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-walkthroughgit 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-walkthrough)<a href="https://agentmods.dev/skills/microsoft/hve-core/rpi-walkthrough"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/rpi-walkthrough/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-walkthrough"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/rpi-walkthrough.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00066 | $0.02728 |
| Opus 5.5 | $0.00026 | $0.01091 |
| Sonnet 5.5 | $0.00013 | $0.00546 |
| Haiku 4.5 | $0.00007 | $0.00273 |
Grade A, and why
rpi-walkthrough 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RPI Walkthrough
Use references/walkthrough.md for the full walkthrough protocol, segment loop, reference-table format, decisions-and-changes ledger format, and deep review.
Follow the shared conventions in copilot-tracking.instructions.md.
Goal
Walk the user through a target one segment at a time, explaining what each line or block does and why with navigable evidence links. Keep target refinement, detail, pacing, current position, and follow-up depth in the conversation. Capture a material user decision or requested change in a narrow ledger only when one occurs, then reconcile it with the user without editing source by default.
A target is source code, UI or UX wiring, a library or feature, a prompt-engineering artifact such as a prompt, instructions, agent, or skill, or a .copilot-tracking artifact such as a research, plan, changes, review, or log document.
When a ledger is needed, derive {{task_slug}} in lower-kebab-case from the primary target's main subject, such as the primary file's base name without its extension or the feature or area name. Use the current date in YYYY-MM-DD and create .copilot-tracking/walkthroughs/{{YYYY-MM-DD}}/{{task_slug}}-decisions.md from templates/walkthrough.md.
Execution
- Resolve the walkthrough target and detail level from explicit input, attached or open files, then conversation context. Default
detailtonormal. When chat context is enabled, incorporate it to refine scope. If no target can be formed, stop and ask; if multiple unrelated targets match, ask the user to choose one. When prior conversation context is unavailable, ask the user for the target and desired starting point instead of reconstructing progress from a ledger. - Deep review before explaining. Trace the codebase, UI, UX, feature flow, prompt-engineering artifact, or
.copilot-trackingartifact with the available search and read tools; a subagent is optional for a large trace, runs only when a dispatch tool is available and the user's direction allows it, and returns a set of leads to confirm, as described in the reference. Run that subagent onClaude Haiku 5.5 (copilot)atmaxreasoning effort, passed as dispatch parameters when the tool accepts them, unless the user names another model. If a subagent dispatch fails because its model does not support the subagent's reasoning effort, retry it once withreasoning_effortset tomedium. When the explanation depends on an external library, framework, or standard, activaterpi-researchwith the walkthrough topic, purpose, audience, questions, evidence criteria, scope, constraints, supplied evidence, requested outputs, and analysis output mode. Read its primary artifact before explaining and scale the review depth todetail. Keep review results in the active conversation. - Plan coherent segments in the conversation: entry point through flow and key blocks for code, or section order for artifacts. Keep their order, pacing, and coverage in conversation context.
- Explain one segment at a time in the conversation: write a clear, scannable explanation of what it does, how it connects, and why it is this way, and follow the human-voice writing guidance in the reference. Start each segment with a segment header; before the first segment, render an overview Mermaid diagram when the target has meaningful structure or flow; add a compact focus diagram only when it adds information beyond the overview and prose. Include inline markdown links beside the explanatory prose for any file, block, or artifact discussed, then render a reference table of file and line links for that segment. Render the full segment turn as visible chat text before every call to the host's question tool, such as
ask_userorvscode_askQuestions, and before yielding control: the segment header, any useful diagrams, inline links, and reference table appear first, and one or two questions come last in that same turn. - Refine or capture on feedback. When the user asks for more depth or why, repeat the deep review with the available tools, then re-explain. When the user makes a material decision or requests a change, lazily create the decisions-and-changes ledger from the template, append the entry, and offer immediate reconciliation or continuing with the entry open within the existing one-or-two-question cadence. Do not edit the codebase unless the user explicitly chooses immediate reconciliation and the change is safely scoped.
- Close once all segments are covered or the user ends early. If a ledger exists, review open entries and ask whether to reconcile them now or leave them for later, then return the Final response. Do not persist segment coverage, completion status, or resumption data.
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 5e43edcc6a01
- 19d ago Changed · +4 lines b96675808c49
- 1mo ago Changed · -1 lines · +1 tokens per session c89c9057751d
- 2mo ago First seen · 94 lines · 65 tokens per session scan A e0c7169a997b
rpi-walkthrough is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 2,728 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 2026-09-03.
rpi-walkthrough compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| rpi-walkthroughmicrosoft/hve-core | Skill | 1.6k | today | MIT | A | 66 | Claude Code |
| hr-onboardingnexu-io/open-design | Skill | 107k | today | Apache-2.0 | A | 62 | Claude Code, Codex |
| book-mirrorgarrytan/gbrain | Skill | 32k | today | MIT | A | 138 | Claude Code |
| exam-forecastanthropics/claude-for-legal | Skill | 9.9k | yesterday | Apache-2.0 | A | 73 | Claude Code |
| lean-formalizewanshuiyin/Auto-claude-code-research-in-sleep | Skill | 19k | today | MIT | A | 81 | Claude Code |
| eli5companion-inc/feynman | Skill | 10k | yesterday | MIT | A | 63 | Claude Code, Codex |
| deck-course-modulenexu-io/html-anything | Skill | 9.5k | today | Apache-2.0 | A | 25 | Claude Code, Codex |