MCPInstall

prospect

by apolloioAI & Agents

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.

Installs
606
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/apolloio/apollo-mcp-plugin --skill prospect
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads prospect automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

FAQ

What does the prospect 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.

How do I install prospect?

Run npx skills add https://github.com/apolloio/apollo-mcp-plugin --skill prospect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prospect/ (personal) or .claude/skills/prospect/ (project).

Which AI agents can use prospect?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is prospect safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains prospect?

It is published by apolloio in apolloio/apollo-mcp-plugin. It has 606 recorded installs.

More from apolloio/apollo-mcp-plugin

View all →

enrich-lead

apolloio/apollo-mcp-plugin

Instant lead enrichment. Drop a name, company, LinkedIn URL, or email and get the full contact card with email, phone, title, company intel, and next actions.

Productivity & Automation602 installs

analytics

apolloio/apollo-mcp-plugin

Instant sales analytics. Ask any performance question — emails, calls, meetings, tasks, opportunities, sequences, conversation intelligence — and get formatted tables with real Apollo data.

Data & Databases601 installs

sequence-load

apolloio/apollo-mcp-plugin

Find leads matching criteria and bulk-add them to an Apollo outreach sequence. Handles enrichment, contact creation, deduplication, and enrollment in one flow.

AI & Agents596 installs

gtm-strategist

apolloio/apollo-mcp-plugin

Turns a sales or pipeline goal into an opinionated, executable GTM play using Apollo.

AI & Agents143 installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs