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品牌語調 (Brand Voice)

從真實貼文、文章、發布說明、文件或網站文案中建立源於來源的寫作風格設定檔,然後在內容創作、外聯和社群媒體工作流程中重複使用該設定檔。當使用者希望保持語調一致性,避免使用通用 AI 寫作套路時使用。

品牌語調 (Brand Voice) 是什麼?

Brand Voice 技能旨在解決 AI 生成內容中常見的語調不一致和過於機械化的問題。它通過系統化的方式,從使用者提供的真實寫作素材(如 X 貼文、文章、郵件、產品文件等)中提取關鍵風格特徵,例如節奏、壓縮程度、標點使用習慣和論述方式,並將其結構化為一個可重複使用的「語音設定檔」(VOICE PROFILE)。這個設定檔可以作為其他下游技能(如 content-engine、crosspost)的輸入,確保所有輸出的內容都遵循統一的語調。技能內建了完整的收集、提取和輸出合約,並包含針對特定作者(如 Affaan / ECC)的預設設定和禁止使用的詞彙與套路,從而實現高效的語調複用和管理。

相容平台~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

品牌語調 (Brand Voice) 是做什麼的?

從真實來源素材建立持久的語調設定檔,然後在所有地方使用該設定檔,而不是每次都重新推導風格,或預設使用通用的 AI 文案。

何時啟用

  • 使用者希望以特定語調產出內容或進行外聯
  • 為 X、LinkedIn、電子郵件、發布貼文、討論串或產品更新撰寫內容
  • 在不同渠道上調整已知作者的語氣
  • 現有內容類別需要一個可重複使用的風格系統,而不是一次性模仿

來源優先順序

使用可用的最強真實來源集,優先順序如下:

  1. 近期原創的 X 貼文和討論串
  2. 文章、論文、備忘錄、發布說明或電子報
  3. 實際有效的外聯電子郵件或私訊
  4. 產品文件、更新日誌、README 框架和網站文案

不要使用通用平台的範例作為來源素材。

收集工作流程

  1. 如果可以,收集 5 到 20 個具代表性的樣本。
  2. 優先使用近期素材,除非使用者表示較舊的寫作更具代表性。
  3. 如果來源集明顯分開,請區分「公開發布語調」和「私人工作語調」。
  4. 如果可以即時存取 X,在草擬前使用 x-api 拉取近期原創貼文。
  5. 如果網站文案很重要,請包含目前的 ECC 登陸頁面和 repository/外掛框架文案。

需要提取的內容

  • 節奏和句子長度
  • 壓縮 vs. 解釋
  • 大小寫規範
  • 括號的使用
  • 提問的頻率和目的
  • 主張的尖銳程度
  • 數字、機制或證據出現的頻率
  • 轉折過渡的方式
  • 作者從來不做什麼

輸出合約

產出一個可重複使用的 VOICE PROFILE 區塊,下游技能可以直接使用。使用 references/voice-profile-schema.md 中的 schema。

保持設定檔結構化且足夠簡潔,以便在會話上下文中重複使用。重點不是文學評論,而是操作上的重複利用。

Affaan / ECC 預設值

如果使用者想要 Affaan / ECC 語調但即時來源較少,且沒有較新的來源素材覆蓋,請從此處開始:

  • 直接、精簡、具體
  • 具體細節、機制、證據和數字優於形容詞
  • 括號用於限定、縮小範圍或過度說明
  • 使用常規大小寫,除非有真正的理由打破規則
  • 提問少見,不應用於誘導
  • 語氣可以是尖銳、直率、懷疑或乾澀
  • 轉折過渡應感覺是得來的,而不是平滑帶過

嚴格禁止

刪除並改寫以下任何內容:

  • 假裝好奇的鉤子
  • 「不是 X,只是 Y」
  • 「沒有廢話」
  • 強制小寫
  • LinkedIn 思想領袖節奏
  • 誘導性問題
  • 「很興奮分享」
  • 通用的創始人歷程填充內容
  • 老套的括號

持久性規則

  • 在同一個會話中,對相關任務重複使用最新的已確認 VOICE PROFILE。
  • 如果使用者要求持久化工件,請將設定檔儲存在請求的工作區位置或記憶體表面。
  • 除非使用者明確要求,否則不要建立儲存個人語音指紋的 repository 追蹤檔案。

下游使用

在以下技能之前或之中使用此技能:

  • content-engine
  • crosspost
  • lead-intelligence
  • 文章或發布文案撰寫
  • 針對 X、LinkedIn 和電子郵件的冷或暖外聯

如果其他技能已經有部分語音擷取章節,則此技能是規範性的真相來源。

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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