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产品透镜 — 先思考再构建

此技能用于在构建功能前验证产品的“为什么”,进行产品诊断、创始人审查、用户旅程审计和功能优先级排序。

产品透镜 — 先思考再构建 是什么?

Product Lens(产品透镜)技能旨在帮助开发者和产品团队在实施任何功能之前验证其根本动机和可行性。它提供四种模式:产品诊断(类似 YC 办公时间自动化,提出七个关键问题并输出 PRODUCT-BRIEF.md)、创始人审查(通过读取 README、CLAUDE.md 等文件推断项目目标并评分产品市场契合度)、用户旅程审计(模拟新用户安装并记录每次摩擦点,计算价值转化时间)、以及功能优先级排序(通过 ICE 评分和约束条件输出优先级路线图)。该技能适合在开始新功能、周度产品回顾、功能选择决策、上线前检查以及将模糊想法转化为产品简报时使用。输出均为可操作的文档,每条建议都附带具体的下一步行动。可与 /browser-qa、/design-system audit、/canary-watch 和 product-capability 技能配合使用。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/product-lens

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

产品透镜 — 先思考再构建 是做什么的?

此职责掌管产品诊断,而非实现就绪的规范编写。

如果用户需要持久的 PRD 到 SRS 或能力契约工件,请将任务转交至 product-capability。

何时使用

  • 开始任何功能之前 — 验证“为什么”
  • 周度产品回顾 — 我们是否在构建正确的东西?
  • 在两个功能之间犹豫不决时
  • 上线之前 — 对用户旅程进行严格检查
  • 将模糊的想法转化为产品简报后,再开始工程规划

工作原理

模式 1:产品诊断

类似于 YC 办公时间的自动化版本。提出棘手的难题:

1. 这是为谁做的?(具体的人,不泛指“开发者”)
2. 痛点是什么?(量化:频率、严重程度、他们现在怎么做?)
3. 为什么是现在?(什么变化使这成为可能/必要?)
4. 10 星版本是什么?(如果无限资金/时间)
5. MVP 是什么?(能证明假设的最小化产品)
6. 反目标是什么?(你明确不构建什么?)
7. 如何知道它有效?(依据指标,而非感觉)

输出:一份 PRODUCT-BRIEF.md,包含答案、风险以及“继续/停止”建议。

如果结果是“是的,构建这个”,下一个职责是 product-capability,而不是更多的创始人表演。

模式 2:创始人审查

以创始人视角审查当前项目:

1. 阅读 README、CLAUDE.md、package.json、最近的提交
2. 推断:这个项目试图成为什么?
3. 评分:产品市场契合度信号(0-10)
   - 使用增长轨迹
   - 留存指标(重复贡献者、回访用户)
   - 收入信号(定价页面、计费代码、Stripe 集成)
   - 竞争护城河(什么难以复制?)
4. 识别:一件事能带来 10 倍增长
5. 标记:你正在构建但无关紧要的东西

模式 3:用户旅程审计

映射实际用户体验:

1. 以新用户身份克隆/安装产品
2. 记录每个摩擦点(令人困惑的步骤、错误、缺失的文档)
3. 计时每个步骤
4. 对比竞争对手的用户引导流程
5. 评分:价值转化时间(用户多久才能获得第一次成功体验?)
6. 推荐:用户引导流程的前三大修复点

模式 4:功能优先级排序

当你有 10 个想法但只能选 2 个时:

1. 列出所有候选功能
2. 对每个功能评分:影响力(1-5)× 信心(1-5)÷ 工作量(1-5)
3. 按 ICE 分数排序
4. 应用约束条件:资金余额、团队规模、依赖关系
5. 输出:带有理由的优先级路线图

输出

所有模式都输出可操作的文档,而非长篇大论。每条建议都附带具体的下一步行动。

集成

与以下技能配合使用:

  • /browser-qa 验证用户旅程审计发现
  • /design-system audit 进行视觉打磨评估
  • /canary-watch 进行发布后监控
  • product-capability 当产品简报需要转变为实现就绪的能力计划时

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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