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-researchgit 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-research)<a href="https://agentmods.dev/skills/microsoft/hve-core/rpi-research"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/rpi-research/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/rpi-research"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/rpi-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
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 Excessive Agency · line 75 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00058 | $0.04435 |
| Opus 5.5 | $0.00023 | $0.01774 |
| Sonnet 5.5 | $0.00012 | $0.00887 |
| Haiku 4.5 | $0.00006 | $0.00443 |
Grade A, and why
rpi-research 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 yesterday.
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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RPI Research
Goal
Produce a dated, human-readable primary research artifact that helps the end user understand the result, challenge the evidence, suggest changes, and make any required decision without planning, implementing, or reviewing. Lead with the summary, material discoveries, findings, and alternatives; keep supporting context with the finding that needs it.
Preserve the canonical evidence, decision state, and planning-readiness record beneath that reader-first synthesis. Each executed research cycle completes wider, deeper, and contrarian waves in that order. The artifact, not the chat response, is the durable source of truth.
Use templates/research.md as the primary-artifact skeleton. Read references/research.md for detailed research-posture selection, the three-wave cycle, extension registry, optional helpers, participation protocol, evidence contract, and response guidance. Follow the shared conventions in copilot-tracking.instructions.md.
When the invoking agent or the user's request names the Research task or its slug in lower-kebab-case, use that name verbatim as {{task_slug}}; otherwise derive {{task_slug}} from the primary target with lower-kebab-case. {{YYYY-MM-DD}} is an ISO 8601 calendar date: use the date that the invoking agent or the user's request explicitly supplies for evidence paths, such as a dated session directory's date; otherwise use the current date. The default artifact path is .copilot-tracking/research/{{YYYY-MM-DD}}/{{task_slug}}-research.md. When the caller supplies a trusted sandbox or evidence root, the artifact path is exactly <root>/research/{{YYYY-MM-DD}}/{{task_slug}}-research.md. A pass that continues the current request extends the current document. When the session already holds research for an earlier request, start a new document named for the new request's work instead: use the name the invoking agent gives the work, or a short lower-kebab-case name that describes the work and differs from the session's other document names, such as task-board-polish, in place of {{task_slug}} in the path, and title the document for that work. Continue the session's question, finding, C#, W#, and decision numbering from the earlier documents so every ID stays unique; findings count on in order from the earlier documents' findings. Record which earlier documents the numbering continues from. Record the resolved root, date source, and path before writing, and return a new path so the parent can list it.
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.
- yesterday Changed 7b8903d39cbf
- 9d ago Changed d8561d44484a
- 19d ago Changed · -2 lines ecebe45078f8
- 1mo ago Changed · +3 lines 3900419e86d0
- 1mo ago Changed · +8 tokens per session 587b312fcab2
- 1mo ago Changed · -4 lines c3c55c43bb2f
- 2mo ago Changed · +19 lines b7f9fe514e17
- 2mo ago First seen · 123 lines · 50 tokens per session scan A df146b62cbfd
rpi-research is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 4,435 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 2026-09-03.
rpi-research compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| rpi-researchmicrosoft/hve-core | Skill | 1.6k | today | MIT | A | 58 | 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 |