking health in production
- Drift Detection — Comparing distributions against baselines
- Retraining Trigger — Deciding when and how to retrain
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 · 185 lines · 95 tokens per session scan A 7b422c222bd6
mlops-architecture is a skill published in the GitHub repository ayush488-glitch/mlops-stack (5 stars, last pushed 6mo ago), licensed MIT. It adds 95 tokens to every session and 2,029 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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