--- id: daily-synthesize-research name: "synthesize-research" url: https://skills.yangsir.net/skill/daily-synthesize-research author: anthropics domain: product tags: ["user-stories", "product-management", "data-synthesis"] install_count: 3000 rating: 4.40 (20 reviews) github: https://github.com/anthropics/knowledge-work-plugins --- # synthesize-research > 从访谈记录、问卷调查等多源数据中提取关键洞察，整合成结构化的用户研究报告 **Stats**: 3,000 installs · 4.4/5 (20 reviews) ## Before / After 对比 ### 用户研究综合 **Before**: 阅读 20 份访谈记录并手动标记关键观点，整理成 Excel 表格统计模式，一个研究项目需要 2 天完成分析 **After**: 自动解析访谈和问卷数据，提取高频主题和用户痛点，生成带引用的结构化报告，2 小时完成深度洞察分析 | Metric | Before | After | Change | |---|---|---|---| | 分析时间 | 2天/小时 | 2天/小时 | -88% | ## Readme # synthesize-research # Synthesize Research If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](https://github.com/anthropics/knowledge-work-plugins/blob/HEAD/product-management/skills/synthesize-research/../../CONNECTORS.md). Synthesize user research from multiple sources into structured insights and recommendations. ## Usage ``` /synthesize-research $ARGUMENTS ``` ## Workflow ### 1. Gather Research Inputs Accept research from any combination of: - **Pasted text**: Interview notes, transcripts, survey responses, feedback - **Uploaded files**: Research documents, spreadsheets, recordings summaries - **~~knowledge base** (if connected): Search for research documents, interview notes, survey results - **~~user feedback** (if connected): Pull recent support tickets, feature requests, bug reports - **~~product analytics** (if connected): Pull usage data, funnel metrics, behavioral data - **~~meeting transcription** (if connected): Pull interview recordings, meeting summaries, and discussion notes Ask the user what they have: - What type of research? (interviews, surveys, usability tests, analytics, support tickets, sales call notes) - How many sources / participants? - Is there a specific question or hypothesis they are investigating? - What decisions will this research inform? ### 2. Process the Research For each source, extract: - **Key observations**: What did users say, do, or experience? - **Quotes**: Verbatim quotes that illustrate important points - **Behaviors**: What users actually did (vs what they said they do) - **Pain points**: Frustrations, workarounds, and unmet needs - **Positive signals**: What works well, moments of delight - **Context**: User segment, use case, experience level ### 3. Identify Themes and Patterns Apply thematic analysis — see **Research Synthesis Methodology** below for detailed guidance on thematic analysis, affinity mapping, and triangulation techniques. Group observations into themes, count frequency across participants, and assess impact severity. Note contradictions and surprises. Create a priority matrix: - **High frequency + High impact**: Top priority findings - **Low frequency + High impact**: Important for specific segments - **High frequency + Low impact**: Quality-of-life improvements - **Low frequency + Low impact**: Note but deprioritize ### 4. Generate the Synthesis Produce a structured research synthesis: #### Research Overview - Methodology: what types of research, how many participants/sources - Research question(s): what we set out to learn - Timeframe: when the research was conducted #### Key Findings For each major finding (aim for 5-8): - **Finding statement**: One clear sentence describing the insight - **Evidence**: Supporting quotes, data points, or observations (with source attribution) - **Frequency**: How many participants/sources support this finding - **Impact**: How significantly this affects the user experience or business - **Confidence level**: High (strong evidence), Medium (suggestive), Low (early signal) Order findings by priority (frequency x impact). #### User Segments / Personas If the research reveals distinct user segments: - Segment name and description - Key characteristics and behaviors - Unique needs and pain points - Size estimate if data is available #### Opportunity Areas Based on the findings, identify opportunity areas: - What user needs are unmet or underserved - Where do current solutions fall short - What new capabilities would unlock value - Prioritized by potential impact #### Recommendations Specific, actionable recommendations: - What to build, change, or investigate further - Tied back to specific findings - Prioritized by impact and feasibility #### Open Questions What the research did not answer: - Gaps in understanding - Areas needing further investigation - Suggested follow-up research methods ### 5. Review and Extend After generating the synthesis: - Ask if any findings need more detail or different framing - Offer to generate specific artifacts: persona documents, opportunity maps, research presentations - Offer to create follow-up research plans for open questions - Offer to draft product implications (how findings should influence the roadmap) ## Research Synthesis Methodology ### Thematic Analysis The core method for synthesizing qualitative research: - **Familiarization**: Read through all the data. Get a feel for the overall landscape before coding anything. - **Initial coding**: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later. - **Theme development**: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question. - **Theme review**: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story? - **Theme refinement**: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures. - **Report**: Write up the themes as findings with supporting evidence. ### Affinity Mapping A