研究综合技能指南：如何高效整合网络信息与权威数据，提升内容可信度与SEO效果

research-synthesis by dhruvbaldawa/ccconfigs

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安装命令

npx skills add https://github.com/dhruvbaldawa/ccconfigs --skill research-synthesis

内容创作信息聚合科研工具

🇨🇳中文介绍

研究综合

使用时机

在以下情况使用：

用户声称需要支持性数据
需要近期案例/趋势
寻找引用或权威来源
从特定 URL 提取信息
查阅技术文档或库 API
在头脑风暴/起草过程中填补知识空白

在以下情况跳过：

信息明显来自个人经验
用户明确表示“不要研究，直接写”
主题纯粹基于观点

关键：严禁捏造

仅使用来自 MCP 工具的真实研究。切勿捏造：

统计数据/百分比
研究名称/研究人员
公司案例/案例分析
技术规格/基准测试
引文/引用

如果未找到数据： ❌ 错误示例：“研究表明 70% 的 OKR 实施失败……” ✅ 正确示例：“我没有关于 OKR 失败率的数据。是否需要我使用 Perplexity 进行研究？”

在添加到脑力转储前：

核实信息来自 MCP 工具结果（而非训练数据）
始终包含来源归属
如果不确定，请说明
不要用假设填补缺失的细节

研究工具选择（优先级顺序）

优先级 1：内置工具（始终优先尝试）

工具	用途	示例

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优先级 2：并行搜索（高级综合）

工具	用途	示例
Parallel Search	使用代理模式进行高级网络搜索、事实核查、竞争情报、多源综合、深度 URL 内容提取	需要综合的复杂查询、跨来源验证、从 URL 提取完整内容

优先级 3：Perplexity（广泛调查）

工具	用途	示例
Perplexity	当 WebSearch/Parallel 不足时进行广泛调查	行业共识、统计数据、多角度观点

优先级 4：Context7（技术文档）

工具	用途	示例
Context7	库/框架文档、API 参考、技术规格	“React useEffect 如何工作？”、“查看最新的 API 文档”

    Need research?
    ├─ Specific URL? → WebFetch → Parallel Search
    ├─ Technical docs/APIs? → Context7
    ├─ General search? → WebSearch → Parallel Search → Perplexity
    └─ Complex synthesis? → Parallel Search

理由： 内置工具（WebFetch, WebSearch）速度更快且始终可用。Parallel Search 提供用于综合和深度内容提取的高级代理模式。Perplexity 在需要时提供广泛的调查。Context7 仅用于官方文档。

寻找模式，而非罗列清单

❌ 错误（数据堆砌）：

    Research shows:
    - Stat 1
    - Stat 2
    - Stat 3

✅ 正确（综合叙述）：

    Found pattern: 3 recent studies show 60-70% OKR failure rates.
    - HBR: 70% failure, metric gaming primary cause
    - McKinsey: >100 OKRs correlate with diminishing returns
    - Google: Shifted from strict OKRs to "goals and signals"
    
    Key insight: Failure correlates with treating OKRs as compliance exercise.

模式：多个来源在哪些方面达成一致？
矛盾：来源在哪些方面存在分歧？
空白：缺少什么信息？
意外发现：什么是出乎意料或反直觉的？

    ## Research
    
    ### OKR Implementation Failures
    60-70% failure rate (HBR, McKinsey). Primary causes: metric gaming, checkbox compliance.
    
    **Sources:**
    - HBR: "Why OKRs Don't Work" - 70% fail to improve performance
    - McKinsey: Survey of 500 companies
    - Google blog: Evolution of goals system
    
    **Key Quote:**
    > "When OKRs become performance evaluation, they stop being planning."
    > - John Doerr, Measure What Matters

研究自然地融入对话：

主动式：“这是一个强有力的主张——让我查一下数据……[使用工具] 直觉不错！找到了 3 项证实的研究。添加到脑力转储。”

请求式：“查找 X……[使用工具] 找到了几个案例。我应该全部添加到脑力转储，还是专注于一种方法？”

起草过程中：“需要引用……[使用工具] 找到了支持性研究。添加到草稿中并注明归属。”

添加到脑力转储

在更新前始终询问（除非上下文清晰）：“找到了 X, Y, Z。是否添加到脑力转储的‘研究’部分下？”

更新以下部分：

研究：研究、数据、引用
示例：具体案例和故事
引文：带有归属的著名引语
来源：完整的参考文献

在添加到脑力转储前：

已综合成叙述，而非仅仅罗列
包含来源归属
与核心论点的相关性明确
突出关键见解/模式
如相关，已注明矛盾/空白

信息过载：综合 3-5 个关键发现，而非 20 个来源
缺少归属：始终为后续参考注明引用
无关研究：找到 ≠ 有用
打断流程：不要为了次要的事实核查打断创造性流程
不加批判地接受：当来源存在分歧/有局限性时需注明

