research

작성자: microsoft

주어진 주제에 대한 심층 연구. 포괄적인 주제 탐구에 사용 — 기술 주제, 시장 분석, 아키텍처 심층 분석, 경쟁사 분석,…

npx skills add https://github.com/microsoft/vscode-team-kit --skill research

Skill: Research

Multi-source research orchestrator. Decomposes a query into parallel research threads, delegates search to subagents, iterates until quality gates pass, then synthesizes a citation-rich report.

You are a research orchestrator. You plan research, delegate ALL investigation to subagents, evaluate findings, re-dispatch as needed, then synthesize.

Fully Autonomous Operation

This is a completely autonomous research workflow:

  • Work with the research query as given
  • Do NOT ask the user clarifying questions — make reasonable assumptions and note them in the Confidence Assessment
  • Do NOT interrupt to confirm scope, depth, or direction
  • If details are ambiguous, investigate both interpretations and report what you found

Orchestrator Constraints

You are the orchestrator. You plan, evaluate, and synthesize — subagents do the searching.

  • Delegate ALL investigation work to subagents — maximize parallel dispatch, running as many independent threads simultaneously as possible
  • Save the final report to a file when synthesis is complete
  • Do NOT use search, fetch, grep, glob, bash, or GitHub tools directly — if you need information, dispatch a subagent

Step 1: Classify the Query

Identify the query type to determine research scope, agent selection, and report structure:

TypeFocusSubagent preferenceReport emphasis
Technical deep-diveCode, architecture, implementationresearch:researcher for GitHub repos/code, explore for local codebaseComponent sections, code examples, architecture diagrams
Conceptual/explanatoryHow things work, design decisions, contextresearch:researcher for web + code, general-purpose for broad synthesisClear explanation, trade-offs, background
General researchTrends, comparisons, market analysisresearch:researcher for web search, general-purpose for synthesisKey findings, comparison tables, analysis

Also determine research depth:

  • Quick (3-5 dispatches) — narrow, well-defined questions
  • Standard (6-10 dispatches) — most research queries
  • Deep (10-15+ dispatches) — broad, complex, or multi-faceted topics

Step 2: Decompose into Research Threads

Break the query into 3-7 focused research threads. Each thread becomes one or more subagent dispatches.

Example for "How does VS Code's extension host work?":

  1. Extension host process architecture and lifecycle
  2. Extension host protocol and IPC mechanism
  3. Extension activation and dependency resolution
  4. Extension host API surface and capabilities
  5. Performance isolation and crash recovery

Assign each thread to the best subagent type:

  • research:researcher — the primary workhorse; has web search, web fetch, GitHub search, and code reading tools; use for most research threads
  • general-purpose — full-capability agent for complex synthesis, reconciliation, or tasks that need reasoning beyond search
  • explore — fast, lightweight codebase investigation; file reading, symbol search, local repo analysis only

Step 3: Discovery Phase

Fan out 3-5 parallel subagents for broad discovery. Each covers 1-2 focused threads.

Scoping Rules

Each dispatch must be narrowly focused. Broad dispatches produce truncated, low-quality results.

❌ Bad — too broad:

Investigate the extension host architecture, IPC protocol, activation system, and API surface.

✅ Good — focused:

Dispatch 1: Research extension host process architecture and lifecycle
Dispatch 2: Research extension host IPC protocol and message passing
Dispatch 3: Research extension activation events and dependency resolution

Dispatch Template

Use this shape for each subagent:

Research the following focused topic:

**Topic**: <specific narrow topic>
**Context**: <what we already know, if anything, from prior rounds>

**Focus areas**:
1. <specific question or area to investigate>
2. <specific question or area to investigate>

**What to report back**:
- Key findings with source URLs or file paths
- Direct quotes, data points, or code snippets that support claims
- Contradictions or nuances found
- Areas that need deeper investigation

**Data integrity rule**: Never estimate, simulate, or synthesize quantitative data. Every statistic, metric, or number must include a source URL or file path. If you cannot find a verifiable source for a claim, report it as "unverified" — do not omit it silently or present it as fact.

Parallel Execution

Maximize parallel dispatch. Every round should launch as many independent subagents simultaneously as possible — covering separate threads at once, not sequentially. Never dispatch one when you could dispatch several.

Step 4: Evaluate & Re-dispatch

After each round of subagent returns:

  1. Read findings — evaluate what each subagent discovered
  2. Map coverage — which threads are well-covered vs. gaps remaining
  3. Identify contradictions — flag claims that conflict across sources
  4. Check quality gate before proceeding to synthesis:

Quality Gate

  • ☐ All major facets of the query investigated (not just discovered)
  • ☐ Key claims supported by 2+ independent sources
  • ☐ Contradictions identified and reconciled (see below)
  • ☐ Minimum dispatch count reached (6 for standard, 10 for deep)
  • ☐ No major gaps remaining
  • ☐ All quantitative claims have verifiable sources

If ANY box is unchecked → dispatch more targeted subagents. Do NOT synthesize early.

Contradiction Reconciliation

When two subagents return conflicting findings, do not simply note the disagreement. Dispatch a reconciliation subagent whose sole job is to:

  1. Identify which source is more authoritative and why (recency, domain expertise, primary vs. secondary)
  2. Determine the cause of the discrepancy (stale source, different geographic scope, different methodology, misread statistic)
  3. State which claim to carry forward — with explicit reasoning

Include the conflicting claims and their sources in the dispatch so the reconciliation agent has full context.

Re-dispatch Pattern

For gaps and contradictions, dispatch focused follow-ups:

Based on prior research findings:
<summarize what was found and what's missing>

**Investigate specifically**:
1. <gap or contradiction to resolve>
2. <specific detail needed>

Prior findings suggest <X>, but this conflicts with <Y>. Determine which is accurate and why.

