rpi-research

rpi-research is a skill for Claude Code from microsoft/hve-core. It costs 50 tokens per session (3,230 once invoked), scanned A, original, MIT.

A research workflow for gathering evidence, comparing options, and framing a coding task before planning begins.

In plain words
What is it for?
Use it to investigate an uncertain task, record sources and alternatives, and produce findings that are ready for implementation planning.
Why use it?
It keeps research findings in a dated file so decisions and supporting evidence are available later instead of being lost in chat.

Skill for Claude Code ✓ vendor

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions subagents.

Good fit Use it to investigate an uncertain task, record sources and alternatives, and…

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Install with agentmods
npx agentmods add skills/microsoft/hve-core/rpi-research
About the project

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.

microsoft/hve-core · 1,436 stars · on GitHub

Install

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.

Any agent
npx skills add microsoft/hve-core --skill rpi-research
Clone the repo
git clone --depth 1 https://github.com/microsoft/hve-core

Made for: Claude Code.

Wrote 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.

agentmods badge for rpi-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/hve-core/rpi-research.svg)](https://agentmods.dev/skills/microsoft/hve-core/rpi-research)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,230 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00050 $0.03230
Opus 5 $0.00025 $0.01615
Sonnet 5 $0.00010 $0.00646
Haiku 4.5 $0.00005 $0.00323

Measured 3d ago against content hash df146b62cbfd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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 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.

.github/skills/rpi/rpi-research/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

rpi-research

Goal

Produce a dated, primary research artifact that gives the caller evidence, parent-owned decision state, and planning readiness without planning, implementing, or reviewing. 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, participation protocol, evidence contract, and response guidance. Follow the shared conventions in copilot-tracking.instructions.md.

Derive {{task_slug}} from the primary target with lower-kebab-case and use the current date in YYYY-MM-DD. The default artifact path is .copilot-tracking/research/YYYY-MM-DD/{{task_slug}}-research.md. A caller-provided trusted sandbox or evidence root may mirror research/YYYY-MM-DD/{{task_slug}}-research.md; record the resolved root before writing.

