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产品能力规划

将PRD意图、路线图需求或产品讨论转化为可供实施的、暴露约束条件、不变项、接口及未决决策的能力规划方案。适用于需要从PRD到SRS的工程化路径而非模糊规划文本的场景。

产品能力规划 是什么?

该技能将产品意图转化为显式的工程约束,填补“构建什么”与“实施前提条件”之间的鸿沟。它要求用户提供产品意图(如PRD、讨论)、当前架构文档、已有的能力上下文以及交付约束(如认证、计费、合规)。工作流分为四步:重述能力、解析约束条件、定义面向实施的契约(包括非目标、参与者、状态转换、接口/数据影响等)、以及转化为具体的执行指令。最终输出格式化了的能力规划文档,可直接用于Claude Code、Codex、Cursor、OpenCode及ECC 2.0规划平台。其核心理念是让隐蔽的约束成为可复用的耐久工件,而非依赖工程师的记忆或零散的笔记。

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

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

产品能力规划 是做什么的?

此技能将产品意图转化为显式的工程约束。

当你面对的差距不是“该构建什么?”而是“在实施开始前,确切必须确认什么?”时使用它。

使用时机

  • 存在PRD、路线图项、讨论或创始人笔记,但实施约束仍为隐式
  • 某个功能跨越多个服务、仓库或团队,在编码前需要一个能力契约
  • 产品意图清晰,但架构、数据、生命周期或策略影响仍模糊
  • 资深工程师在评审中反复重申同样的隐藏假设
  • 你需要一个能在不同工作环境和会话中持续存在的可复用工件

规范工件

如果仓库已有持久性的产品上下文文件,如 PRODUCT.md、docs/product/ 或程序规范目录,则在此处更新。

如果尚不存在能力清单,请使用模板创建:

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

目标并非创建又一个规划栈。目标是让隐式的能力约束变得持久且可复用。

不可妥协的规则

  • 不要捏造产品事实。明确标记未解决的疑问。
  • 将用户可见的承诺与实现细节分开。
  • 指出哪些是固定策略、哪些是架构偏好、哪些仍处于开放状态。
  • 如果请求与现有仓库约束冲突,请明确说出,而非粉饰。
  • 优先选择一份可复用的能力工件,而非分散的临时笔记。

输入

仅读取所需内容:

  1. 产品意图
    • issue、讨论、PRD、路线图笔记、创始人消息
  2. 当前架构
    • 相关仓库文档、契约、模式、路由、现有工作流
  3. 现有能力上下文
    • PRODUCT.md、设计文档、RFC、迁移笔记、运营模式文档
  4. 交付约束
    • 认证、计费、合规、发布、向后兼容性、性能、评审策略

核心工作流

1. 重述能力

将需求压缩为一条精确的陈述:

  • 用户或操作者是谁
  • 上线后存在哪些新能力
  • 因此结果发生了何种变化

如果这一陈述薄弱,实施将会偏离方向。

2. 解析能力约束

提取实施前必须满足的约束:

  • 业务规则
  • 范围边界
  • 不变项
  • 信任边界
  • 数据所有权
  • 生命周期转换
  • 发布/迁移要求
  • 故障与恢复预期

这些往往是仅存于资深工程师记忆中的内容。

3. 定义面向实施的契约

生成一份SRS风格的能力规划,包含:

  • 能力摘要
  • 明确的非目标
  • 参与者和接触面
  • 必须的状态与转换
  • 接口/输入/输出
  • 数据模型影响
  • 安全性/计费/策略约束
  • 可观测性与操作者要求
  • 阻碍实施的未决问题

4. 转化为执行

最后给出确切的交接说明:

  • 可立即实施
  • 需先通过架构评审
  • 需先明确产品定义

如有帮助,可指向下一个ECC原生流程:

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

输出格式

按以下顺序返回结果:

CAPABILITY
- 一段话重述

CONSTRAINTS
- 固定规则、不变项和边界

IMPLEMENTATION CONTRACT
- 参与者
- 接触面
- 状态与转换
- 接口/数据影响

NON-GOALS
- 此流程明确不拥有的内容

OPEN QUESTIONS
- 阻碍项或仍需做出的产品决策

HANDOFF
- 下一步应做什么以及由哪个ECC流程接手

良好成果

  • 产品意图现在足够具体,可以在不于PR中间重新发现隐藏约束的情况下实施。
  • 工程评审拥有一份耐久工件,而非依赖记忆或Slack上下文。
  • 生成的规划可在Claude Code、Codex、Cursor、OpenCode和ECC 2.0规划面上复用。

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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