product-research

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product-research

产品研究

Product / user research as an operational discipline: choosing the right method, sizing it honestly, and synthesizing findings into governed insights. The core rule: method must match the goal, and an insight requires recurrence across independent participants — a single quote is an anecdote.

将产品/用户研究作为一项运营规范：选择合适的方法、合理确定研究规模，并将研究结果整合为可管控的洞察。核心原则：方法必须匹配目标，且洞察需要在独立参与者中重复出现——单一用户的表述只是轶事。

Purpose

用途

Product researchers, ResearchOps teams, and PMs running discovery need method rigor and an insight repository they can trust. This skill structures three decisions:

Three deterministic tools:

```
study_designer.py
```
— Maps (research goal × product stage) to an appropriate method and emits a method-matched plan skeleton (objective, participant criteria, guide structure, success criteria). Redirects live A/B to
```
product-team/experiment-designer
```
.
```
saturation_planner.py
```
— Method-based sample guidance with an explicit confidence label: Nielsen problem-discovery (5/segment), Guest et al. thematic saturation (~12), and evaluative coverage. Never claims a prevalence rate from a small-n usability test.
```
insight_synthesizer.py
```
— Clusters coded observations by tag, counts distinct participants, ranks by cross-participant recurrence, and flags any candidate below the source threshold as an ANECDOTE, never promoting it to an insight.

产品研究员、ResearchOps团队以及开展探索工作的产品经理，需要严谨的研究方法和可信的洞察存储库。该技能围绕三个决策构建体系：

三个确定性工具：

```
study_designer.py
```
—— 将（研究目标 × 产品阶段）映射为合适的方法，并生成匹配方法的规划框架（目标、参与者标准、指南结构、成功标准）。若涉及实时A/B测试，将重定向至
```
product-team/experiment-designer
```
。
```
saturation_planner.py
```
—— 基于方法提供样本量指导，并附带明确的置信标签：Nielsen问题发现法（每个细分群体5人）、Guest等人的主题饱和法（约12人），以及评估覆盖法。绝不会从小样本可用性测试中得出普及率结论。
```
insight_synthesizer.py
```
—— 按标签对编码后的观察结果进行聚类，统计不同参与者的数量，按跨参与者的重复频次排序，并将低于来源阈值的候选结果标记为轶事，绝不将其提升为洞察。

When to use

使用场景

Invoke this skill when:

You are planning a study and need the method to match the goal (generative vs evaluative vs validation).
You need a defensible sample size / saturation rationale with a stated confidence.
You have raw coded observations and need to synthesize insights without over-claiming.
You are setting up or auditing a research repository and need the insight-vs-observation discipline.

Do NOT use this skill to: generate personas / journey maps (use

product-team/ux-researcher-designer

), plan a discovery sprint or validate an opportunity (use

product-team/product-discovery

), design or analyze a live product A/B experiment (use

product-team/experiment-designer

), or do market sizing / surveys (use the

market-research

sibling).

在以下场景中调用该技能：

你正在规划一项研究，需要让方法匹配目标（生成式vs评估式vs验证式）。
你需要一个具有明确置信度的、可辩护的样本量/饱和理由。
你拥有原始编码观察结果，需要在不过度断言的前提下整合为洞察。
你正在搭建或审核研究存储库，需要区分洞察与观察的规范。

请勿使用该技能生成角色画像/旅程地图（请使用

product-team/ux-researcher-designer

）、规划发现冲刺或验证机会（请使用

product-team/product-discovery

）、设计或分析实时产品A/B实验（请使用

product-team/experiment-designer

），或进行市场规模测算/调研（请使用姊妹技能

market-research

）。

Workflow

工作流程

Frame the study — Fill
```
assets/research_plan_template.md
```
(research questions, method rationale, participant criteria, analysis plan, repository tagging scheme).

Pick the method — Run

study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {b2b-saas|consumer-app|enterprise|marketplace|hardware|platform}

. Honor the redirect if it routes to experiment-designer.

Size it — Run

saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N

. Record the confidence label and limits.

Synthesize — After fielding, code observations and run
```
insight_synthesizer.py --input observations.json --min-sources 3
```
. Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship.
File in the repository — Tag insights to the atomic schema at synthesis time, with their evidence and confidence.

