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Agent Skill · 研究检索

product-taste-intuition(产品味道直觉)

product-taste-intuition 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

product-taste-intuition 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 46,597。

总安装 774周安装 31GitHub Stars 3下载 250核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:product-taste-intuition(产品味道直觉)
来源仓库:https://github.com/oldwinter/skills
仓库路径:skills/product-taste-intuition
安装命令:
npx skills add https://github.com/oldwinter/skills --skill product-taste-intuition
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/oldwinter/skills --skill product-taste-intuition

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,支持关键词和任务场景定位。适合在 Codex、Claude 等宿主中快速获取候选结果。可结合来源仓库 README 核验具体用法和权限范围。安装前建议确认是否会触发联网或文件读写操作。product-taste-intuition 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/product-taste-intuition

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/oldwinter/skills --skill product-taste-intuiti…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

product-taste-intuition 怎么安装?

在终端执行 npx skills add https://github.com/oldwinter/skills --skill product-taste-intuition。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

product-taste-intuition 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

product-taste-intuition 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

product-taste-intuition 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

product-taste-intuition 用的人多吗?

公开数据是累计安装 774 次,近一周安装 31 次,下载 250 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

product-taste-intuition 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
product-taste-intuition

产品味道直觉 · 本页

7743只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Product Taste & Intuition

Scope

Covers

  • Developing product taste (what “good” looks like) through deliberate exposure, observation, and critique
  • Using intuition as a hypothesis generator (turning “gut feel” into testable hypotheses)
  • Building a repeatable practice loop (exposure hours → analysis → validation → updated taste rules)

When to use

  • “Help me improve my product taste / product sense.”
  • “Calibrate what ‘good onboarding’ looks like for our product category.”
  • “Turn my intuition about this flow into testable hypotheses.”
  • “Create a structured way to study great products and extract patterns.”

When NOT to use

  • You need to decide *what to build* (use problem-definition, prioritizing-roadmap, or defining-product-vision).
  • You need user evidence first (use conducting-user-interviews or usability-testing).
  • You want aesthetic critique only (this is product experience: value, UX, clarity, trust, speed—not just visuals).
  • You can’t name any target user, use case, or the “taste domain” you want to improve (we’ll narrow first).

Inputs

Minimum required

  • Taste domain to improve (pick 1): onboarding, activation, navigation/IA, editor/workflow, pricing/packaging UX, notifications, retention loops, trust/safety, performance/latency feel, copy/voice
  • Target user + top job-to-be-done for that domain
  • 3–10 benchmark products/experiences to study (or “unknown—please propose”)
  • Time box (e.g., 60–120 min sprint; or a 2–4 week practice plan)
  • Constraints (platform, geography, accessibility, compliance, brand voice, etc.)

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md.
  • If inputs remain missing, proceed with explicit assumptions and provide 2 scope options (narrow vs broad).

Outputs (deliverables)

Produce a Taste Calibration Pack (in-chat Markdown; or as files if requested):

  1. Taste Calibration Brief (domain, target user/job, what “good” means, constraints)
  2. Benchmark Set (5–10 products) + “why these” + what to study
  3. Product Study Notes (1 page per benchmark) using a consistent critique template
  4. Taste Rules + Anti-Patterns (do/don’t rules derived from evidence)
  5. Intuition → Hypothesis Log (testable hypotheses + predicted signals)
  6. Validation Plan (qual + quant checks; smallest viable tests)
  7. Practice Plan (2–4 weeks: exposure hours + weekly synthesis cadence)
  8. Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Intake + pick the taste domain (narrow the problem)

  • Inputs: User context; references/INTAKE.md.
  • Actions: Choose 1 taste domain and 1 “moment” (e.g., first-run onboarding). Define target user + job + constraints. Set time box.
  • Outputs: Taste Calibration Brief (draft).
  • Checks: A stakeholder can answer: “What specific experience are we calibrating taste for?”

2) Define “good taste” as decision criteria (not vibes)

  • Inputs: Domain + user/job.
  • Actions: Draft 6–10 criteria (e.g., clarity, time-to-value, trust, agency, error recovery, perceived speed, cognitive load). Add explicit tradeoffs (what you’ll sacrifice).
  • Outputs: Criteria list + tradeoffs section in the brief.
  • Checks: Criteria are observable in-product (you can point to UI/behavior), not generic adjectives.

3) Build the benchmark set (exposure hours, curated)

  • Inputs: Known benchmarks (or none).
  • Actions: Select 5–10 exemplars (direct, adjacent, and at least 1 “gold standard”). For each: what you’re studying and why it’s relevant.
  • Outputs: Benchmark Set table.
  • Checks: Set includes at least 2 “outside the category” references to avoid local maxima.

4) Study like a voracious user (structured observation)

  • Inputs: Benchmarks; critique template.
  • Actions: Use each product as the target user. Capture micro-moments: friction, delight, confusion, trust breaks. Record “what happened” before “why it’s good/bad”.
  • Outputs: Product Study Notes (draft).
  • Checks: Each benchmark note includes at least 3 concrete moments with screenshots/quotes if available (or precise descriptions).

5) Synthesize: turn observations into taste rules + anti-patterns

  • Inputs: Study notes across benchmarks.
  • Actions: Cluster patterns. Convert into rules: DO/DO NOT, plus rationale and where it applies. Add anti-patterns that create “AI slop” (generic, incoherent, misaligned experiences).
  • Outputs: Taste Rules + Anti-Patterns.
  • Checks: Each rule is backed by ≥ 2 observations from different benchmarks (or explicitly marked “hypothesis”).

6) Intuition as hypothesis generator (make it testable)

  • Inputs: Rules + your gut reactions.
  • Actions: Write intuition statements (“It feels off because…”) and convert into testable hypotheses with predicted signals and counter-signals.
  • Outputs: Intuition → Hypothesis Log.
  • Checks: Each hypothesis has a clear falsification condition (“If X doesn’t change after Y, we were wrong.”).

7) Validate with smallest viable checks (qual + quant)

  • Inputs: Hypothesis log; available data/research access.
  • Actions: Choose the lightest validation per hypothesis: usability task, intercept prompt, session replay review, funnel slice, A/B smoke test, copy test, etc. Define success metrics and sample.
  • Outputs: Validation Plan with owners/cadence if known.
  • Checks: Validation steps are feasible within the stated time box and don’t require sensitive data.

8) Create a practice loop + quality gate + finalize

  • Inputs: Draft pack.
  • Actions: Build a 2–4 week practice plan (exposure hours schedule + weekly synthesis). Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks/Open questions/Next steps.
  • Outputs: Final Taste Calibration Pack.
  • Checks: A reader can follow the practice plan without additional context; assumptions are explicit.

Quality gate (required)

Examples

Example 1 (Onboarding): “Calibrate our onboarding taste vs best-in-class. Target users are first-time PMs. Time box: 90 minutes. Output a Taste Calibration Pack.” Expected: benchmark set, critique notes, taste rules, hypotheses, and a lightweight validation plan.

Example 2 (B2B workflow UX): “My gut says our ‘create project’ flow feels slow and confusing. Turn that into testable hypotheses and a validation plan.” Expected: intuition→hypothesis log with falsification conditions and smallest viable checks.

Boundary example: “Tell me what good taste is in general.” Response: require a specific domain + target user/job; otherwise produce a menu of domain options and propose a narrow starting point.

平台分布

Codex

37.22%
按下载量换算93

Claude

28.96%
按下载量换算72

Cursor

18.62%
按下载量换算47

Gemini CLI

10.66%
按下载量换算27

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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