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/experiment-designnpx skills add microsoft/hve-core --skill experiment-designgit 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/experiment-design)<a href="https://agentmods.dev/skills/microsoft/hve-core/experiment-design"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/experiment-design.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 | $0.00086 | $0.01840 |
| Opus 5 | $0.00043 | $0.00920 |
| Sonnet 5 | $0.00017 | $0.00368 |
| Haiku 4.5 | $0.00009 | $0.00184 |
Grade A, and why
experiment-design 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design Reference Pack
Goal
Support experiment work end to end: turning unknowns into testable hypotheses, screening out work that is not a real experiment, and scoping it so the result is comparable and decision-ready.
Support the step that precedes it as well: translating a stated business outcome into candidate data-science problem classes with the reasoning that produced them, so a practitioner knows what kind of problem is on the table before deciding what to test.
The two concerns stay distinct. Problem-class framing exposes candidates and never selects one. Experiment work assumes a candidate direction already exists and concludes by selecting an experiment with the team.
This pack is general purpose. It applies to data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments, not to data science alone.
Inputs
- The problem statement, customer context, and business driver
- The stated business outcome, when the active concern is problem-class framing
- Known unknowns, assumptions, and risks
- The decision the experiment is meant to unblock
- Prior experiment results, when a sequence of experiments is in flight
Reference index
Read only the reference that matches the active concern.
| Reference | Read this when |
|---|---|
| problem-framing.md | Translating a stated business outcome into candidate data-science problem classes, applying per-paradigm entry tests, ordering discriminating questions, or recording assignable gaps |
| mve-coaching.md | Framing an MVE, forming or sharpening hypotheses, applying vetting criteria and red flags, designing the experiment, evaluating results, or producing session and backlog-bridge artifacts |
| experiment-readiness.md | Deciding which experiment to run at all: turning a risk landscape into candidates, prioritizing among competing unknowns, comparing options with evidence, or re-prioritizing mid-flight |
| provenance.md | Confirming what is upstream guidance, what is HVE Core derivation or repository convention, and where upstream is silent |
What ships with it
4 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 · 96 lines · 86 tokens per session scan A 647c154915c6
experiment-design is a skill published in the GitHub repository microsoft/hve-core (1,422 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,840 once invoked, about $0.0004 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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