collaborative method for grouping observations: - **Capture observations**: Write each distinct observation, quote, or data point as a separate note - **Cluster**: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data. - **Label clusters**: Give each cluster a descriptive name that captures the common thread - **Organize clusters**: Arrange clusters into higher-level groups if patterns emerge - **Identify themes**: The clusters and their relationships reveal the key themes **Tips for affinity mapping**: - One observation per note. Do not combine multiple insights. - Move notes between clusters freely. The first grouping is rarely the best. - If a cluster gets too large, it probably contains multiple themes. Split it. - Outliers are interesting. Do not force every observation into a cluster. - The process of grouping is as valuable as the output. It builds shared understanding. ### Triangulation Strengthen findings by combining multiple data sources: - **Methodological triangulation**: Same question, different methods (interviews + survey + analytics) - **Source triangulation**: Same method, different participants or segments - **Temporal triangulation**: Same observation at different points in time A finding supported by multiple sources and methods is much stronger than one supported by a single source. When sources disagree, that is interesting — it may reveal different user segments or contexts. ## Interview Note Analysis ### Extracting Insights from Interview Notes For each interview, identify: **Observations**: What did the participant describe doing, experiencing, or feeling? - Distinguish between behaviors (what they do) and attitudes (what they think/feel) - Note context: when, where, with whom, how often - Flag workarounds — these are unmet needs in disguise **Direct quotes**: Verbatim statements that powerfully illustrate a point - Good quotes are specific and vivid, not generic - Attribute to participant type, not name: "Enterprise admin, 200-person team" not "Sarah" - A quote is evidence, not a finding. The finding is your interpretation of what the quote means. **Behaviors vs stated preferences**: What people DO often differs from what they SAY they want - Behavioral observations are stronger evidence than stated preferences - If a participant says "I want feature X" but their workflow shows they never use similar features, note the contradiction - Look for revealed preferences through actual behavior **Signals of intensity**: How much does this matter to the participant? - Emotional language: frustration, excitement, resignation - Frequency: how often do they encounter this issue - Workarounds: how much effort do they expend working around the problem - Impact: what is the consequence when things go wrong ### Cross-Interview Analysis After processing individual interviews: - Look for patterns: which observations appear across multiple participants? - Note frequency: how many participants mentioned each theme? - Identify segments: do different types of users have different patterns? - Surface contradictions: where do participants disagree? This often reveals meaningful segments. - Find surprises: what challenged your prior assumptions? ## Survey Data Interpretation ### Quantitative Survey Analysis - **Response rate**: How representative is the sample? Low response rates may introduce bias. - **Distribution**: Look at the shape of responses, not just averages. A bimodal distribution (lots of 1s and 5s) tells a different story than a normal distribution (lots of 3s). - **Segmentation**: Break down responses by user segment. Aggregates can mask important differences. - **Statistical significance**: For small samples, be cautious about drawing conclusions from small differences. - **Benchmark comparison**: How do scores compare to industry benchmarks or previous surveys? ### Open-Ended Survey Response Analysis - Treat open-ended responses like mini interview notes - Code each response with themes - Count frequency of themes across responses - Pull representative quotes for each theme - Look for themes that appear in open-ended responses but not in structured questions — these are things you did not think to ask about ### Common Survey Analysis Mistakes - Reporting averages without distributions. A 3.5 average could mean everyone is lukewarm or half love it and half hate it. - Ignoring non-response bias. The people who did not respond may be systematically different. - Over-interpreting small differences. A 0.1 point change in NPS is noise, not signal. - Treating Likert scales as interval data. The difference between "Strongly Agree" and "Agree" is not necessarily the same as between "Agree" and "Neutral." - Confusing correlation with causation in cross-tabulations. ## Combining Qualitative and Quantitative Insights ### The Qual-Quant Feedback Loop - **Qualitative first**: Interviews and observation reveal WHAT is happening and WHY. They generate hypotheses. - **Quantitative validation**: Surveys and analytics reveal HOW MUCH and HOW MANY. They test hypotheses at scale. - **Qualitative deep-dive**: Return to qualitative methods to understand unexpected quantitative findings. ### Integration Strategies - Use quantitative data to prioritize qualitative findings. A theme from interviews is more important if usage data shows it affects many users. - Use qualitative data to explain quantitative anomalies. A drop in retention is a number; interviews reveal it is because of a confusing onboarding change. - Present combined evidence: "47% of surveyed users report