确定研究框架 —— 填写
```
assets/research_plan_template.md
```
（研究问题、方法依据、参与者标准、分析计划、存储库标签方案）。

选择方法 —— 运行

study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {b2b-saas|consumer-app|enterprise|marketplace|hardware|platform}

。若重定向至experiment-designer，请遵循该指引。

确定规模 —— 运行

saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N

。记录置信标签及限制条件。

整合洞察 —— 完成实地研究后，对观察结果进行编码，运行
```
insight_synthesizer.py --input observations.json --min-sources 3
```
。将标记为ANECDOTE的聚类视为需要进一步探索的信号，而非可落地的结论。
存入存储库 —— 在整合洞察时，按照原子模式为洞察添加标签，并附上证据和置信度。

Scripts

脚本说明

Script	Purpose	Profiles
`scripts/study_designer.py`	(goal × stage) → method + plan skeleton	b2b-saas, consumer-app, enterprise, marketplace, hardware, platform
`scripts/saturation_planner.py`	Method-based sample guidance + confidence	n/a (method-driven)
`scripts/insight_synthesizer.py`	Cluster observations, flag anecdotes	n/a (evidence-driven)

All three: stdlib-only,

--help

--sample

--output {human,json}

脚本	用途	适用场景
`scripts/study_designer.py`	（目标 × 阶段）→ 方法 + 规划框架	b2b-saas, consumer-app, enterprise, marketplace, hardware, platform
`scripts/saturation_planner.py`	基于方法的样本量指导 + 置信度	无（由方法驱动）
`scripts/insight_synthesizer.py`	聚类观察结果，标记轶事	无（由证据驱动）

所有脚本：仅依赖标准库，支持

--help

、

--sample

、

--output {human,json}

参数。

Onboarding & customization

入门与自定义

Run the onboarding questionnaire once before you start — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior (e.g. the insight source-threshold).

bash

python3 scripts/onboard.py            # interactive (also: --defaults, --set key=value, --reset)
python3 scripts/onboard.py --show     # see the questions + current effective config

Answers are saved to

~/.config/research-ops/product-research.json

(global) or

./.research-ops/product-research.json

(

--scope project

) and are read automatically by

config_loader.py

. They set the default product profile, the insight source-threshold (how many independent participants make a finding an insight, not an anecdote), the default saturation method, and the high-stakes flag. CLI flags always override saved config;

RESEARCH_OPS_NO_CONFIG=1

ignores it.

The four questions: product profile · insight source-threshold · saturation method · high-stakes flag.

开始使用前请先运行一次入门问卷——它会记录你的默认设置，使该技能中的所有工具都预先配置完成。自定义是核心：问卷答案会实际改变工具行为（例如洞察来源阈值）。

bash

python3 scripts/onboard.py            # 交互式模式（也支持：--defaults, --set key=value, --reset）
python3 scripts/onboard.py --show     # 查看问题及当前生效配置

答案将保存至

~/.config/research-ops/product-research.json

（全局配置）或

./.research-ops/product-research.json

（

--scope project

项目级配置），并由

config_loader.py

自动读取。这些配置会设置默认产品场景、洞察来源阈值（需要多少独立参与者的重复反馈才能将结论视为洞察而非轶事）、默认饱和方法，以及高风险标记。CLI参数始终优先于保存的配置；设置

RESEARCH_OPS_NO_CONFIG=1

将忽略配置文件。

四个核心问题：产品场景 · 洞察来源阈值 · 饱和方法 · 高风险标记。

Optimize with autoresearch (opt-in)

可选：通过自动研究优化

This skill ships an isolated, opt-in bridge to

engineering/autoresearch-agent

. Only when you ask to "optimize the synthesis" / "run a loop" does an autoresearch experiment iteratively refine the coding/clustering of a fixed evidence set so more cross-participant patterns surface.

scripts/ar_evaluator.py

is the ground-truth evaluator; it prints

validated_insights: <int>

(higher is better). It optimizes the coding, never fabricates evidence.

bash

/ar:setup --domain custom --name insight-synthesis \
  --target observations.json \
  --eval "python3 ar_evaluator.py --target observations.json" \
  --metric validated_insights --direction higher
/ar:loop custom/insight-synthesis

Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits

observations.json

, never the evaluator.

