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產品能力規劃

將產品需求文件(PRD)意圖、路線圖需求或產品討論,轉化為可直接實施的能力計畫,揭露約束條件、不變量、介面與未決策。適用於需要從 PRD 到 SRS 的精確轉換,而非模糊的規劃敘述。

產品能力規劃 是什麼?

此技能專為將模糊的產品意圖轉化為明確的工程約束而設計,解決「要建什麼?」之後「實施前必須明確什麼?」的關鍵鴻溝。它適用於跨多個服務、儲存庫或團隊的功能,需要一份可重複使用的能力契約,以取代僅存在資深工程師記憶中的隱性假設。技能會產出一份標準化的成品,包含能力重述、約束條件、實施合約(包含參與者、界面、狀態轉換)、明確的非目標以及待解決的開放問題,確保產品意圖在編碼開始前已完全具體化。支援的平台包括 Claude Code、Codex、Cursor、OpenCode 與 ECC 2.0 規劃環境,且能與 `project-flow-ops`、`tdd-workflow` 等後續開發流程順暢銜接。

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

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說明文件

產品能力

此技能將產品意圖轉化為明確的工程約束。

當問題不是「我們該建什麼?」而是「實施開始前必須明確什麼?」的時候,請使用它。

使用時機

  • 存在 PRD、路線圖項目、討論或創始人筆記,但實施約束仍然隱含未明
  • 功能跨越多個服務、儲存庫或團隊,並且在編碼前需要一份能力契約
  • 產品意圖明確,但架構、資料、生命週期或政策影響仍然模糊不清
  • 資深工程師在審查時不斷重申相同的隱藏假設
  • 你需要一份可跨工具和會話重複使用的持久化成品

標準成品

如果儲存庫中已存在持久的產品背景檔案,例如 PRODUCT.md、docs/product/ 或規格目錄,請直接在那裡更新。

如果尚無能力清單,請根據以下範本建立一個:

  • docs/examples/product-capability-template.md

目標不是建立另一個規劃堆疊。目標是讓隱藏的能力約束變得持久且可重複使用。

不可妥協的規則

  • 不要捏造產品事實。明確標記未解決的問題。
  • 區分用戶可見的承諾與實現細節。
  • 指出哪些是固定政策、哪些是架構偏好、哪些仍待決定。
  • 如果請求與現有儲存庫約束衝突,請明確指出,而不是輕描淡寫帶過。
  • 偏好一份可重複使用的能力成品,而非零散的臨時筆記。

輸入

只讀取必要的內容:

  1. 產品意圖
    • Issue、討論、PRD、路線圖筆記、創始人訊息
  2. 當前架構
    • 相關儲存庫文件、契約、綱要、路由、現有工作流程
  3. 現有能力背景
    • PRODUCT.md、設計文件、RFC、遷移筆記、營運模型文件
  4. 交付約束
    • 認證、計費、合規、部署、向後相容、效能、審查政策

核心工作流程

1. 重述能力

將需求壓縮成一個精確的陳述:

  • 用戶或操作者是誰
  • 上線後存在哪些新能力
  • 因為它,有哪些結果會改變

如果這個陳述很薄弱,實施就會偏離方向。

2. 解析能力約束

提取實施前必須成立的約束條件:

  • 業務規則
  • 範圍邊界
  • 不變量
  • 信任邊界
  • 資料歸屬
  • 生命週期轉換
  • 部署/遷移需求
  • 故障與復原期望

這些通常是只存在於資深工程師記憶中的東西。

3. 定義面向實施的合約

產出一份 SRS 風格的能力計畫,包含:

  • 能力摘要
  • 明確的非目標
  • 參與者與表面
  • 必需的狀態與轉換
  • 介面/輸入/輸出
  • 資料模型影響
  • 安全/計費/政策約束
  • 可觀測性與操作者需求
  • 阻礙實施的開放問題

4. 轉化為執行

以精確的交接事項作結:

  • 可直接實施
  • 需先進行架構審查
  • 需先釐清產品問題

如有幫助,可指向下一個 ECC 原生流程:

  • project-flow-ops
  • workspace-surface-audit
  • api-connector-builder
  • dashboard-builder
  • tdd-workflow
  • verification-loop

輸出格式

請按以下順序回傳結果:

能力
- 一段式重述

約束條件
- 固定規則、不變量與邊界

實施合約
- 參與者
- 表面
- 狀態與轉換
- 介面/資料影響

非目標
- 本流程明確不負責的事項

開放問題
- 仍須解決的阻礙或產品決策

交接事項
- 下一步應採取的行動以及應由哪個 ECC 流程接手

良好成果

  • 產品意圖現在已足夠具體,可在不於 PR 中途重新發現隱藏約束的情況下實施。
  • 工程審查擁有一份持久的成品,而非依賴記憶或 Slack 背景資訊。
  • 生成的計畫可在 Claude Code、Codex、Cursor、OpenCode 與 ECC 2.0 規劃環境中重複使用。

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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