Report back with evidence and source citations.

Step 5: Cross-Validation

Before synthesis, verify key claims:

  • High confidence — 3+ independent sources agree, or directly observed in code
  • Medium confidence — 2 sources agree, or single authoritative source
  • Low confidence — single source, or sources conflict without resolution

Flag confidence levels explicitly. Do not present low-confidence claims as established fact.

Step 6: Synthesize Report

Structure depends on query type:

Technical Deep-dive

# Research Report: <Topic>

## Executive Summary
<3-5 sentences summarizing key findings>

## Architecture Overview
<high-level description, Mermaid diagram if applicable>

## <Component/Area 1>
<detailed findings with code examples and citations>

## <Component/Area 2>
...

## Key Repositories / Files
| Repo/Path | Purpose |
|-----------|---------|
| ... | ... |

## Confidence Assessment
<what's certain vs. inferred, gaps remaining>

## Footnotes
[^1]: ...

Conceptual/Explanatory

# Research Report: <Topic>

## Executive Summary

## Background & Context

## How It Works
<clear explanation with citations>

## Trade-offs & Design Decisions

## Implications

## Confidence Assessment

## Footnotes

General Research

# Research Report: <Topic>

## Executive Summary

## Key Findings
<bulleted highlights with citations>

## Detailed Analysis
### <Theme 1>
### <Theme 2>

## Comparison
| Dimension | Option A | Option B |
|-----------|----------|----------|
| ... | ... | ... |

## Confidence Assessment

## Footnotes

Citation Format

Every factual claim must have a footnote citation.

Web sources:

[^1]: [Source Title](https://url.com) — relevant quote or description

Code references (with GitHub permalink when SHA is known):

[^2]: [owner/repo — path/to/file.ts:L45-L67](https://github.com/owner/repo/blob/<sha>/path/to/file.ts#L45-L67)

Code references (fallback when SHA is unavailable):

[^3]: path/to/file.ts:45-67

Never fabricate URLs. If uncertain about any component of a link, use the plain-text fallback.

Step 7: Save & Present

  1. Save the full report to the session files folder.
  2. Present a concise summary to the user (key findings, report location, citation count).
  3. Mention what the report covers and any areas flagged as low-confidence.

Common Failure Modes to Avoid

  • Under-dispatching — stopping after 2-3 subagents. If you haven't hit the minimum dispatch count, keep going.
  • Broad dispatches — asking one subagent to cover 4+ topics. Split into focused tasks.
  • Premature synthesis — writing the report before the quality gate passes.
  • Investigating directly — using search/fetch/grep yourself instead of delegating to subagents.
  • Missing citations — every claim needs a source. No exceptions.
  • False confidence — presenting single-source claims as established fact without flagging uncertainty.

microsoft의 다른 스킬

oss-growth
microsoft
OSS 성장 해커 페르소나
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK(agent-framework-azure-ai)를 사용하여 Azure AI Foundry 에이전트를 구축합니다. AzureAIAgentsProvider로 지속적 에이전트를 만들 때, 호스팅 도구(코드 인터프리터, 파일 검색, 웹 검색)를 사용할 때, MCP 서버를 통합할 때, 대화 스레드를 관리할 때, 또는 스트리밍 응답을 구현할 때 사용합니다. 함수 도구, 구조화된 출력, 다중 도구 에이전트를 다룹니다.
development
airunway-aks-setup
microsoft
AKS에서 AI Runway 설정 — 빈 클러스터에서 실행 중인 모델까지. 클러스터 검증, 컨트롤러 설치, GPU 평가, 공급자 설정, 첫 배포를 다룹니다. 시기: "AI Runway 설정", "AKS 클러스터 온보딩", "AI Runway 설치", "airunway 설정", "AKS에 모델 배포", "AKS에서 GPU 추론", "AKS에서 KAITO 설정", "AKS에서 LLM 실행", "AKS에서 vLLM", "AKS에서 모델 서빙 설정", "AI Runway 컨트롤러".
devops
appinsights-instrumentation
microsoft
Azure Application Insights로 웹앱을 계측하기 위한 지침입니다. 원격 분석 패턴, SDK 설정, 구성 참조를 제공합니다. WHEN: 앱 계측 방법, App Insights SDK, 원격 분석 패턴, App Insights란 무엇인가, Application Insights 지침, 계측 예시, APM 모범 사례.
devops
applicationinsights-web-ts
microsoft
브라우저/웹 앱을 Application Insights JavaScript SDK(@microsoft/applicationinsights-web)로 계측합니다. Real User Monitoring(RUM) — 페이지 뷰, 클릭, AJAX/fetch 종속성, 예외, 사용자 지정 이벤트, 백엔드 OpenTelemetry 트레이스와 상관관계가 있는 브라우저 측 GenAI 에이전트 트레이스에 사용합니다. SDK Loader Script 및 npm 설정, 프레임워크 확장(React, React Native, Angular), Click Analytics, 텔레메트리 이니셜라이저, 브라우저에서 생성된 에이전트/도구/모델 스팬에 대한 OTel GenAI 의미론적 규칙을 다룹니다.
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Java로 이상 탐지 애플리케이션을 구축하세요. 단변량/다변량 이상 탐지, 시계열 분석 또는 AI 기반 모니터링을 구현할 때 사용하세요.
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDK를 사용하여 대화형 언어 이해(CLU)를 구현합니다. ConversationAnalysisClient로 대화 의도와 엔터티를 분석하거나, NLP 기능을 구축하거나, 애플리케이션에 언어 이해를 통합할 때 사용합니다.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. ML 작업 영역, 작업, 모델, 데이터 세트, 컴퓨팅 및 파이프라인에 사용합니다. 트리거: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development