该技能附带一个独立、可选的桥接工具，可连接至

engineering/autoresearch-agent

。只有当你要求“优化整合过程”/“运行循环”时，自动研究实验才会迭代优化固定证据集的编码/聚类，以挖掘更多跨参与者的模式。

scripts/ar_evaluator.py

是真值评估工具，它会输出

validated_insights: <int>

（数值越高越好）。它仅优化编码过程，绝不编造证据。

bash

/ar:setup --domain custom --name insight-synthesis \
  --target observations.json \
  --eval "python3 ar_evaluator.py --target observations.json" \
  --metric validated_insights --direction higher
/ar:loop custom/insight-synthesis

独立性：无强依赖——自动研究仅按需运行，循环仅修改

observations.json

，绝不修改评估工具。

References

参考资料

```
references/research_methods_canon.md
```
— Portigal Interviewing Users; Christensen/Ulwick JTBD; Rohrer's UX-research methods landscape (NN/g); Sauro & Lewis Quantifying the User Experience; Goodman/Kuniavsky.
```
references/sampling_and_saturation.md
```
— Nielsen "test with 5 users"; Guest, Bunce & Johnson saturation; Faulkner on more-than-5; Sauro usability sample size; Braun & Clarke thematic analysis.
```
references/repository_and_synthesis.md
```
— ResearchOps / atomic research (Tomer Sharon "Polaris"); insight-vs-observation discipline; repository governance; affinity mapping; democratization guardrails.

```
references/research_methods_canon.md
```
—— Portigal《访谈用户》；Christensen/Ulwick的JTBD理论；Rohrer的UX研究方法全景图（NN/g）；Sauro & Lewis《量化用户体验》；Goodman/Kuniavsky的相关研究。
```
references/sampling_and_saturation.md
```
—— Nielsen的“用5名用户做测试”；Guest, Bunce & Johnson的饱和理论；Faulkner关于样本量大于5的研究；Sauro的可用性测试样本量研究；Braun & Clarke的主题分析。
```
references/repository_and_synthesis.md
```
—— ResearchOps/原子研究（Tomer Sharon的“北极星”理论）；洞察与观察的区分规范；存储库管控；亲和图法；民主化防护措施。

Assumptions

假设前提

Method selection assumes you can name the goal honestly; if the goal is fuzzy, grill it first (the goal drives everything).
Saturation guidance is method-based, not a power calculation — usability tests find problems, not prevalence rates.
The synthesizer counts evidence you provide; coding quality is upstream of it. Garbage tags → garbage clusters.
The insight threshold (
```
--min-sources
```
) defaults to 3; raise it for high-stakes or heterogeneous populations.

方法选择假设你能明确表述真实目标；若目标模糊，需先厘清（目标驱动一切）。
饱和度指导基于方法，而非功效计算——可用性测试用于发现问题，而非测算普及率。
整合工具仅统计你提供的证据；编码质量是前置条件。标签质量差→聚类结果差。
洞察阈值（
```
--min-sources
```
）默认值为3；针对高风险或异质性群体可提高该值。

Anti-patterns

反模式

Mismatching method to goal. A usability test cannot discover unmet needs; an interview cannot measure task success.
Reporting usability problems as percentages. Small-n tests surface problems, not population rates.
Promoting an anecdote to an insight. One participant is a signal to probe, not a finding.
Framing interview questions as feature reactions. Probe the job-to-be-done and recent real behavior, not hypothetical opinions.
Synthesizing without a repository scheme. Tag at synthesis time, or insights rot unfindable.

方法与目标不匹配：可用性测试无法发现未被满足的需求；访谈无法衡量任务完成度。
将可用性问题按百分比报告：小样本测试仅用于发现问题，而非测算群体普及率。
将轶事提升为洞察：单一参与者的反馈是需要探索的信号，而非结论。
将访谈问题设计为功能反馈：应探索用户的待办任务和近期真实行为，而非假设性观点。
无存储库方案就进行整合：需在整合时添加标签，否则洞察会因无法检索而失效。

Distinct from

与其他技能的区别

Neighbor	Scope	Difference
`product-team/ux-researcher-designer`	Personas, journey maps, usability frameworks tied to design output	That produces artifacts; this is method + repository discipline
`product-team/product-discovery`	Opportunity validation, discovery-sprint planning	That plans discovery sprints; this designs and synthesizes the research
`product-team/experiment-designer`	Live product A/B hypothesis + sample size	That runs live experiments; this runs qualitative/evaluative research
`market-research` (sibling)	Market sizing, surveys, segmentation	That studies the market; this studies users