Flow

  1. Establish the research brief in the primary artifact: topic, purpose, audience or use, scope and non-goals, criteria, requested outputs, output mode, initial questions, research posture and its provenance, and any explicit limits or deadline. Infer an initial topic only when the conversation provides enough context, and label assumptions for verification.
  2. Determine applicable extensions at intake.
    • Apply matching instruction files by applyTo glob to the research inputs and evidence path.
    • Identify domain skills whose descriptions match the topic or evidence need.
    • Identify research-specialist subagents by stable frontmatter name, routing description, and host visibility or registration.
    • Record every relevant instruction, skill, and specialist as selected or skipped with its provenance and scoped authority or output contract.
  3. Resolve extensions in this order:
    1. Platform and host safety
    2. Explicit caller scope and criteria
    3. Matching repository instructions and enforced schemas
    4. This rpi-research contract
    5. Domain skills and specialists
    6. Examples and preferences Extensions may add scoped criteria or evidence. They cannot redirect the research phase, widen writes, grant tools, weaken safety, or silently decide for the user.
  4. Use the native vscode_askQuestions tool for an optional intake checkpoint only when an answer about topic, scope, criteria, or priorities would materially change the research.
    1. Batch a small set of decision-relevant questions and prefer fixed options with a freeform choice where useful.
    2. Do not request secrets.
    3. When inputs are sufficient or interaction is unavailable, continue and record the no-interaction rationale.
  5. Establish the current cycle before research action.
    1. Run the prior-knowledge gate, decompose answerable questions, classify independent uncertainties, and resolve a proportionate research posture from the brief and evidence.
      • expansive: apply no preset upper limit. Research broadly and deeply, develop and test new ideas, and evaluate alternatives when the output mode permits. Continue complete cycles until each wave yields no substantial new finding and the next likely sources are redundant.
      • balanced: investigate adjacent material beyond the immediate task when it could improve the answer, including new ideas and alternatives. Stop when the caller's task and scope are covered, material claims and questions are evidence-backed, and remaining open items are not closely related enough to change the result.
      • focused: investigate deeply within the caller's task and scope. Widen only when clear evidence shows that broader research could materially change the result; use native vscode_askQuestions and persist approval before crossing that boundary.
      • Prefer focused or balanced for a bounded internal task with named source targets and supplied failure evidence. Use expansive when the brief is broad, the decision space is materially unknown, or the caller or applicable codebase instructions select it.
    2. Record active caller direction controls, including additions, changes, narrowed scope, exclusions, discarded directions, selected posture and provenance, and explicit limits or deadline. When uncertainty would materially affect the research, use native vscode_askQuestions and persist the answer before continuing.
    3. Before substantive search or delegation, persist the canonical opening state in its owning sections, then send the opening update defined in Conversation guidance.
    4. Delegate only a named independent uncertainty whose isolated investigation materially improves evidence quality, parallelism, or context control. Keep tightly coupled or low-volume wave work inline. Use RPI Researcher as the default general worker for a delegated internal, external, or hybrid lane. Select a discovered specialist only when its routing description fits the uncertainty and its stable name, host visibility or registration, independent-lane fit, and output-contract fit support the dispatch.
    5. Pass each worker the cycle number, wave type, topic, one bounded lane, questions, criteria, scope, research posture, explicit limits, an exact caller-approved candidate lane path under the parent-approved research/subagents path or a mirrored trusted subagents path, and the distinct parent primary artifact path.
    6. Parallelize only independent lanes. When suitable dispatch is unavailable, investigate the focused lane inline and record the fallback.
  6. Complete all three waves in order for each executed cycle. Do not stop the cycle after early evidence appears sufficient.
    1. Wider: investigate inline or dispatch named independent uncertainties to identify breadth for ideas, conjectures, hypotheses, claims, and questions, including relevant libraries, frameworks, APIs, schemas, contracts, standards, current resources, current decisions or documentation, and potential evidence.
    2. Deeper: parent-prioritize the material from Wider, then investigate inline or dispatch named independent uncertainties for key details, findings, evidence, examples, schemas, APIs, contracts, standards, patterns, practices, and relevant code or visual style.
    3. Contrarian: investigate inline or dispatch named independent uncertainties to seek credible counter-evidence and in-scope alternatives that challenge the active ideas, conjectures, hypotheses, claims, and questions. Honor caller exclusions and specific-only boundaries.
    4. Reflect after each material search or worker return as a separate action. Keep worker returns compact, lift evidence into the primary artifact rather than duplicating raw output, and apply the material-update decision rules in references/research.md.
  7. Parent-synthesize the completed cycle. Map findings to questions and stable C# and W# evidence IDs. The parent alone records accepted, rejected, and deferred material with evidence-based rationale; workers provide evidence and synthesis pointers without selecting a final recommendation or decision state. Record alternatives, current and unresolved decisions, risks, potential further research, Planning Readiness, and Research disposition.
    • In convergence mode, select one recommendation only when the evidence supports it.
    • In analysis, audit, or comparison mode, record the decision state without selecting an implementation recommendation outside caller intent.
    • In research-only or no-handoff mode, record the evidence and explicit no-handoff reason.
    • The parent owns evidence-state classification and any user update. Workers provide evidence relationships without classifying evidence state or deciding whether a message is useful.
    • Use references/research.md to record whether the selected output mode supports planning and to determine continuation.
  8. Evaluate whether another complete three-wave cycle is required under the selected posture. Repeat the full cycle when evidence is missing for material claims, conjectures remain unclear, hypotheses are untested or unresolved, required examples, APIs, schemas, contracts, or links are missing, or contrarian evidence weakens earlier material or introduces material questions. Do not impose a fixed cycle ceiling. When an explicit caller or codebase limit prevents a needed cycle, record the gap and readiness honestly.
  9. After a completed cycle, use vscode_askQuestions only when a proposed direction, further-research choice, or material finding would significantly change the research. Persist answers, unanswered questions, resulting decisions, and selected further-research items before continuing.
  10. When useful, offer a conversational walkthrough in the final response and use the primary artifact as its navigable source of truth. Reserve vscode_askQuestions for the material research decisions in steps 4, 5, and 9.

Read the full file on GitHub · 123 lines

Files

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.

Changes

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.

  1. 3d ago First seen · 123 lines · 50 tokens per session scan A df146b62cbfd

Subscribe to this mod's changes

rpi-research is a skill published in the GitHub repository microsoft/hve-core (1,436 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 3,230 once invoked, about $0.0003 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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