rpi-walkthrough

作者: microsoft

引導式、對話式的逐步解說,用於說明程式碼、UI、UX、功能或 .copilot-tracking 產出物,並附有可導覽的證據連結、深入的子代理審查……

npx skills add https://github.com/microsoft/hve-core --skill rpi-walkthrough

RPI Walkthrough

Use references/walkthrough.md for the full walkthrough protocol, segment loop, reference-table format, decisions-and-changes ledger format, and subagent dispatch.

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

  1. Resolve the walkthrough target and detail level from explicit input, attached or open files, then conversation context. Default detail to normal. 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.
  2. Deep review before explaining. Dispatch a generic exploration subagent (Explore, or runSubagent with no named agent) to trace the codebase, UI, UX, feature flow, prompt-engineering artifact, or .copilot-tracking artifact. When the explanation depends on an external library, framework, or standard, activate rpi-research with 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 to detail. Keep review results in the active conversation and subagent returns.
  3. 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.
  4. 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 vscode_askQuestions call 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.
  5. Refine or capture on feedback. When the user asks for more depth or why, repeat the deep review with subagents and tools as needed, 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.
  6. 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.

Inputs

  • target=...: the files, feature, UI or UX area, library, or .copilot-tracking artifact to walk through; infer from attached or open files when not provided.
  • detail={brief|normal|deep}: technical depth of the explanation; default normal; the user can change it mid-session.
  • chat: incorporate conversation context to refine scope before the walkthrough begins.
  • task_slug: lower-kebab-case from the primary target; use it with the current date in YYYY-MM-DD only when creating the dated decisions-and-changes ledger.

Conversation format requirements

  • Use well-formatted markdown in every walkthrough turn. Each segment must begin with a segment header such as ### Segment 1: ... before any narrative explanation.
  • Before the first segment, render an overview Mermaid diagram when the target has meaningful architecture, control or data flow, section relationships, or a user journey. Show that actual target structure and add segment numbers only as navigation cues.
  • During a segment, include a compact focus diagram only when it clarifies real inbound or outbound relationships beyond the overview and prose. Omit it rather than inventing or repeating decorative boxes.
  • Use short labels, meaningful relationship labels where useful, semantic role colors rather than progress colors, and one sentence that states the diagram's takeaway. The labels and prose carry the meaning independently of color. Follow the contrast-safe pattern in references/walkthrough.md.
  • Keep the explanation scannable. Each sentence or paragraph that discusses a specific file, line range, block, or artifact must include a nearby markdown link to that reference, rather than relying only on the reference table.
  • Keep the reference table requirement. Render it near the bottom of each segment turn, immediately before the questions.

Conversation guidance

  • During material walkthrough work, provide concise updates at meaningful boundaries through the target-resolution summary, each segment, ledger update, and closeout. Explain what is being covered and why, what changed or was learned, key decisions, blockers, relevant inline links and reference tables, and one important point the user might otherwise miss. Do not narrate low-level actions.
  • Before a user question, render the segment or decision context first. State viable choices and consequences, an evidence-backed recommendation when available, blockers, and relevant Markdown links, then keep the existing one-or-two-question cadence.
  • Do not use status emojis in walkthrough headings or bullets. The existing prose, headings, inline links, diagrams, and reference tables provide the visual structure.
  • At closeout, separate walkthrough session status from decisions-and-changes ledger state. Summarize covered segments, important updates, decisions, blockers or open entries, and anything the user might otherwise miss.
  • Advise /compact only when stale tool output, superseded reasoning, or completed-segment detail outweighs useful current context and the target and any ledger are current. When advising it, name the state and artifact pointers to retain. Otherwise omit compaction guidance.
  • In a standalone walkthrough, state /rpi-quick or the exact applicable /rpi-* command only when a ledger entry needs downstream work. Otherwise state the explicit no-handoff reason. In an active rpi-quick or confirmed automatic RPI Agent context, return the relevant ledger and evidence to the parent and state that it selects eligible continuation.
  • For the walked target and every relevant existing artifact, use the two-cell row | [actual/workspace-relative/path.ext](actual/workspace-relative/path.ext) | Short description |, using that artifact's actual workspace-relative path as both link text and destination; omit unavailable files and render the table immediately before the final ## Next Steps section. End with ## Next Steps: state the exact eligible user command, active-parent action, blocker-clearing action, or that no user action is required. When compaction is warranted, tell the user to run /compact before the next RPI command; otherwise omit compaction guidance.

Success criteria

  • The target, detail level, and segment plan are resolved before any explanation begins.
  • A deep review through subagents precedes explanation, and its results ground the active conversation.
  • Each segment is explained in the conversation with a segment header, useful target-derived diagrams where they clarify the target, inline markdown links beside the explanatory prose, and a reference table of workspace-relative file and line markdown links rendered before every vscode_askQuestions call and before yielding control.
  • Each vscode_askQuestions turn carries at most one or two clear questions that offer more detail on the current segment or continue to the next.
  • A decisions-and-changes ledger exists only after a material user decision or requested change. Each entry records its reconciliation disposition and outcome or handoff evidence.
  • The final response names covered segments and detail level from conversation context. It reports no artifact when no ledger was needed, or links the ledger and its Reconciliation section with counts and open entries when one exists.

