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 rpi-challengergit 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/rpi-challenger)<a href="https://agentmods.dev/skills/microsoft/hve-core/rpi-challenger"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/rpi-challenger.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.01391 |
| Opus 5 | $0.00016 | $0.00696 |
| Sonnet 5 | $0.00006 | $0.00278 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
rpi-challenger 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 today.
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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RPI Challenger
Use references/challenge.md for challenge posture, adaptive questioning guidance, and record-update detail.
Goal
Help the user examine a confirmed subject through adaptive, skeptical questions that surface material assumptions, boundaries, evidence needs, and unresolved decisions without turning the active exchange into a review, solution, or coaching session.
Flow
- Form a factual candidate scope from caller-supplied subject, targets, context, and focus. When those inputs are insufficient, inspect only the focused likely targets needed to form a scope, or ask for the smallest missing context.
- Present the candidate scope, related artifacts, and boundary factually. Receive user confirmation before asking challenge questions.
- Create or resume
.copilot-tracking/challenges/{{YYYY-MM-DD}}/{{task_slug}}-challenge.mdfrom templates/challenge-session.md. - Choose challenge angles and their order from the confirmed subject, available evidence, and the user's answers. Use the working challenge coverage in the record to avoid repetition, not as a prescribed checklist.
- During the active exchange, ask one focused, open-ended, non-leading challenge question per turn. Let each answer determine whether to probe, change angle, narrow the boundary, or redirect.
- Update the record with material questions and answers, evidence basis, coverage, and unresolved items. Preserve claim-bearing user language accurately while condensing nonmaterial wording.
- Conclude when the user ends the session or the challenge has saturated. Return the record, coverage, unresolved material, and any advisory next options.
Inputs
subject=...: The task, decision, plan, implementation, requirement, or artifact to challenge.artifacts=...: Optional supplied paths or factual context that define the candidate scope.focus=...: Optional boundary or concern that should receive particular attention.task_slug: Lower-kebab-case identifier derived from the confirmed subject.
What ships with it
2 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.
- today Changed 438b8429790e
- 4d ago First seen · 75 lines · 32 tokens per session scan A a19436147e1b
rpi-challenger is a skill published in the GitHub repository microsoft/hve-core (1,436 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 1,391 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 2026-09-03.
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