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analyzing-source

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Analyzing Source

来源分析

This skill guides you through analyzing a single source in depth and creating a comprehensive summary suitable for research synthesis.
本技能将引导你对单个来源进行深度分析，并创建适用于研究整合的综合摘要。

Process

流程

Follow these steps to analyze a source and create a comprehensive summary:
遵循以下步骤来分析来源并创建综合摘要：

Step 1: Source Discovery and Retrieval

步骤1：来源发现与获取

If given a URL:
  • Fetch it directly using WebFetch
  • Verify the content is accessible and relevant
If given a topic or search query:
  • Use WebSearch to find the best source on the topic
  • Prioritize authoritative, detailed sources
  • Fetch the most relevant result using WebFetch
If source is inaccessible or low-quality:
  • Try alternative sources
  • Be persistent in finding substantive information
  • Note any access issues in your summary
若提供URL：
  • 使用WebFetch直接获取内容
  • 验证内容是否可访问且相关
若提供主题或搜索查询：
  • 使用WebSearch查找该主题的最佳来源
  • 优先选择权威、详尽的来源
  • 使用WebFetch获取最相关的结果
若来源无法访问或质量低下：
  • 尝试替代来源
  • 坚持不懈寻找实质性信息
  • 在摘要中记录任何访问问题

Step 2: Deep Analysis

步骤2：深度分析

Conduct thorough analysis focusing on:
  • Core concepts, definitions, and frameworks presented in the source
  • Main arguments, claims, and findings - what is the source asserting?
  • Evidence, data, and examples - what supports the claims?
  • Methodologies or approaches - how was this work conducted?
  • Limitations, caveats, and counterarguments - what are the boundaries?
  • Connections to broader themes - how does this relate to the research focus?
  • Quality and credibility - how reliable is this source?
  • Unique insights or perspectives - what new understanding does this offer?
进行全面分析，重点关注：
  • 来源中呈现的核心概念、定义和框架
  • 主要论点、主张和发现——来源在断言什么？
  • 证据、数据和示例——哪些内容支撑了这些主张？
  • 方法论或研究方法——这项工作是如何开展的？
  • 局限性、注意事项和反驳论点——适用边界是什么？
  • 与更广泛主题的关联——这与研究重点有何联系？
  • 质量与可信度——该来源的可靠性如何？
  • 独特见解或视角——它带来了哪些新的认知？

Step 3: Create Comprehensive Summary

步骤3：创建综合摘要

Use the template from 
./templates/article-summary.md
to create your summary.
VERY IMPORTANT: Your summary must be concise yet thorough, which means being extreme information-dense and leveraging key data as much as possible.
Template structure includes:
  • Executive summary
  • Key concepts & definitions
  • Main arguments/findings with evidence
  • Methodology/approach
  • Specific examples & case studies
  • Notable quotes
  • Critical evaluation
  • Relevance to research focus
  • Practical implications
Key principles:
  • Include specific quotes and examples, not just paraphrasing
  • Provide analytical insights about significance and relevance
  • Make clear connections to the research focus provided
  • Be detailed enough that someone can understand the source without reading the original
使用
./templates/article-summary.md
中的模板创建你的摘要。
非常重要： 你的摘要必须简洁但全面，这意味着要极致浓缩信息，并尽可能利用关键数据。
模板结构包括：
  • 执行摘要
  • 关键概念与定义
  • 带证据的主要论点/发现
  • 方法论/研究方法
  • 具体示例与案例研究
  • 重要引用
  • 批判性评估
  • 与研究重点的相关性
  • 实际应用意义
核心原则：
  • 包含具体引用和示例，而非仅进行意译
  • 提供关于重要性和相关性的分析见解
  • 明确关联所提供的研究重点
  • 内容需足够详尽，让他人无需阅读原文就能理解该来源

Step 4: Save Summary File

步骤4：保存摘要文件

Create filename:
  • Use a descriptive slug based on the source
  • Example: 
    kubernetes-scaling-patterns.md
    , 
    netflix-chaos-engineering.md
Save location:
  • Save to: 
    {working_directory}/summaries/{filename}.md
  • Use the complete template structure
  • Ensure all sections are filled out
创建文件名：
  • 使用基于来源的描述性短命名
  • 示例：
    kubernetes-scaling-patterns.md
    、
    netflix-chaos-engineering.md
保存位置：
  • 保存至：
    {working_directory}/summaries/{filename}.md
  • 使用完整的模板结构
  • 确保所有部分均已填写