Constraints

  • Read-only by default: explain and capture, and never modify source files unless the user explicitly asks for an immediate change.
  • Deep-review the target with subagents before explaining, and re-review when the user asks for more depth or why before re-explaining.
  • Put the explanation in the conversation window, keep it scannable and easy to follow, and do not present more than one segment at a time.
  • Write every walkthrough explanation, including the question text, in a plain human voice: lead with the point, keep each turn short, avoid em dashes, and avoid filler, promotional or inflated wording, formulaic openers and recaps, over-signposting, decorative formatting, sycophancy, and self-referential asides. Follow the fuller guidance in references/walkthrough.md under "Writing the explanation for human eyes" and "Shape of a segment message".
  • Render file references in the conversation as workspace-relative markdown links with line numbers, not as inline code, and keep .copilot-tracking/ references out of production code, code comments, documentation strings, and commit messages.
  • Keep at most one or two questions per vscode_askQuestions turn.
  • Do not over-condense the walkthrough. When the target is large or nuanced, use more segments rather than forcing a compact summary, and 25 or more segments is acceptable when needed.
  • Reuse existing subagents for review and research rather than duplicating their full work inline; when dispatch tooling is unavailable, perform the equivalent review inline and state the fallback reason in the conversation.
  • Reconcile an open ledger entry with the user as applied now, handed off to an RPI follow-on, deferred for later, or declined. Record the choice and any outcome or evidence pointer. A later request can read the ledger to reconcile open entries, but it does not resume the walkthrough.

Stop rules

  • Stop and ask when no walkthrough target can be resolved from the inputs.
  • Stop and ask the user to choose when multiple unrelated targets match.
  • Pause for the user's direction at each segment boundary through vscode_askQuestions before continuing.
  • Conclude the walkthrough when the user declines another segment, asks for a summary, or ends the session. Review open ledger entries when a ledger exists, then run the closing review and Final response.
  • Hard stop and ask for clarification when an immediate source change is unsafe, ambiguous, destructive, externally visible, or out of scope.

Handoff

For a standalone walkthrough, recommend /rpi-quick or the exact applicable /rpi-research, /rpi-plan, /rpi-implement, or /rpi-review command only for a ledger entry handed off to RPI work or still requiring downstream work. Do not invoke it. State the no-handoff reason when no entry needs downstream work. Return the evidence to rpi-quick or a confirmed automatic RPI Agent parent when one owns continuation.

Final response

Return walkthrough session status, covered segments, detail level, important updates, blockers or open entries, and conditional compaction advice when warranted. If no ledger exists, state that no decisions-and-changes artifact was needed and do not invent a link. If a ledger exists, report its counts of material decisions and requested changes, remaining open entries, and a Markdown link to its Reconciliation section. Recommend RPI follow-on work only for entries handed off or still requiring downstream work. End with the final next steps required by Conversation guidance after the linked target and artifact table.

來自 microsoft 的更多技能

oss-growth
microsoft
開源增長駭客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。适用于使用AzureAIAgentsProvider创建持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应。涵盖函数工具、结构化输出和多工具代理。
development
airunway-aks-setup
microsoft
在AKS上設定AI Runway——從裸叢集到執行模型。涵蓋叢集驗證、控制器安裝、GPU評估、供應商設定及首次部署。時機:「設定AI Runway」、「上線AKS叢集」、「安裝AI Runway」、「airunway設定」、「部署模型至AKS」、「在AKS上進行GPU推論」、「在AKS上設定KAITO」、「在AKS上執行LLM」、「在AKS上使用vLLM」、「在AKS上設定模型服務」、「AI Runway控制器」。
devops
appinsights-instrumentation
microsoft
使用Azure Application Insights檢測Web應用程式的指南。提供遙測模式、SDK設定與組態參考。適用時機:如何檢測應用程式、App Insights SDK、遙測模式、什麼是App Insights、Application Insights指南、檢測範例、APM最佳實踐。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)為瀏覽器/Web應用程式進行檢測。適用於真實使用者監控(RUM)——頁面檢視、點擊、AJAX/fetch依賴、例外、自訂事件,以及與後端OpenTelemetry追蹤關聯的瀏覽器端GenAI代理追蹤。涵蓋SDK載入器指令碼與npm設定、框架擴充(React、React Native、Angular)、點擊分析、遙測初始化器,以及從瀏覽器發出的代理/工具/模型span的OTel GenAI語意慣例。
devops
azure-ai-anomalydetector-java
microsoft
使用適用於 Java 的 Azure AI 異常偵測器 SDK 建置異常偵測應用程式。在實作單變量/多變量異常偵測、時間序列分析或 AI 驅動監控時使用。
development
azure-ai-language-conversations-py
microsoft
使用 azure-ai-language-conversations Python SDK 實作對話語言理解(CLU)。當使用 ConversationAnalysisClient 分析對話意圖與實體、建置 NLP 功能,或將語言理解整合至應用程式時使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用於機器學習工作區、作業、模型、資料集、計算資源與管線。 觸發詞:「azure-ai-ml」、「MLClient」、「workspace」、「model registry」、「training jobs」、「datasets」。
development