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 vaquarkhan/data-engineering-agent-skills --skill feature-store-and-ml-data-pipelinesgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-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/skills/vaquarkhan/data-engineering-agent-skills/feature-store-and-ml-data-pipelines)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/feature-store-and-ml-data-pipelines"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/feature-store-and-ml-data-pipelines/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/vaquarkhan/data-engineering-agent-skills/feature-store-and-ml-data-pipelines"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/feature-store-and-ml-data-pipelines.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 | $0.00049 | $0.00462 |
| Opus 5 | $0.00024 | $0.00231 |
| Sonnet 5 | $0.00010 | $0.00092 |
| Haiku 4.5 | $0.00005 | $0.00046 |
Grade A, and why
feature-store-and-ml-data-pipelines 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 12d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Store And ML Data Pipelines
Overview
Use this skill when the platform must support model training and inference safely. It helps agents design feature generation, point-in-time correctness, serving parity, and operational contracts for ML-focused data products.
When to Use
- building training datasets
- designing feature stores or reusable features
- supporting online and offline feature access
- preventing leakage and training-serving mismatch
- publishing model-ready data products
Do not treat feature pipelines as ordinary marts with different names. ML pipelines have different correctness risks.
Workflow
-
Define the feature contract. Include:
- entity key
- feature meaning
- update cadence
- online or offline use
- freshness expectation
-
Protect point-in-time correctness. Training data must only include information available at prediction time.
-
Align offline and online logic. Reuse definitions and validation wherever possible to prevent training-serving drift.
-
Define feature lifecycle and ownership. Clarify:
- producer
- consumers
- deprecation path
- quality monitoring
-
Validate operational behavior. Models break when stale or missing features silently propagate.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We can use the latest value for training." | That often introduces leakage and overstates model performance. |
| "Online parity is a model-team problem." | Feature consistency is a data pipeline responsibility too. |
| "Features are internal, so contracts are unnecessary." | Unclear feature meaning leads to misuse and drift. |
Red Flags
- no point-in-time logic is defined
- offline and online definitions diverge
- stale features are not monitored
- feature ownership is unclear
Verification
- Feature meaning, keys, and freshness are documented
- Point-in-time correctness is protected
- Offline and online parity expectations are explicit
- Monitoring exists for stale, missing, or drifting features
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.
- 12d ago First seen · 69 lines · 49 tokens per session scan A b1c623fbd882
feature-store-and-ml-data-pipelines is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 462 once invoked, about $0.0002 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 2026-08-30.
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