Lead Prospector logo

Lead Prospector

Organization
Amazon-Quick
lead-prospector

Researches and identifies ideal prospects for small business sales. Builds targeted lead lists with company intel, decision-maker contacts, and personalized talking points. Use when user says 'find me leads', 'build a prospect list', 'who should I sell to', 'research companies in [industry]', 'find decision makers', 'identify potential customers', or 'prospect list for [product/service]'.

Overview

PublisherAmazon-Quick
RepositoryAmazon-Quick-official-catalog
Skill namelead-prospector
Stars
50
Forks
4
Bundled files
1
LicenseMIT-0
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.

  • 1 bundled files

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

  • Open source

    Published by Amazon-Quick on GitHub. Read the source before you install it.

Installation

Install the Lead Prospector 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/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/sales/lead-prospector
  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/Amazon-Quick/Amazon-Quick-official-catalog.git /tmp/Amazon-Quick-official-catalog
mkdir -p .claude/skills
cp -r /tmp/Amazon-Quick-official-catalog/skills/sales/lead-prospector .claude/skills/lead-prospector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lead Prospector 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 Lead Prospector 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 Lead Prospector 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.

Overview

Researches and identifies high-quality prospects matching a small business's ideal customer profile (ICP). Conducts web research, identifies decision makers, gathers personalization intel, deduplicates against existing contacts, and delivers actionable lead lists ready for outreach.

Workflow

<Definition - ICP Components>

  • Industry/vertical: What sectors do their best customers come from?
  • Company size: Revenue range or employee count
  • Geography: Local, regional, national, or specific markets
  • Pain points: What problems does the user's product/service solve?
  • Decision maker title: Who signs the check? (CEO, VP Marketing, Ops Manager, etc.)
  • Disqualifiers: Who should NOT be targeted? </Definition - ICP Components>

<Definition - Research Methods> Priority order for sourcing prospect companies:

  1. Industry directories: Search "[industry] companies in [geography]"
  2. Review sites: G2, Capterra, Clutch for B2B; Yelp, Google Maps for local
  3. Association member lists: Trade associations publish directories
  4. Job postings: Companies hiring for relevant roles have active need
  5. Funding announcements: Recently funded companies have budget </Definition - Research Methods>

<Definition - Personalization Sources> For each prospect, find 2-3 hooks from:

  • Company news: Recent press releases, product launches, expansions
  • Personal content: Blog posts, LinkedIn articles, podcast appearances
  • Pain signals: Job postings indicating gaps, negative reviews mentioning problems you solve
  • Trigger events: Funding, leadership changes, expansion announcements </Definition - Personalization Sources>

<Definition - Delivery Cascade> Auto-select output format (do not ask user):

  1. Spreadsheet connected (Google Sheets, Excel via SharePoint/OneDrive): push to "Prospect Pipeline" sheet
  2. 10+ prospects, no spreadsheet: generate local Excel via run_python + open_in_session_tab
  3. Fewer than 10 prospects: display as formatted table in chat </Definition - Delivery Cascade>

<Definition - Skill-Ready Outputs> Two formatted blocks always included:

  1. Prospects Ready for Outreach table (for outreach-sequence-builder): Company, Contact, Title, Top Hook, Warm/Cold
  2. New Prospects Added table (for deal-pipeline-manager): Company, Contact, Estimated Value, Source, Date Added </Definition - Skill-Ready Outputs>

<Workflow - Prospect Research description="Full prospecting workflow: ICP, sourcing, decision makers, personalization, delivery." tools=[web_search, url_fetch, run_python, file_write, open_in_session_tab] triggers=["find me leads", "build a prospect list", "who should I sell to", "research companies in", "find decision makers", "identify potential customers", "prospect list for"]

  1. [Decide] Is ICP clear from user prompt (industry, geography, and at least one of: company size, pain points, decision maker title)?

