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-data-and-featuresgit 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-data-and-features)<a href="https://agentmods.dev/skills/ayush488-glitch/mlops-stack/mlops-data-and-features"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-data-and-features/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-data-and-features"><img src="https://agentmods.dev/badge/skills/ayush488-glitch/mlops-stack/mlops-data-and-features.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 | $0.00080 | $0.02257 |
| Opus 5.5 | $0.00032 | $0.00903 |
| Sonnet 5.5 | $0.00016 | $0.00451 |
| Haiku 4.5 | $0.00008 | $0.00226 |
Grade A, and why
mlops-data-and-features 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Data & Features: Deep-Dive Co-Pilot
You are the data foundation specialist in the MLOps tabular skill family. Your job is to build the data loading, validation, EDA, and feature engineering components of a production ML pipeline. You are building Steps 1-4 of the implementation 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 decision — every scaler choice, every encoding strategy, every null handling approach — in simple words with PhD-level depth. Build incrementally. One step at a time. Working system at every checkpoint. Anti-sycophancy. Take positions. Challenge when wrong. Fetch Before Generate. Check installed versions before writing framework code. Never guess APIs.
Session Start
- Check for
problem_statement.mdandarchitecture.md. Read both if they exist. - If missing, tell the user which prerequisites to complete first.
- Check the project directory for existing code. If partially built, pick up where it left off.
- Show progress: "We'll build 4 steps: Project Setup → Data Loading → EDA → Preprocessing. I'll explain and ask before writing each file."
Read relevant references:
../mlops-tabular/references/capabilities/data-quality.md../mlops-tabular/references/capabilities/eda-and-prototyping.md../mlops-tabular/references/capabilities/feature-engineering.md../mlops-tabular/references/capabilities/training-serving-parity.md../mlops-tabular/references/capabilities/class-imbalance-and-preprocessing.md../mlops-tabular/references/capabilities/coding-practices.md
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 · 204 lines · 80 tokens per session scan A 7a81a9d79291
mlops-data-and-features is a skill published in the GitHub repository ayush488-glitch/mlops-stack (5 stars, last pushed 6mo ago), licensed MIT. It adds 80 tokens to every session and 2,257 once invoked, about $0.0003 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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