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Prospect

OrganizationPopular
anthropics
prospect

Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers.

Overview

Publisheranthropics
Repositoryknowledge-work-plugins
Skill nameprospect
Stars
25.9K
Forks
3K
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Prospect 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/anthropics/knowledge-work-plugins/tree/main/partner-built/apollo/skills/prospect
  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/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins
mkdir -p .claude/skills
cp -r /tmp/knowledge-work-plugins/partner-built/apollo/skills/prospect .claude/skills/prospect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prospect 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 Prospect 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 Prospect 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.

Prospect

Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".

Examples

  • /apollo:prospect VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees
  • /apollo:prospect heads of marketing at e-commerce companies in Europe
  • /apollo:prospect CTOs at fintech startups, 50-500 employees, New York
  • /apollo:prospect procurement managers at manufacturing companies with 1000+ employees
  • /apollo:prospect SDR leaders at companies using Salesforce and Outreach

Step 1 — Parse the ICP

Extract structured filters from the natural language description in "$ARGUMENTS":

Company filters:

  • Industry/vertical keywords → q_organization_keyword_tags
  • Employee count ranges → organization_num_employees_ranges
  • Company locations → organization_locations
  • Specific domains → q_organization_domains_list

Person filters:

  • Job titles → person_titles
  • Seniority levels → person_seniorities
  • Person locations → person_locations

If the ICP is vague, ask 1-2 clarifying questions before proceeding. At minimum, you need a title/role and an industry or company size.

Step 2 — Search for Companies

Use mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search with the company filters:

  • q_organization_keyword_tags for industry/vertical
  • organization_num_employees_ranges for size
  • organization_locations for geography
  • Set per_page to 25

Step 3 — Enrich Top Companies

Use mcp__claude_ai_Apollo_MCP__apollo_organizations_bulk_enrich with the domains from the top 10 results. This reveals revenue, funding, headcount, and firmographic data to help rank companies.

Step 4 — Find Decision Makers

Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with:

  • person_titles and person_seniorities from the ICP
  • q_organization_domains_list scoped to the enriched company domains
  • per_page set to 25

Step 5 — Enrich Top Leads

Credit warning: Tell the user exactly how many credits will be consumed before proceeding.

Use mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match to enrich up to 10 leads per call with:

  • first_name, last_name, domain for each person
  • reveal_personal_emails set to true

If more than 10 leads, batch into multiple calls.

Step 6 — Present the Lead Table

Show results in a ranked table:

Leads matching: [ICP Summary]

#NameTitleCompanyEmployeesRevenueEmailPhoneICP Fit

ICP Fit scoring:

  • Strong — title, seniority, company size, and industry all match
  • Good — 3 of 4 criteria match
  • Partial — 2 of 4 criteria match

Summary: Found X leads across Y companies. Z credits consumed.

Step 7 — Offer Next Actions

Ask the user:

  1. Save all to Apollo — Bulk-create contacts via mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: true for each lead
  2. Load into a sequence — Ask which sequence and run the sequence-load flow for these contacts
  3. Deep-dive a company — Run /apollo:company-intel on any company from the list
  4. Refine the search — Adjust filters and re-run
  5. Export — Format leads as a CSV-style table for easy copy-paste

Frequently asked questions

What does the Prospect AI skill do?

Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers.

Why use Prospect on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/knowledge-work-plugins/tree/main/partner-built/apollo/skills/prospect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prospect?

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 Prospect?

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

Is the Prospect AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 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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