关联技能	范围	差异
`product-team/ux-researcher-designer`	角色画像、旅程地图、与设计输出绑定的可用性框架	该技能产出工件；本技能是方法+存储库规范
`product-team/product-discovery`	机会验证、发现冲刺规划	该技能规划发现冲刺；本技能设计并整合研究内容
`product-team/experiment-designer`	实时产品A/B假设+样本量	该技能运行实时实验；本技能开展定性/评估式研究
`market-research` （姊妹技能）	市场规模测算、调研、细分	该技能研究市场；本技能研究用户

Quick examples

快速示例

bash

python3 scripts/study_designer.py --sample
python3 scripts/saturation_planner.py --method thematic --segments 3
python3 scripts/insight_synthesizer.py --sample --min-sources 3

The synthesizer sample correctly promotes "import-confusion" (3 independent participants) to INSIGHT and flags "wants-slack" (1 participant) as an ANECDOTE.

bash

python3 scripts/study_designer.py --sample
python3 scripts/saturation_planner.py --method thematic --segments 3
python3 scripts/insight_synthesizer.py --sample --min-sources 3

整合工具的示例会正确将“导入困惑”（来自3名独立参与者）标记为INSIGHT，将“想要Slack集成”（来自1名参与者）标记为ANECDOTE。

Forcing-question library (Matt Pocock grill discipline)

追问问题库（Matt Pocock严谨性规范）

Walked one at a time by

/cs:grill-research-ops

or the orchestrator. Recommended answer + canon citation per question. Never bundled.

"Is this study generative (discover problems) or evaluative (test a solution)?" Recommended: name it first — the method follows from the goal. Canon: Rohrer, When to Use Which User-Experience Research Methods (NN/g).
"What's your sample size and saturation rationale — and at what confidence?" Recommended: method-based n (5/segment usability; ~12 for thematic saturation), state the confidence. Canon: Nielsen; Guest, Bunce & Johnson (2006); Faulkner (2003).
"How many independent participants support each insight — or is it a single-source anecdote?" Recommended: require recurrence across ≥3 sources before calling it an insight; flag singletons. Canon: atomic research / ResearchOps; Braun & Clarke thematic analysis.
"Are your interview / usability tasks framed as outcomes (jobs) or as feature reactions?" Recommended: frame around the job-to-be-done and recent real behavior, not hypothetical opinion. Canon: Christensen/Ulwick Jobs-to-be-Done; Portigal Interviewing Users.
"Where does this land in the repository, and how is it tagged for reuse?" Recommended: tag to the atomic schema at synthesis time, not later. Canon: Tomer Sharon, Polaris / ResearchOps repository practice.

Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke

study_designer.py

→

saturation_planner.py

→ (after fielding)

insight_synthesizer.py

由

/cs:grill-research-ops

或编排工具逐一提出。每个问题配有推荐答案及权威引用。请勿批量提问。

“这项研究是生成式（发现问题）还是评估式（测试解决方案）？” 推荐：先明确类型——方法由目标决定。引用：Rohrer，《何时使用何种用户体验研究方法》（NN/g）。
“你的样本量和饱和理由是什么——置信度是多少？” 推荐：基于方法确定样本量（可用性测试每个细分群体5人；主题饱和约12人），并说明置信度。引用：Nielsen；Guest, Bunce & Johnson（2006）；Faulkner（2003）。
“每个洞察有多少独立参与者支持——还是仅为单一来源的轶事？” 推荐：要求至少≥3个来源的重复反馈才能称为洞察；标记单一来源的内容。引用：原子研究/ResearchOps；Braun & Clarke的主题分析。
“你的访谈/可用性任务是围绕结果（待办任务）还是功能反馈设计的？” 推荐：围绕用户待办任务和近期真实行为设计，而非假设性观点。引用：Christensen/Ulwick的待办任务理论；Portigal《访谈用户》。
“这项研究将存入存储库的哪个位置，如何标记以便复用？” 推荐：在整合洞察时按原子模式添加标签，而非事后补充。引用：Tomer Sharon，《北极星》/ResearchOps存储库实践。

按深度优先顺序提问。在回答1-2个问题后再开启3-5的提问。所有问题回答完毕后，依次调用

study_designer.py

→

saturation_planner.py

→ （完成实地研究后）

insight_synthesizer.py

。