agentmods.dev

ml-experimentation

ml-experimentation is a skill for Claude Code from microsoft/hve-core. It costs 55 tokens per session (1,152 once invoked), scanned A, original, MIT.

A reference guide for setting up and reviewing machine-learning experiments, including how to track runs, manage datasets and models, and judge production readiness.

In plain words
What is it for?
Use it when creating ML experimentation infrastructure, organizing repositories and environments, tracking parameters and datasets, or reviewing a model before production.
Why use it?
It gives teams consistent ways to record experiments and reproduce results. It also helps reveal whether a trained model is ready for real-world use.

Skill for Claude Code ✓ vendor

Written for Claude Code: user-invocable in frontmatter.

not rated 1.6krepo +14 today A scan SkillSpector: warn 55 tokens original MIT

Good fit Use it when creating ML experimentation infrastructure, organizing repositories and environments, tracking parameters and datasets, or reviewing a model before production.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microsoft/hve-core/ml-experimentation
View source ↗ microsoft/hve-core
About the project

HVE Core is a collection of agents, prompts, coding instructions, and skills for building repeatable software-development workflows with GitHub Copilot. It is intended for individuals and teams that want structured AI-assisted research, planning, implementation, and review, while the catalogue entries provide many of its reusable workflow components.

microsoft/hve-core · 1,606 stars · on GitHub

Install

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.

Any agent
npx skills add microsoft/hve-core --skill ml-experimentation
Clone the repo
git clone --depth 1 https://github.com/microsoft/hve-core

Made for: Claude Code.

Wrote 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.

agentmods badge for ml-experimentation

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/hve-core/ml-experimentation/github.svg)](https://agentmods.dev/skills/microsoft/hve-core/ml-experimentation)
Your own site
<a href="https://agentmods.dev/skills/microsoft/hve-core/ml-experimentation"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/ml-experimentation/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.

agentmods 80×15 button for ml-experimentation

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,152 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Prompt Injection · line 32
    Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.
    Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.01152
Opus 5.5 $0.00022 $0.00461
Sonnet 5.5 $0.00011 $0.00230
Haiku 4.5 $0.00006 $0.00115

Measured 2mo ago against content hash 29bde774d1bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.github/skills/data-science-engineering/ml-experimentation/SKILL.md · 81 lines

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

Read the full file on GitHub · 81 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 81 lines · 55 tokens per session scan A 29bde774d1bb

Subscribe to this mod's changes

ml-experimentation is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,152 once invoked, about $0.0002 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 2026-08-30.

Analytics and metricsData and AI
Related

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