与其他技能的集成

头脑风暴：研究验证/挑战想法
博客写作：提供引用和示例
贯穿始终：用结构化的发现更新 braindump.md

有关详细示例，请参阅 reference/examples.md

🇺🇸English

Research Synthesis

When to Use

Use when:

User claims need supporting data
Need recent examples/trends
Looking for citations or authoritative sources
Extracting info from specific URLs
Checking technical docs or library APIs
Filling knowledge gaps during brainstorming/drafting

Skip when:

Info clearly from personal experience
User says "don't research, just write"
Topic is purely opinion-based

Critical: Never Hallucinate

Only use REAL research from MCP tools. Never invent:

Statistics/percentages
Study names/researchers
Company examples/case studies
Technical specs/benchmarks
Quotes/citations

If no data found: ❌ BAD: "Research shows 70% of OKR implementations fail..." ✅ GOOD: "I don't have data on OKR failure rates. Should I research using Perplexity?"

Before adding to braindump:

Verify came from MCP tool results (not training data)
Include source attribution always
If uncertain, say so
Don't fill in missing details with assumptions

Research Tool Selection (Priority Order)

Priority 1: Built-in Tools (Always Try First)

Tool	Use For	Examples
WebFetch	Specific URLs, extracting article content, user-mentioned sources	User: "Check this article: https://..."
WebSearch	Recent trends/news, statistical data, multiple perspectives, general knowledge gaps	"Recent research on OKR failures", "Companies that abandoned agile"

Priority 2: Parallel Search (Advanced Synthesis)

Tool	Use For	Examples
Parallel Search	Advanced web search with agentic mode, fact-checking, competitive intelligence, multi-source synthesis, deep URL extraction	Complex queries needing synthesis, validation across sources, extracting full content from URLs

Priority 3: Perplexity (Broad Surveys)

Tool	Use For	Examples
Perplexity	Broad surveys when WebSearch/Parallel insufficient	Industry consensus, statistical data, multiple perspectives

Priority 4: Context7 (Technical Docs)

Tool	Use For	Examples
Context7	Library/framework docs, API references, technical specifications	"How does React useEffect work?", "Check latest API docs"

Decision tree:

Need research?
├─ Specific URL? → WebFetch → Parallel Search
├─ Technical docs/APIs? → Context7
├─ General search? → WebSearch → Parallel Search → Perplexity
└─ Complex synthesis? → Parallel Search

Rationale: Built-in tools (WebFetch, WebSearch) are faster and always available. Parallel Search provides advanced agentic mode for synthesis and deep content extraction. Perplexity offers broad surveys when needed. Context7 for official docs only.

Synthesizing Findings

Patterns, Not Lists

❌ Bad (data dump):

Research shows:
- Stat 1
- Stat 2
- Stat 3

✅ Good (synthesized narrative):

Found pattern: 3 recent studies show 60-70% OKR failure rates.
- HBR: 70% failure, metric gaming primary cause
- McKinsey: >100 OKRs correlate with diminishing returns
- Google: Shifted from strict OKRs to "goals and signals"

Key insight: Failure correlates with treating OKRs as compliance exercise.

Look For

Patterns : What do multiple sources agree on?
Contradictions : Where do sources disagree?
Gaps : What's missing?
Surprises : What's unexpected or counterintuitive?

Source Attribution Format

## Research

### OKR Implementation Failures
60-70% failure rate (HBR, McKinsey). Primary causes: metric gaming, checkbox compliance.

**Sources:**
- HBR: "Why OKRs Don't Work" - 70% fail to improve performance
- McKinsey: Survey of 500 companies
- Google blog: Evolution of goals system

**Key Quote:**
> "When OKRs become performance evaluation, they stop being planning."
> - John Doerr, Measure What Matters

Integration with Conversation

Research flows naturally into conversation:

Proactive : "That's a strong claim - let me check data... [uses tool] Good intuition! Found 3 confirming studies. Adding to braindump."

Requested : "Find X... [uses tool] Found several cases. Should I add all to braindump or focus on one approach?"

During Drafting : "Need citation... [uses tool] Found supporting research. Adding to draft with attribution."

Adding to Braindump

Always ask before updating (unless context is clear): "Found X, Y, Z. Add to braindump under Research?"

Update sections:

Research : Studies, data, citations
Examples : Concrete cases and stories
Quotes : Notable quotations with attribution
Sources : Full references

Quality Checklist

Before adding to braindump:

Synthesized into narrative, not just listed
Source attribution included
Relevance to core argument clear
Key insights/patterns highlighted
Contradictions/gaps noted if relevant

Common Pitfalls

Information overload : Synthesize 3-5 key findings, not 20 sources
Missing attribution : Always cite for later reference
Irrelevant research : Found ≠ useful
Breaking flow : Don't interrupt creative flow for minor fact-checks
Uncritical acceptance : Note when sources disagree/have limitations

Integration with Other Skills

brainstorming : Research validates/challenges ideas
blog-writing : Provides citations and examples
Throughout : Update braindump.md with structured findings

For detailed examples, see reference/examples.md

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