Communitygithub.com

產品透鏡 - 先思考,再動手

使用此技能驗證產品假設、診斷產品方向,並在開始實作前確保建構正確的功能。

產品透鏡 - 先思考,再動手 是什麼?

Product Lens 是一個 AI 代理技能,旨在協助開發者與產品負責人在投入開發之前,對產品想法進行策略性驗證。它與傳統的規格撰寫不同,專注於診斷「為什麼要建構這個功能」以及「是否建構正確的東西」。此技能支援四種模式:產品診斷(類似 YC 辦公時間)、創辦人審查(評估專案的產品市場適配訊號)、使用者旅程審計(記錄實際使用者的入門摩擦點),以及功能優先級排序(使用 ICE 分數量化取捨)。輸出是可操作的 docs(如 PRODUCT-BRIEF.md),而不是冗長的論文。此技能可與 /browser-qa、/design-system audit、/canary-watch 以及 product-capability 配對,形成完整的開發前驗證與規格化流程。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/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

產品透鏡 — 先思考,再動手

此車道負責產品診斷,而非可實作的規格撰寫。

如果使用者需要持久的 PRD 轉 SRS 或能力契約工件,請轉交給 product-capability。

使用時機

  • 開始任何功能之前 — 驗證「為什麼」
  • 每週產品檢討 — 我們是否在建構正確的東西?
  • 在功能之間猶豫不決時
  • 產品上線前 — 對使用者旅程進行健全性檢查
  • 在工程規劃開始前,將模糊的想法轉換為產品簡報時

運作方式

模式 1:產品診斷

如同 YC 辦公時間但自動化。提出關鍵問題:

1. 這是為誰設計的?(具體人物,而非「開發者」)
2. 痛點是什麼?(量化:頻率、嚴重程度、目前的做法)
3. 為什麼是現在?(什麼改變讓這件事變得可能/必要?)
4. 10 星版本是什麼?(如果金錢/時間無限)
5. MVP 是什麼?(最小能證明論點的產品)
6. 反目標是什麼?(你明確不建構什麼?)
7. 如何知道它有效?(指標,而非感覺)

輸出:包含答案、風險與 Go/No-Go 建議的 PRODUCT-BRIEF.md。

如果結果是「是的,建構這個」,下一車道是 product-capability,而非更多創始人劇場。

模式 2:創辦人審查

以創辦人視角審查你當前的專案:

1. 閱讀 README、CLAUDE.md、package.json、最近的 commits
2. 推斷:這個專案試圖成為什麼?
3. 評分:產品市場適配訊號(0-10)
   - 使用成長軌跡
   - 留存指標(重複貢獻者、回訪使用者)
   - 營收訊號(定價頁面、計費程式碼、Stripe 整合)
   - 競爭護城河(什麼難以複製?)
4. 找出:能讓這件事 10 倍成長的一件事
5. 標記:你在建構但並不重要的事情

模式 3:使用者旅程審計

繪製實際使用者體驗:

1. 以新使用者身份克隆/安裝產品
2. 記錄每一個摩擦點(困惑的步驟、錯誤、缺少的文件)
3. 計時每個步驟
4. 與競爭對手的入門流程比較
5. 評分:時間到價值(使用者多久獲得第一次成功?)
6. 建議:前 3 個入門修復項目

模式 4:功能優先級排序

當你有 10 個想法卻只能選 2 個時:

1. 列出所有候選功能
2. 對每個功能評分:影響(1-5)× 信心(1-5)÷ 努力(1-5)
3. 按 ICE 分數排序
4. 套用限制:開發時間、團隊規模、依賴項
5. 輸出:附有理由的優先路線圖

輸出

所有模式都輸出可操作的 docs,而非論文。每個建議都有具體的下一步。

整合

搭配使用:

  • /browser-qa 驗證使用者旅程審計的發現
  • /design-system audit 進行視覺品質評估
  • /canary-watch 進行上線後監控
  • product-capability 當產品簡報需要轉換為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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

相關技能