Lead Qualify logo

Lead Qualify

Organization
runxhq
lead-qualify

Assess known person or company facts against supplied ICP criteria, keeping commercial fit separate from contact eligibility.

Overview

Publisherrunxhq
Repositoryrunx
Skill namelead-qualify
Stars
105
Forks
109
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Lead Qualify 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/runxhq/runx/tree/main/skills/lead-qualify
  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/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/lead-qualify .claude/skills/lead-qualify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lead Qualify 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 Qualify 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 Qualify 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.

Lead Qualify

Decide whether one lead meets the actual ICP. Use lead-enrichment for factual context; use this skill for commercial judgment; use cold-outreach for copy. Qualification neither obtains consent nor creates a provider send plan.

Procedure

  1. Supply the enrichment packet, explicit ICP criteria, and a separately sourced contact-eligibility decision. At least one criterion must be mandatory. The eligibility names the subject it decides, with the same ref and kind as the enrichment subject. Eligibility decided for any other subject requests evidence before any criterion is assessed.
  2. Keep missing and conflicting facts visible. Partial enrichment can support a criterion only through its admitted, non-conflicting facts.
  3. Assess every criterion as met, not_met or unknown. Cite exact factual field names for each supported assessment and explain its relevance.
  4. A failed mandatory criterion disqualifies the lead. An unknown mandatory criterion requests evidence. All mandatory criteria met establish fit; optional criteria explain strengths and weaknesses without overriding it.
  5. Return the fit independently from contact eligibility. A good fit with unknown or ineligible contact status remains a hold.

Never replace the supplied ICP with generic scoring. A company title is not buying authority, activity is not consent, and a matched work email is not permission to contact. Do not infer protected or sensitive personal traits.

Result and composition

lead_qualification records the exact enrichment digest, subject, criteria, assessments, contact-eligibility evidence and a bounded next action: prepare_outreach, request_evidence, resolve_contact_policy or stop. Contact eligibility appears only when it was decided for that subject; another subject's eligibility is left out, and its blocker remains. No message, CRM update, enrollment or durable suppression change occurs.

A qualified result is context for cold-outreach#prepare, not send authority. That skill requires its own exact recipient-bound eligibility/suppression input. The selected sending capability performs native approval and delivery. The calling application owns task assignment, refresh, follow-up timing and continuing work.

Agent task contract

lead-qualify-assess

Apply each supplied criterion to the admitted facts. Return one assessment per criterion ID, with status, rationale and exact fact_fields. Use unknown when the required evidence is absent or contradictory; it may cite no field. Do not invent fact fields, change mandatory criteria, score on sensitive traits, reinterpret contact eligibility or claim an external action.

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 Qualify AI skill do?

Assess known person or company facts against supplied ICP criteria, keeping commercial fit separate from contact eligibility.

Why use Lead Qualify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/lead-qualify. 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 Qualify?

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 Qualify?

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

Is the Lead Qualify AI skill free?

Yes. It is published on GitHub by runxhq 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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