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Brand Discovery

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affaan-m
brand-discovery

Use when a brand needs to discover or articulate its identity through structured multi-session interviews. Covers purpose, positioning, audience, personality, voice, narrative, and founder-brand tension across 8 modules using laddering, 5 Whys, and projective techniques. Produces a resumable session with disk-persisted state and a master brandbook (90_SYNTHESIS.md).

Overview

Publisheraffaan-m
RepositoryECC
Skill namebrand-discovery
Stars
270.2K
Forks
40.4K
Bundled files
9
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Brand Discovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/affaan-m/ECC/tree/main/.agents/skills/brand-discovery
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/brand-discovery .claude/skills/brand-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brand Discovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brand Discovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brand Discovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

json
{
  "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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Brand Discovery AI skill do?

Use when a brand needs to discover or articulate its identity through structured multi-session interviews. Covers purpose, positioning, audience, personality, voice, narrative, and founder-brand tension across 8 modules using laddering, 5 Whys, and projective techniques. Produces a resumable session with disk-persisted state and a master brandbook (90_SYNTHESIS.md).

Why use Brand Discovery on TypingMind?

Because you install it once and use it with any model. Brand Discovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brand Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/brand-discovery. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Brand Discovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brand Discovery?

As many as you like. As long as a model supports skills, you can use Brand Discovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brand Discovery AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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