lead-intelligence
by affaan-mlead-intelligence is an AI lead intelligence workflow for Lead Research, scoring prospects, finding warm paths, and drafting outreach. Use the lead-intelligence skill to build a ranked lead list, assess fit, and turn research into email, LinkedIn, or X outreach with less guesswork.
This skill scores 82/100, which means it is a solid listing candidate for directory users who want a real lead-intelligence workflow rather than a generic outreach prompt. The repository gives enough operational detail to trigger the skill, understand the pipeline, and see how it uses signals for scoring, mutual mapping, enrichment, and outreach, though it still depends on external tool access and lacks some install-time guidance.
- Clear activation cues for lead finding, outreach lists, warm intros, and prospect ranking, including example user phrases.
- Multi-stage workflow is explicit: signal scoring, enrichment, mutual mapping, and outreach drafting are separated into dedicated agent files.
- Concrete scoring rubrics and output expectations reduce guesswork for agents and make the workflow more reusable.
- Requires external services such as Exa MCP and X API credentials, which may limit immediate usability.
- No install command, scripts, or support files are provided, so setup and integration will require manual interpretation.
Overview of lead-intelligence skill
What lead-intelligence does
lead-intelligence is an AI lead intelligence workflow for finding, scoring, and prioritizing prospects, then turning that research into outreach paths. It is best for users who need lead-intelligence for Lead Research: building a target list, judging who matters most, and finding a warm route in instead of guessing.
Who should use it
Use the lead-intelligence skill if you are doing sales prospecting, partnership outreach, fundraising research, creator/influencer sourcing, or founder-to-founder networking. It is a strong fit when the real job is not just “find names,” but “find the right names, with context, and decide who is worth contacting first.”
What makes it different
This skill combines signal scoring, mutual ranking, warm path discovery, and channel-specific outreach. That matters if you care about more than a static enrichment list: it helps you move from raw search results to a ranked, actionable short list with better timing and better entry points.
How to Use lead-intelligence skill
Install and activate it
For lead-intelligence install, add the skill to your Claude Code environment and then work from the skill files in the repo. The baseline command shown in the source is:
npx skills add affaan-m/everything-claude-code --skill lead-intelligence
After installation, make sure the environment can access the required tools, especially Exa search and X API credentials, or the workflow will be partially blocked.
Start with the right input
The lead-intelligence usage pattern is strongest when you give a narrow target. Good inputs include:
- target industry or niche
- buyer persona or role
- geography or timezone
- what counts as a qualified lead
- preferred channel: email, LinkedIn, or X
- whether you want warm paths, scoring, or outreach drafting
A weak ask like “find leads for my startup” leaves too much undefined. A stronger prompt looks like: “Find 25 SaaS ops leaders in North America, score by relevance and recent activity, then identify warm intro paths and draft 5 cold emails.”
Read these files first
For the fastest implementation path, inspect:
SKILL.mdfor activation rules and required toolsagents/signal-scorer.mdfor ranking logicagents/mutual-mapper.mdfor warm-path analysisagents/enrichment-agent.mdfor profile and company contextagents/outreach-drafter.mdfor message length and personalization rules
This is the best lead-intelligence guide approach because it tells you what the workflow needs before you try to run it.
Workflow that produces better results
A practical sequence is:
- Define the target market and ICP.
- Use signal scoring to build a ranked prospect list.
- Enrich the top prospects with current role, company, activity, and context.
- Map mutuals or other warm paths.
- Draft outreach only after you have a believable reason to contact each person.
If you skip scoring or enrichment, the outreach step tends to become generic. The skill works best when each stage narrows uncertainty for the next one.
lead-intelligence skill FAQ
Is lead-intelligence only for sales teams?
No. The lead-intelligence skill is also useful for partnerships, fundraising, recruiting, and expert sourcing. If you need to prioritize people by relevance and contactability, it can help.
Do I need special APIs for lead-intelligence?
Yes, the core workflow depends on Exa and X API access. Optional sources like LinkedIn, Apollo, Clay, or GitHub can improve coverage, but the skill is not just a prompt template; it expects real search and graph data.
Is this better than a normal prompt?
Usually yes, if you need repeatable prospecting. A generic prompt can draft a few leads, but lead-intelligence adds a structured method for scoring, mutual analysis, and outreach sequencing, which reduces guesswork and improves consistency.
When should I not use it?
Do not use it if you only need a one-off list of company names or if you do not have access to the required data sources. It is also a poor fit when your audience is too broad, because the ranking logic depends on clear targeting.
How to Improve lead-intelligence skill
Give the scoring model better inputs
The biggest quality gain comes from clearer criteria: ideal title, company stage, geography, deal size, topic relevance, and what makes someone worth contacting now. The more explicit your filters, the less the lead-intelligence workflow has to infer.
Ask for evidence, not just names
When you request output, ask for the signal behind each lead: recent post, role change, funding event, shared connection, or topic overlap. That helps the skill avoid superficial matches and makes the shortlist easier to defend internally.
Separate research from outreach
A common failure mode is asking for a lead list and final messaging in one pass. Better results come from a two-step loop: first identify and rank, then enrich and draft. If the first pass looks off, tighten the target before generating messages.
Iterate on the weakest link
If results are close but not usable, refine the part that failed: improve the persona definition, add disqualifiers, or narrow the channel. For lead-intelligence for Lead Research, small changes to ICP or source requirements usually improve output more than asking for “more leads.”