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brand-discovery

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brand-discovery is a Claude Code agent skill that >-.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/brand-discovery

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Brand Discovery

Use this skill to conduct a structured, adaptive brand identity interview. The goal is a complete 90_SYNTHESIS.md — a master brandbook the organization can use to brief designers, writers, and external collaborators.

The interview runs across multiple sessions. Capture answers to disk as you go so that no elicited knowledge is lost when a conversation ends, and so a later session can resume from where the last one stopped.

When to Activate

  • A brand is being created, repositioned, or needs a written identity reference to brief collaborators.
  • Multiple sessions are expected — the conversation will span days or weeks.
  • Multiple founders or stakeholders need individual interviews before a reconciliation pass.
  • The user wants a structured, repeatable method rather than an ad-hoc chat.
  • Existing brand documentation is scattered, implicit, or founder-dependent and needs to be made explicit.

Session start protocol

On every activation, perform these steps before asking any interview question:

  1. Check for prior progress. Look for an existing set of module files and a state.json checkpoint in the project's brand-identity directory. If none exists, this is a fresh start — confirm the brand name, participants, and where to save the brand-identity files, then begin at the first module.
  2. Read the current module file if one is in progress, and scan its Raw section for previously captured answers.
  3. Report to the user in two or three sentences: which module we are in, its status, and what remains. Then ask: "Continue here, or switch module?"

Interview discipline

Apply these rules throughout every module:

  1. One question at a time. Never present a list of questions.
  2. After each answer: short paraphrase → one deepening probe OR close the thread if the topic is saturated. Never move on silently.
  3. Laddering: for every "what" answer, follow with "Why does that matter to you?" until a core value surfaces (typically two to four iterations).
  4. 5 Whys: for beliefs or positioning claims — push until the root reason, not the surface declaration, is on the table.
  5. Detect thin answers: if generic, jargon-heavy, or vague, ask for one concrete example, a client story, or a number.
  6. Projective techniques (use once per module to break a plateau):
    • "If the brand were a person, how would they walk into a room?"
    • Brand obituary: "If the organization closed in five years, what would customers miss? What would you regret not having said?"
    • Competitive contrast: "Name one peer you admire but would never want to become. What specifically makes them the wrong model?"
  7. Saturation signal: when two consecutive probes produce no new information, summarise and close the module.
  8. End of module: write a structured module file with two sections:
    • ## Raw — verbatim quotes and examples.
    • ## Synthesis — your interpretation, three candidate formulations, open questions, contradictions between participants. Then update the state.json checkpoint (see State protocol below).

Module sequence

FileLabelFrameworks used
10_purpose-why.mdPurpose / WhySinek Golden Circle, Lencioni
20_positioning.mdPositioningDunford "Obviously Awesome", Moore template
30_audience-niche.mdAudience & NicheBaker "Business of Expertise", ICP
40_personality-archetype.mdPersonality & ArchetypeMark & Pearson 12 archetypes, J. Aaker 5 dims
50_voice-tone.mdVoice & ToneBrand voice guidelines
60_narrative-story.mdNarrative / StoryNeumeier trueline, brand story arc
70_founder-tension.mdFounder Brands vs Studio BrandEnns "Win Without Pitching"
90_SYNTHESIS.mdMaster BrandbookKapferer prism, Aaker brand system

Complete modules in order. Honour a user request to jump modules and note the skip in state.json.

State write protocol

After each module reaches saturation or done status, write two files:

Module file at modules/{moduleFile} — full Raw and Synthesis content.

state.json — a lightweight checkpoint so a later session can resume. Update completedModules, inProgressModule, nextModule, lastUpdated. Schema:

{
  "session": "{brand_name}-brand-{YYYY-MM}",
  "outputPath": "{path_to_brand_identity_directory}",
  "completedModules": [],
  "inProgressModule": "10_purpose-why.md",
  "nextModule": "20_positioning.md",
  "participants": ["founder-A"],
  "lastUpdated": "{ISO-8601}"
}

After writing, confirm: "Module X saved. State updated. Next: Y."

Terminal module (90_SYNTHESIS.md): when writing the final synthesis, set inProgressModule to "90_SYNTHESIS.md" and nextModule to null in state.json. After writing, set completedModules to include "90_SYNTHESIS.md", then set inProgressModule to null — leaving it populated would cause a future resumption to treat the completed brandbook as still in progress. Confirm: "Brandbook complete. All modules saved."

Multi-founder mode

When more than one founder participates, write each founder's answers to founders/{participant}.md instead of the main module files. Validate the participant name before writing: accept only alphanumeric characters and hyphens (e.g. founder-a, anna); reject names containing path separators (/, \, ..) or special characters. Validate moduleFile against the enumerated module sequence (10 through 90 only). Validate outputPath to ensure it is an absolute path within the project directory — reject relative paths and paths that escape via .. segments. After all founders complete a module, run a reconciliation pass: summarise convergences and divergences in the module file, flag "productive tensions" for the group alignment workshop.

Anti-Patterns

  • Starting without reading state first. Every session must open by checking for existing module files and state.json. Skipping this loses all continuity from prior sessions.
  • Asking multiple questions at once. One question at a time is not optional — lists produce checklist answers, not real insight.
  • Moving to Synthesis before saturation. If the last two probes produced no new information, the module is done. If they did — it isn't.
  • Skipping multi-founder reconciliation. When multiple stakeholders are involved, individual interviews must complete before reconciliation. Discussing the brand collectively first introduces anchoring bias.
  • Treating this as a one-shot session. This skill is designed for multiple sessions. Rushing to 90_SYNTHESIS.md in one conversation produces shallow output.

Related Skills

  • competitive-platform-analysis — after brand-discovery establishes the positioning brief, use this to scope and categorise the competitor set.
  • brand-voice (ECC) — if the brand-discovery voice-and-tone module needs a separate, source-derived writing-style profile.

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

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