Communitygithub.com

brand-voice

Build a source-derived writing style profile from real posts, essays, launch notes, docs, or site copy, then reuse that profile across content, outreach, and social workflows. Use when the user wants voice consistency without generic AI writing tropes.

Was ist brand-voice?

brand-voice is a Claude Code agent skill that build a source-derived writing style profile from real posts, essays, launch notes, docs, or site copy, then reuse that profile across content, outreach, and social workflows. Use when the user wants voice consistency without generic AI writing tropes.

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

In Ihrer bevorzugten KI fragen

Öffnet einen neuen Chat, in dem dieser Agent-Skill bereits geladen ist.

Dokumentation

Brand Voice

Build a durable voice profile from real source material, then use that profile everywhere instead of re-deriving style from scratch or defaulting to generic AI copy.

When to Activate

  • the user wants content or outreach in a specific voice
  • writing for X, LinkedIn, email, launch posts, threads, or product updates
  • adapting a known author's tone across channels
  • the existing content lane needs a reusable style system instead of one-off mimicry

Source Priority

Use the strongest real source set available, in this order:

  1. recent original X posts and threads
  2. articles, essays, memos, launch notes, or newsletters
  3. real outbound emails or DMs that worked
  4. product docs, changelogs, README framing, and site copy

Do not use generic platform exemplars as source material.

Collection Workflow

  1. Gather 5 to 20 representative samples when available.
  2. Prefer recent material over old material unless the user says the older writing is more canonical.
  3. Separate "public launch voice" from "private working voice" if the source set clearly splits.
  4. If live X access is available, use x-api to pull recent original posts before drafting.
  5. If site copy matters, include the current ECC landing page and repo/plugin framing.

What to Extract

  • rhythm and sentence length
  • compression vs explanation
  • capitalization norms
  • parenthetical use
  • question frequency and purpose
  • how sharply claims are made
  • how often numbers, mechanisms, or receipts show up
  • how transitions work
  • what the author never does

Output Contract

Produce a reusable VOICE PROFILE block that downstream skills can consume directly. Use the schema in references/voice-profile-schema.md.

Keep the profile structured and short enough to reuse in session context. The point is not literary criticism. The point is operational reuse.

Affaan / ECC Defaults

If the user wants Affaan / ECC voice and live sources are thin, start here unless newer source material overrides it:

  • direct, compressed, concrete
  • specifics, mechanisms, receipts, and numbers beat adjectives
  • parentheticals are for qualification, narrowing, or over-clarification
  • capitalization is conventional unless there is a real reason to break it
  • questions are rare and should not be used as bait
  • tone can be sharp, blunt, skeptical, or dry
  • transitions should feel earned, not smoothed over

Hard Bans

Delete and rewrite any of these:

  • fake curiosity hooks
  • "not X, just Y"
  • "no fluff"
  • forced lowercase
  • LinkedIn thought-leader cadence
  • bait questions
  • "Excited to share"
  • generic founder-journey filler
  • corny parentheticals

Persistence Rules

  • Reuse the latest confirmed VOICE PROFILE across related tasks in the same session.
  • If the user asks for a durable artifact, save the profile in the requested workspace location or memory surface.
  • Do not create repo-tracked files that store personal voice fingerprints unless the user explicitly asks for that.

Downstream Use

Use this skill before or inside:

  • content-engine
  • crosspost
  • lead-intelligence
  • article or launch writing
  • cold or warm outbound across X, LinkedIn, and email

If another skill already has a partial voice capture section, this skill is the canonical source of truth.

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/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.

api-connector-builder

Build a new API connector or provider by matching the target repo

Verwandte Skills