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.
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 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/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/microsoft/hve-core/experiment-design"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/experiment-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, 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 37 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
- medium Prompt Injection · line 67 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
- medium Prompt Injection · line 72 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
- medium Prompt Injection · line 76 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
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 | $0.00086 | $0.01886 |
| Opus 5.5 | $0.00034 | $0.00754 |
| Sonnet 5.5 | $0.00017 | $0.00377 |
| Haiku 4.5 | $0.00009 | $0.00189 |
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 1mo 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 — 97 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 |
| rpi-research-preparation.md | Preparing an experiment with bounded prior-art or current-constraint Research while keeping validation and experiment verdicts with their current owners |
| provenance.md | Confirming what is upstream guidance, what is HVE Core derivation or repository convention, and where upstream is silent |
What ships with it
5 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.
- 1mo ago Changed · +1 lines 8b4a9625d7d5
- 2mo 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 (1,606 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,886 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 2026-08-30.
experiment-design compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| experiment-designmicrosoft/hve-core | Skill | 1.6k | today | MIT | A | 86 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |