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product-lens

使用此技能驗證產品方向,確保在開始建構前先釐清「為什麼」。

product-lens 是什麼?

Product Lens 是一個專注於產品診斷而非實作規格撰寫的 AI Agent 技能。它能在開發者啟動任何新功能之前,透過結構化提問與自動化分析來驗證「為什麼要建構這個產品」。此技能支援多種模式:產品診斷(像 YC 辦公時間般提出關鍵問題)、創辦人審查(分析專案以評估產品市場契合度)、使用者旅程審計(實際模擬新用戶體驗並找出摩擦點)以及功能優先級排序(使用 ICE 分數排序候選功能)。產出結果為可直接行動的文件(如 PRODUCT-BRIEF.md),並附帶明確的下一步建議。Product Lens 可與 /browser-qa、design-system audit、canary-watch 及 product-capability 等技能整合,形成完整的產品開發流程。它主要適用於 Claude Code 等 AI 編碼代理平台,幫助團隊在投入資源前確保建構正確的產品。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

Product Lens — Think Before You Build

此技能負責產品診斷,而非實作規格的撰寫。

如果使用者需要持久的 PRD 到 SRS 或能力合約文件,請移交給 product-capability。

何時使用

  • 開始任何功能之前 — 驗證「為什麼」
  • 每週產品回顧 — 我們是否在建構正確的東西?
  • 在功能之間猶豫不決時
  • 上線前 — 對使用者旅程進行健康檢查
  • 在工程規劃開始前,將模糊想法轉換成產品簡報時

運作方式

模式 1:產品診斷

就像 YC 辦公時間但自動化。提出困難的問題:

1. 這是為誰設計的?(具體人物,不是「開發者」)
2. 痛點是什麼?(量化:發生頻率、嚴重程度、他們目前如何解決?)
3. 為什麼是現在?(什麼改變了使這變得可能/必要?)
4. 10 星版本是什麼?(如果資金/時間無上限)
5. MVP 是什麼?(能夠證明假設的最小事物)
6. 反目標是什麼?(你明確不建構什麼?)
7. 你如何知道它有效?(指標,不是感覺)

輸出:一個包含答案、風險以及執行/不執行建議的 PRODUCT-BRIEF.md。

如果結果是「是的,建構這個」,下一階段是 product-capability,而不是更多的創辦人表演。

模式 2:創辦人審查

以創辦人的視角審查你目前的專案:

1. 閱讀 README、CLAUDE.md、package.json、最近的提交
2. 推斷:這試圖成為什麼?
3. 評分:產品市場契合度訊號(0-10)
   - 使用成長軌跡
   - 留存指標(重複貢獻者、回訪使用者)
   - 收入訊號(定價頁面、計費程式碼、Stripe 整合)
   - 競爭護城河(什麼難以複製?)
4. 識別:能夠讓這個專案提升 10 倍的一件事
5. 標記:你正在建構但不重要的事情

模式 3:使用者旅程審計

繪製實際的使用者體驗:

1. 以新使用者身分複製/安裝產品
2. 記錄每一個摩擦點(令人困惑的步驟、錯誤、缺少的文件)
3. 為每個步驟計時
4. 與競爭對手的入門體驗比較
5. 評分:達到價值的時間(使用者多快獲得第一個成功體驗?)
6. 建議:入門流程的前三大修正

模式 4:功能優先級排序

當你有 10 個點子但需要選出 2 個:

1. 列出所有候選功能
2. 為每個功能評分:影響(1-5) × 信心(1-5) ÷ 努力(1-5)
3. 按 ICE 分數排序
4. 套用限制:資金跑道、團隊規模、依賴關係
5. 輸出:附有理由的優先級路線圖

輸出

所有模式輸出可立即行動的文件,而不是論文。每個建議都包含具體的下一步行動。

整合

搭配使用:

  • /browser-qa 以驗證使用者旅程審計的發現
  • /design-system audit 進行視覺打磨評估
  • /canary-watch 進行上線後監控
  • product-capability 當產品簡報需要變成可實作的能力計畫時

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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