difficulty with X (survey), and interviews reveal this is because Y (qualitative finding)." ### When Sources Disagree - Quantitative and qualitative sources may tell different stories. This is signal, not error. - Check if the disagreement is due to different populations being measured - Check if stated preferences (survey) differ from actual behavior (analytics) - Check if the quantitative question captured what you think it captured - Report the disagreement honestly and investigate further rather than choosing one source ## Persona Development from Research ### Building Evidence-Based Personas Personas should emerge from research data, not imagination: - **Identify behavioral patterns**: Look for clusters of similar behaviors, goals, and contexts across participants - **Define distinguishing variables**: What dimensions differentiate one cluster from another? (e.g., company size, technical skill, usage frequency, primary use case) - **Create persona profiles**: For each behavioral cluster: Name and brief description - Key behaviors and goals - Pain points and needs - Context (role, company, tools used) - Representative quotes - **Validate with data**: Can you size each persona segment using quantitative data? ### Persona Template ``` [Persona Name] — [One-line description] Who they are: - Role, company type/size, experience level - How they found/started using the product What they are trying to accomplish: - Primary goals and jobs to be done - How they measure success How they use the product: - Frequency and depth of usage - Key workflows and features used - Tools they use alongside this product Key pain points: - Top 3 frustrations or unmet needs - Workarounds they have developed What they value: - What matters most in a solution - What would make them switch or churn Representative quotes: - 2-3 verbatim quotes that capture this persona's perspective ``` ### Common Persona Mistakes - Demographic personas: defining by age/gender/location instead of behavior. Behavior predicts product needs better than demographics. - Too many personas: 3-5 is the sweet spot. More than that and they are not actionable. - Fictional personas: made up based on assumptions rather than research data. - Static personas: never updated as the product and market evolve. - Personas without implications: a persona that does not change any product decisions is not useful. ## Opportunity Sizing ### Estimating Opportunity Size For each research finding or opportunity area, estimate: - **Addressable users**: How many users could benefit from addressing this? Use product analytics, survey data, or market data to estimate. - **Frequency**: How often do affected users encounter this issue? (Daily, weekly, monthly, one-time) - **Severity**: How much does this issue impact users when it occurs? (Blocker, significant friction, minor annoyance) - **Willingness to pay**: Would addressing this drive upgrades, retention, or new customer acquisition? ### Opportunity Scoring Score opportunities on a simple matrix: - **Impact**: (Users affected) x (Frequency) x (Severity) = impact score - **Evidence strength**: How confident are we in the finding? (Multiple sources > single source, behavioral data > stated preferences) - **Strategic alignment**: Does this opportunity align with company strategy and product vision? - **Feasibility**: Can we realistically address this? (Technical feasibility, resource availability, time to impact) ### Presenting Opportunity Sizing - Be transparent about assumptions and confidence levels - Show the math: "Based on support ticket volume, approximately 2,000 users per month encounter this issue. Interview data suggests 60% of them consider it a significant blocker." - Use ranges rather than false precision: "This affects 1,500-2,500 users monthly" not "This affects 2,137 users monthly" - Compare opportunities against each other to create a relative ranking, not just absolute scores ## Output Format Use clear headers and structured formatting. Each finding should stand on its own — a reader should be able to read any single finding and understand it without reading the rest. ## Tips - Let the data speak. Do not force findings into a predetermined narrative. - Distinguish between what users say and what they do. Behavioral data is stronger than stated preferences. - Quotes are powerful evidence. Include them generously, with attribution to participant type (not name). - Be explicit about confidence levels. A finding from 2 interviews is a hypothesis, not a conclusion. - Contradictions in the data are interesting, not inconvenient. They often reveal distinct user segments. - Recommendations should be specific enough to act on. "Improve onboarding" is not actionable. "Add a progress indicator to the setup flow" is. - Resist the temptation to synthesize too many themes. 5-8 strong findings are better than 20 weak ones. Weekly Installs296Repository[anthropics/know…-plugins](https://github.com/anthropics/knowledge-work-plugins)GitHub Stars10.5KFirst Seen14 days agoSecurity Audits[Gen Agent Trust HubPass](/anthropics/knowledge-work-plugins/synthesize-research/security/agent-trust-hub)[SocketPass](/anthropics/knowledge-work-plugins/synthesize-research/security/socket)[SnykPass](/anthropics/knowledge-work-plugins/synthesize-research/security/snyk)Installed oncodex285gemini-cli282opencode281github-copilot280amp280cursor280 --- *Source: https://skills.yangsir.net/skill/daily-synthesize-research* *Markdown mirror: https://skills.yangsir.net/api/skill/daily-synthesize-research/markdown*