winui-session-report

作者: microsoft

分析當前或最近的代理工作階段(GitHub Copilot CLI 或 Claude Code),並生成診斷報告。在請求工作階段回饋時使用,…

npx skills add https://github.com/microsoft/win-dev-skills --skill winui-session-report

Session Analysis Report

Generate a diagnostic report for an agent session by running the Analyze-Session.ps1 script included with this skill. The script auto-detects whether the current session was produced by GitHub Copilot CLI or Claude Code from environment variables and on-disk file format, and dispatches to the appropriate parser. If neither harness can be detected, the script exits with a clear error.

[!IMPORTANT] Run this skill only when the user explicitly asks for session analysis or a report. If it is loaded without an explicit request, do not inspect session data; explain what the report contains and wait for confirmation.

Privacy and sensitivity — surface this guidance to the user

Analyze-Session.ps1 always:

  1. Embeds a "Privacy and sensitivity" section at the top of the generated session-report.md (right above the Overview table), and
  2. Prints a yellow PRIVACY NOTICE banner to the console when it finishes writing the file.

You (the agent) must surface this guidance to the user in your response — do not let it stay buried in script output the user might not have read. When you finish running the script and reporting the findings, include a short privacy reminder in your reply to the user, in plain second-person language. Use this template, adapting wording as needed:

⚠️ Heads-up before you share session-report.md — this file contains your unredacted session transcript: file contents and paths the agent read or edited, your prompts verbatim (including any secrets you may have pasted), tool output, environment values, and local paths under C:\Users\<you>\…. You're responsible for what you share — please open the file in your editor and read it end-to-end before attaching it to a public issue, posting it in chat, or sending it outside your organization. Redact anything sensitive. If you only need to share the high-level metrics, ask me to summarize the file instead of attaching it.

If the user only wants the high-level metrics (turn counts, skill usage, build success rate) without the per-turn detail, summarize the report and share the summary instead of the file — and tell the user that's what you're doing so they don't have to read it themselves to confirm.

Steps

  1. Run the analysis script to generate the report:
# Analyze the most recent session (auto-detects harness) and save report
.\Analyze-Session.ps1 -OutputFile session-report.md

# Or analyze a specific session by ID (searched in both harness locations)
.\Analyze-Session.ps1 -SessionId "<session-id>" -OutputFile session-report.md

# Or analyze a transcript file directly (format sniffed from content)
.\Analyze-Session.ps1 -EventsFile <path-to-transcript.jsonl> -OutputFile session-report.md

# Force a specific format if auto-detection picks the wrong harness
.\Analyze-Session.ps1 -Format ClaudeCode -OutputFile session-report.md

# Skip subagent transcripts (Claude Code only) for a parent-only view
.\Analyze-Session.ps1 -SkipSubagents -OutputFile session-report.md

Detection rules:

  • The current session is preferred when an explicit ID is available: COPILOT_AGENT_SESSION_ID (Copilot CLI) or CLAUDE_SESSION_ID (Claude Code) take priority over "most recently modified" so a parallel session in another terminal can't shadow the one the skill was invoked from.
  • Environment first: CLAUDECODE=1 or CLAUDE_CODE_ENTRYPOINT -> Claude Code; COPILOT_* env vars -> Copilot.
  • For Claude Code, the most-recent JSONL whose cwd matches the current working directory is preferred.
  • For an explicit -EventsFile, the format is sniffed from the first events.
  • If neither harness is detected, the script exits with a non-zero status and a message naming both supported locations.
  1. Review the generated report — read session-report.md and summarize key findings for the user:

    • How many turns, how long, token usage
    • What skills were loaded and when
    • Build/run workflow and command-failure pattern
    • Any stuck patterns or tooling issues detected
  2. Add your own observations — append a section to the report with any additional context:

    • Was the final app working? What's missing?
    • Quality assessment of the generated code
    • Suggestions specific to what went wrong
  3. Include any tooling improvements or recommendations based on the analysis.

    • Are there rules that need to be added to the Roslyn analyzer to prevent common mistakes detected during the session?
    • Were there bugs or issues with winapp new, project-mode winapp run, winapp find-ui, or the BuildAndRun.ps1 wrapper?
    • Are there features that could be added to lower the number of turns required to complete a task?

What the Report Covers

SectionDetails
OverviewHarness, session ID, model, duration, turns, tokens (incl. cache tokens for Claude Code)
PromptThe original user request
Turn BreakdownTurns and tokens by category (building, coding, exploring, subagent dispatch, etc.)
SkillsWhich were invoked and when, including from inside subagent transcripts
Subagents(Claude Code only) Per-agent breakdown of dispatched subagents and their work
Build AnalysisBuild-capable workflow attempts, command failures/errors, and whether project-mode winapp run / BuildAndRun.ps1 was used
Stuck PatternsBuild loops, repeated file reads, obj/ clean cycles
Tooling IssuesAuto-detected improvement opportunities
Turn DetailEvery turn with tools used and errors flagged, parent and subagent transcripts shown separately

When to Use

  • When the user asks for a session report to understand what happened during an agent session.

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