Step 5: Report Results

步骤5：报告结果

Provide a brief report including:
  1. Confirmation of what source you analyzed
  2. The file path where you saved the summary
  3. A 2-3 sentence overview of the most important insights discovered
提供一份简短报告，包含：
  1. 确认你分析的来源
  2. 摘要文件的保存路径
  3. 2-3句话概述所发现的最重要见解

Important Guidelines

重要指南

  • Be thorough, not brief: This is deep research, not light scanning. Capture nuance and detail.
  • Include specific evidence: Direct quotes, data points, examples - not just general statements.
  • Think critically: Note limitations, assess quality, identify assumptions.
  • Stay focused: While being comprehensive, ensure everything relates to the research focus.
  • Be self-contained: Your summary should make sense without reading the original source.
  • Save your work: Always save the summary file - the main coordinator depends on it.
  • 全面详尽，勿浅尝辄止：这是深度研究，而非快速浏览。要捕捉细节和细微差别。
  • 包含具体证据：直接引用、数据点、示例——而非仅泛泛而谈。
  • 批判性思考：记录局限性、评估质量、识别假设。
  • 保持专注：虽然要全面，但需确保所有内容均与研究重点相关。
  • 内容独立完整：你的摘要无需依赖原文就能让人理解。
  • 保存工作成果：务必保存摘要文件——主协调人需要依赖这些文件。

Example Execution

执行示例

Input received:
- Source topic: "Kubernetes horizontal pod autoscaling best practices"
- Research focus: "Scalability patterns in cloud-native systems"
- Working directory: /Users/research/cloud-native-scaling

Step 1: Using WebSearch to find authoritative source on Kubernetes HPA...
Found: kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
Fetching with WebFetch...

Step 2: Analyzing content...
- Identified core HPA concepts: target metrics, scale-up/down policies, cooldown periods
- Found detailed configuration examples with CPU and custom metrics
- Noted limitations around cluster resources and metric collection latency

Step 3: Creating comprehensive summary using template...
- Executive summary: 3 paragraphs covering main patterns and tradeoffs
- Key concepts: HPA, target utilization, metric servers, custom metrics API
- Main findings: 5 configuration patterns with evidence from examples
- 8 notable quotes extracted from official docs and linked blog posts
- Evidence quality: High (official documentation + real-world examples)

Step 4: Saving summary...
Created: /Users/research/cloud-native-scaling/summaries/kubernetes-hpa-best-practices.md

Step 5: Report
Source analyzed: Kubernetes official documentation on Horizontal Pod Autoscaling
Saved to: /Users/research/cloud-native-scaling/summaries/kubernetes-hpa-best-practices.md

Key insights: This source provides detailed HPA configuration patterns with real-world examples from production systems at scale. Most valuable finding is the discussion of custom metrics integration and the tradeoffs between reactive vs predictive scaling approaches. Also documents common pitfalls like resource request misconfiguration causing scaling issues.
收到的输入：
- 来源主题："Kubernetes horizontal pod autoscaling best practices"
- 研究重点："Scalability patterns in cloud-native systems"
- 工作目录：/Users/research/cloud-native-scaling

步骤1：使用WebSearch查找关于Kubernetes HPA的权威来源...
找到：kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
使用WebFetch获取内容...

步骤2：分析内容...
- 确定了HPA的核心概念：目标指标、扩缩容策略、冷却周期
- 找到包含CPU和自定义指标的详细配置示例
- 注意到关于集群资源和指标收集延迟的局限性

步骤3：使用模板创建综合摘要...
- 执行摘要：3个段落，涵盖主要模式和权衡
- 关键概念：HPA、目标利用率、指标服务器、自定义指标API
- 主要发现：5个配置模式，附示例中的证据
- 从官方文档和相关博客文章中提取了8条重要引用
- 证据质量：高（官方文档+真实世界示例）

步骤4：保存摘要...
已创建：/Users/research/cloud-native-scaling/summaries/kubernetes-hpa-best-practices.md

步骤5：报告
分析的来源：Kubernetes官方文档中的Horizontal Pod Autoscaling章节
保存路径：/Users/research/cloud-native-scaling/summaries/kubernetes-hpa-best-practices.md

关键见解：该来源提供了详细的HPA配置模式，以及来自大规模生产系统的真实世界示例。最有价值的发现是关于自定义指标集成的讨论，以及反应式与预测式扩缩容方法之间的权衡。文档还记录了常见陷阱，例如资源请求配置错误导致的扩缩容问题。