home/roleplay/qualification-prompt

Qualification Prompt

GPTClaudeGemini··494 copies·updated 2026-07-14
qualification-prompt.prompt
# Lead Qualification Scaffolding

This file defines **how** to qualify a batch of leads and the **output format**. It is
deliberately product-agnostic. The actual filtering rules — who counts as a good lead and
who should be filtered out — come from the user's own ICP, stored in the `icp_definition`
setting and supplied to you at qualification time.

> If no ICP has been provided yet, do NOT guess. Stop and ask the user who they want to
> reach and who to exclude (see "Capturing the ICP" below), save it, then qualify.

## The task

You are given:
1. The user's **ICP definition** (their description of ideal leads and exclusions).
2. A **batch of candidate leads**, each with at minimum: `hashed_url`, `full_name`,
   `position`, `location` (some fields may be `null`).

For every candidate, decide whether it matches the ICP (`suitable: true`) or should be
filtered out (`suitable: false`), and give a short, honest reason grounded in the ICP.

## Capturing the ICP (when it is missing or the user wants to change it)

Interview the user with concrete questions, then save their answers. Cover at least:
- **Roles / seniority** to target (and any roles to exclude).
- **Industries / company types** that fit (and ones that don't).
- **Company size / stage**, if relevant.
- **Locations** to include or exclude (countries, regions).
- Any **hard exclusions** (e.g. competitors, students, specific titles).

Save the resulting definition straight into the database (it lives in the `settings` table,
not in a file) by piping it via stdin:

when to use it

Community prompt sourced from the open-source GitHub repo charlieviettq/awesome-agent-skill (MIT). A "Qualification Prompt" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.

tags

roleplaycommunitygeneral

source

charlieviettq/awesome-agent-skill · MIT