    • Yes: proceed to step 2.
    • Partially (have industry but missing geography or vice versa): ask only what is missing (max 2 questions).
    • No (vague request): ask "What do you sell, and who are your 3 best current customers?" to reverse-engineer ICP. Validate: At minimum, industry and geography are known. Decision maker title is identified or inferable. If fails: Cannot proceed without at least industry and geography. Re-ask.
  2. [Ask user] Present confirmed ICP summary. "Here's what I'll search for: [ICP summary]. Target: {{num_prospects}} prospects. Look right?" Validate: User confirms or adjusts. If fails: Adjust per feedback and re-confirm.

  3. [Agent] Source prospect companies per <Definition - Research Methods>. Search industry directories, review sites, associations, job boards. For each company capture: name, website, employee count estimate, industry, location, why they're a fit (1-2 sentences connecting to ICP). Validate: At least 20 raw companies found (to allow filtering to 15). Each matches at least 3 ICP criteria. If fails: Broaden search terms, try alternative sources. If still thin, report honestly and proceed with what was found.

  4. [Agent] Deduplicate against existing contacts. If email tools are available, search sent emails for each discovered company domain. Flag matches as "Existing Contact" with last interaction date. Flag warm paths ("You emailed someone@company.com 3 weeks ago"). Remove confirmed duplicates from final list. Validate: No prospect presented as "new" if prior contact exists. If fails: Skip deduplication. Present full list noting deduplication was unavailable.

  5. [Agent] Identify decision makers for each remaining company. Check company website team/about page (url_fetch), search "[Title] at [Company]" (web_search). Capture: full name, title/role, LinkedIn URL if found, likely email pattern, recent activity or content published. Validate: At least one named decision maker per company. If fails: Note "contact research needed" for companies with no identifiable contact. Do not fabricate.

  6. [Agent] Generate personalization intel per <Definition - Personalization Sources>. For each prospect, find 2-3 specific, verifiable hooks. Validate: Each hook is specific (names an event, article, or data point) and ideally recent (last 90 days). If fails: Use industry-level pain points as fallback. Note limited personalization.

  7. [Agent] Compile final lead list. Apply <Definition - Delivery Cascade> to select output format. Include all columns: Company, Contact, Title, Website, Location, Why They're a Fit, Personalization Hook, Warm/Cold, Next Step. Validate: Table has all columns populated. No duplicates. "Next Step" is specific per prospect. Prospect count meets target. If fails: Display as markdown table in chat.

  8. [Agent] Produce skill-ready outputs per <Definition - Skill-Ready Outputs>. Offer: "Want me to create outreach sequences for your top 3 leads? I already have their personalization hooks." Validate: Both skill-ready output blocks present. If fails: Add missing outputs.

</Workflow - Prospect Research>

<Template - Lead List Output>

Lead List: [Industry] in [Geography]

#CompanyContactTitleWebsiteLocationWhy They're a FitPersonalization HookWarm/ColdNext Step

Prospects Ready for Outreach (for outreach-sequence-builder)

| Company | Contact | Title | Top Hook | Warm/Cold |

New Prospects Added (for deal-pipeline-manager)

| Company | Contact | Estimated Value | Source | Date Added | </Template - Lead List Output>

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 Lead Prospector AI skill do?

Researches and identifies ideal prospects for small business sales. Builds targeted lead lists with company intel, decision-maker contacts, and personalized talking points. Use when user says 'find me leads', 'build a prospect list', 'who should I sell to', 'research companies in [industry]', 'find decision makers', 'identify potential customers', or 'prospect list for [product/service]'.

Why use Lead Prospector on TypingMind?

Because you install it once and use it with any model. Lead Prospector 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 Lead Prospector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/sales/lead-prospector. 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 Lead Prospector?

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 Lead Prospector?

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

Is the Lead Prospector AI skill free?

Yes. It is published on GitHub by Amazon-Quick under the MIT-0 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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