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品牌语调

从真实文章、笔记、启动文档或网站文案中构建可复用的写作风格档案,并在内容、推广和社交工作流中统一使用,避免AI写作的通用腔调。

品牌语调 是什么?

品牌语调技能旨在帮助用户从真实、可靠的原始素材中提取并构建一个持久、结构化的写作风格档案(VOICE PROFILE),从而在各类内容创作、推广和社交互动中实现一致的语调。它特别适用于需要特定风格的文章、X平台帖子、领英动态、电子邮件、启动公告等场景。用户只需提供5到20个代表性样本,技能会引导系统按照“X最新原创帖、文章笔记、已有效果的邮件、产品文档”等优先级顺序采集素材,并从中提取节奏、句子长度、大小写规范、修辞偏好等25+项风格特征。最终生成一个可被其他下游技能(如content-engine、crosspost、lead-intelligence)直接引用的标准化档案块。该技能还内置了Affaan/ECC默认风格以及“硬性禁止”规则(如虚假好奇心钩子、套话、标题党问题等),并强调档案在会话内的持久复用,但不主动创建git跟踪文件,除非用户明确要求。通过这种方式,品牌语调将一次性风格模仿升级为可持续、可复用的写作系统,大幅提升内容一致性和效率。

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

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

品牌语调 是做什么的?

从真实源素材构建一个持久的语调档案,然后在各处使用该档案,而不是每次都重新推导风格或退回到通用的AI文案。

何时激活

  • 用户希望内容或推广采用特定语调
  • 为X、LinkedIn、电子邮件、启动帖、讨论串或产品更新撰写内容
  • 将已知作者的语调跨渠道适配
  • 现有内容通道需要一套可复用的风格系统,而非一次性模仿

源优先级

使用可用的最强真实源集合,顺序如下:

  1. 近期原创的X帖子和讨论串
  2. 文章、随笔、备忘录、启动笔记或通讯稿
  3. 实际发送且效果良好的外联邮件或私信
  4. 产品文档、变更日志、README框架和网站文案

不要使用通用平台范例作为源素材。

采集工作流

  1. 在可用时收集5到20个代表性样本。
  2. 优先选择近期素材而非旧素材,除非用户说明旧的写作更具典范性。
  3. 如果源集合明显区分“公开启动语调”与“私下工作语调”,请分开处理。
  4. 如果具有实时X访问权限,请在草稿前使用x-api拉取近期原创帖子。
  5. 如果网站文案重要,请包含当前的ECC着陆页以及仓库/插件框架说明。

提取内容

  • 节奏和句子长度
  • 压缩 vs. 解释
  • 大小写规范
  • 括号使用
  • 问题频率和目的
  • 论点表达的锐利程度
  • 数字、机制或实证出现的频率
  • 过渡方式
  • 作者从不做的事情

输出契约

生成一个可复用的VOICE PROFILE块,下游技能可以直接使用。遵循 references/voice-profile-schema.md 中的模式。

保持档案结构化且足够简短,以便在会话上下文中复用。重点不是文学评论,而是操作上的复用。

Affaan / ECC 默认

如果用户想要Affaan/ECC语调且实时源素材不足,请从这里开始,除非有更新的源素材覆盖它:

  • 直接、压缩、具体
  • 具体细节、机制、实证和数字优于形容词
  • 括号用于限定、缩小或过度澄清
  • 大写规范遵循常规,除非有真实理由打破它
  • 问题很少出现,不应作为诱饵
  • 语气可以尖锐、直白、怀疑或干练
  • 过渡应感觉自然,而非平滑过度

硬性禁止

删除并重写以下任何内容:

  • 虚假好奇心钩子
  • “不是X,只是Y”
  • “无废话”
  • 强制的小写
  • 领英思想领袖式节奏
  • 诱饵问题
  • “兴奋地分享”
  • 通用的创始人成长故事填充
  • 做作的括号

持久性规则

  • 在同一会话的相关任务中复用最新确认的VOICE PROFILE。
  • 如果用户要求持久化制品,将档案保存到指定的工作空间位置或记忆表面。
  • 不要创建仓库跟踪文件来存储个人语调指纹,除非用户明确要求。

下游使用

在此技能之前或内部使用:

  • content-engine
  • crosspost
  • lead-intelligence
  • 文章或启动文案创作
  • 在X、LinkedIn和电子邮件上的冷/暖外联

如果其他技能已有部分语调捕获部分,此技能是规范的事实来源。

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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