Lead Enrichment

Organization

runxhq

lead-enrichment

Normalize factual person and company evidence for one confidently matched lead, preserving provenance, freshness, missing fields and conflicts.

Overview

Publisher	runxhq
Repository	`runx`
Skill name	`lead-enrichment`
Stars	105
Forks	109
Bundled files	6
License	Apache-2.0
Links	Skill source Repository Homepage

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 Enrichment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/runxhq/runx/tree/main/skills/lead-enrichment
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.

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-enrichment .claude/skills/lead-enrichment

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lead Enrichment 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 Enrichment 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 Enrichment 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 Enrichment

Build factual context for one person or company. Enrichment answers what is known about the subject. lead-qualify decides whether those facts meet an ICP; cold-outreach prepares copy only after contact eligibility is supplied.

The default enrich runner reuses supplied structured evidence. It makes no provider request and spends no enrichment credits. An upstream declared connector operation can supply evidence; Apollo is the planned first lookup provider, whose credential presentation remains a native connector dependency. Do not claim that running this skill performs an Apollo lookup.

Procedure

Bind one subject reference and the professional fields actually needed.
Supply source references, native or upstream digests, observation times, explicit subject matches, and factual field/value pairs. A caller-supplied digest is provenance, not independent provider verification.
Reuse only fresh sources confidently matched to that exact subject. A weak name match, stale observation or mismatched identity does not fill a field.
Preserve conflicting values with their separate sources. Do not silently choose the latest value or invent a merged person from several matches.
Return enriched, partial or needs_more_evidence, supported facts, missing fields, conflicts and the admitted source index.

The runner performs deterministic normalization rather than an extra model turn. Evidence acquisition and entity matching belong to the selected provider; commercial fit belongs to qualification. It does not infer buying intent, budget, consent, sensitive traits or private personal details.

Composition

Pass lead_enrichment_packet into lead-qualify#qualify together with the actual ICP criteria. Preserve the native artifact digest. For another lookup, request only missing professional fields with a bounded credit allowance and the same subject identity. Never enable personal-email/phone reveal or a waterfall merely to make an incomplete packet look complete.

The skill neither updates CRM records nor authorizes messages. The calling application owns refresh schedules and continuing work; connectors own exact provider requests, credential custody and billing evidence.

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 Enrichment AI skill do?

Normalize factual person and company evidence for one confidently matched lead, preserving provenance, freshness, missing fields and conflicts.

Why use Lead Enrichment on TypingMind?

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

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

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 Enrichment?

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

Is the Lead Enrichment 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Lead Enrichment

Procedure

Composition

Bundled files

Frequently asked questions

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