Customer Finder logo

Customer Finder

Organization
buildfastwithai
customer-finder

Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals. Use when Codex needs to analyze a product URL or idea, define an ideal customer profile, research public discussions and business pages, identify first-user prospects, rank lead fit and timing, prepare source-based outreach drafts, or create a shareable early-customer prospecting report without sending messages automatically.

Overview

Publisherbuildfastwithai
Repositorygen-ai-experiments
Skill namecustomer-finder
Stars
785
Forks
225
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by buildfastwithai on GitHub. Read the source before you install it.

Installation

Install the Customer Finder 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/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder
  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/buildfastwithai/gen-ai-experiments.git /tmp/gen-ai-experiments
mkdir -p .claude/skills
cp -r /tmp/gen-ai-experiments/skills/customer-finder-skill/customer-finder .claude/skills/customer-finder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Finder 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 Customer Finder 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 Customer Finder 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.

Customer Finder

Turn a startup URL or product description into a short, evidence-backed list of plausible first customers. Use public signals, preserve privacy, and distinguish a prospect from a confirmed buyer.

Read references/research-framework.md before researching or scoring prospects. Read references/report-artifact.md before creating the final report.

Workflow

1. Understand the product

  • Inspect the supplied URL, repository, landing-page copy, or product description.
  • Identify the product, outcome, buyer, user, price or buying motion, geography, and strongest use case.
  • Define one primary ICP, one adjacent ICP, pain triggers, positive signals, and disqualifiers.
  • Infer missing context when safe and label the inference. Ask one concise question only when ambiguity would materially change the search.

2. Build a public-signal search plan

Search current public sources for:

  • explicit tool or alternative requests
  • first-person descriptions of the target problem
  • manual workflows and repeated workaround complaints
  • migration, churn, or competitor-frustration signals
  • public company changes that create timing, such as hiring, launching, expanding, or adopting a relevant workflow

Use multiple query angles and source types. Prefer original pages over search snippets. Record the source URL, source type, publication date when visible, and the exact evidence supporting qualification.

3. Research safely

  • Use public, intentionally shared professional or business information only.
  • Do not bypass login walls, paywalls, access controls, rate limits, or robots restrictions.
  • Do not use data brokers, leaked datasets, private groups, personal email discovery, phone enrichment, or sensitive personal information.
  • Do not infer protected traits or target people using health, financial hardship, political belief, sexuality, religion, or other sensitive attributes.
  • Prefer companies, public professional profiles, public requests, and community posts relevant to the product.
  • Quote minimally and paraphrase by default. Link every material pain or timing signal.

4. Qualify and deduplicate

Score each prospect using the bundled framework:

  • pain strength
  • product fit
  • timing
  • public reachability
  • evidence quality

Remove duplicates and weak matches. A prospect without a cited pain, need, or timing signal is only a speculative fit and must not appear in the primary shortlist.

Never claim that a prospect is interested, has consented, or will buy. Label the output “potential customer based on public signals.”

5. Draft outreach, never send it

  • Recommend the most natural public or professional channel already associated with the source.
  • Write one short opener grounded only in the cited public context.
  • Avoid pretending to know the person, overstating familiarity, or mentioning unrelated personal details.
  • Do not send messages, submit forms, connect, follow, comment, or create CRM records unless the user separately requests and authorizes that action.

6. Produce the report

Lead with the most actionable evidence. Use this order:

  1. Verdict — whether the startup has reachable early-customer signals.
  2. ICP — buyer, job, trigger, and disqualifiers.
  3. Top prospect — strongest evidence-backed candidate and why now.
  4. Prospect shortlist — source, pain signal, fit score, stage, why now, channel, and opener.
  5. Repeated patterns — pains and triggers appearing across prospects.
  6. Seven-day outreach plan — a manual, low-volume validation sequence.
  7. Limits — missing evidence and what must be confirmed through real conversations.

Create a standalone HTML report unless the user explicitly requests chat-only output:

  1. Write structured JSON using references/report-artifact.md.
  2. Run scripts/generate_report.py <analysis.json> <report.html>.
  3. Save the report in the workspace outputs/ directory.
  4. Verify prospect cards, source links, scores, patterns, outreach plan, and limitations.
  5. Return a clickable absolute file link in the final response so it opens from Codex.

Modes

  • quick: Find and qualify up to five strong prospects.
  • standard: Find up to ten prospects across several public source types.
  • deep: Research up to twenty prospects and map repeated pain patterns.
  • design-partners: Prioritize users willing to test and give feedback over immediate buyers.
  • b2b: Prioritize companies, public business triggers, and relevant decision roles.
  • community: Prioritize public discussion and explicit request signals.

Use standard by default.

Quality bar

  • Link every prospect to at least one meaningful public signal.
  • Prefer ten strong matches over a long generic lead list.
  • Make uncertainty and stale evidence visible.
  • Personalize from the source, not from invented assumptions.
  • Keep outreach manual and respectful.
  • Treat the shortlist as a research hypothesis, not a customer database.

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 Customer Finder AI skill do?

Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals. Use when Codex needs to analyze a product URL or idea, define an ideal customer profile, research public discussions and business pages, identify first-user prospects, rank lead fit and timing, prepare source-based outreach drafts, or create a shareable early-customer prospecting report without sending messages automatically.

Why use Customer Finder on TypingMind?

Because you install it once and use it with any model. Customer Finder 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 Customer Finder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/customer-finder-skill/customer-finder. 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 Customer Finder?

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 Customer Finder?

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

Is the Customer Finder AI skill free?

Yes. It is published on GitHub by buildfastwithai 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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