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 agentmods add skills/microsoft/hve-core/ml-experimentationnpx skills add microsoft/hve-core --skill ml-experimentationgit clone --depth 1 https://github.com/microsoft/hve-coreWrote 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/microsoft/hve-core/ml-experimentation)<a href="https://agentmods.dev/skills/microsoft/hve-core/ml-experimentation"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/ml-experimentation.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 | $0.00055 | $0.01152 |
| Opus 5 | $0.00028 | $0.00576 |
| Sonnet 5 | $0.00011 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
Grade A, and why
ml-experimentation 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 3d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Experimentation Reference Pack
Goal
Ground machine learning experimentation in the Microsoft CSE engineering playbook so that environment setup, repository structure, experiment tracking, dataset and model abstractions, evaluation flow, and production-readiness review are applied consistently and attributed accurately.
This pack is machine learning specific. It assumes a model is being trained, tracked, evaluated, or assessed for production. General experiment framing, hypothesis formation, and vetting belong to experiment-design.
Inputs
- The ML experimentation setup under discussion: environments, repository layout, tracking framework, or evaluation flow
- The model under assessment and its training and evaluation history, when readiness is the question
- Existing dataset versioning, parameter tracking, and environment capture practice
- The engagement stage, since the production checklist has a lifecycle precondition
Reference index
Read only the reference that matches the active concern.
| Reference | Read this when |
|---|---|
| model-experimentation.md | Standing up virtual environments, repository and notebook structure, experiment tracking and reproducibility, dataset and model abstractions, or evaluation flow |
| ml-checklists.md | Checking ML engagement fundamentals or assessing whether a trained model is ready to move toward production |
| provenance.md | Confirming what is upstream guidance, what is HVE Core derivation or repository convention, and where upstream is silent |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 81 lines · 55 tokens per session scan A 29bde774d1bb
ml-experimentation is a skill published in the GitHub repository microsoft/hve-core (1,422 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,152 once invoked, about $0.0003 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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