lead-researcher

name: lead-researcher description: > Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks. Use when building a target account list, qualifying inbound leads, prepping for outreach, or scoring prospects. license: MIT + Commons Clause metadata: version: 1.0.0 author: borghei category: personal-productivity domain: sales-prospecting updated: 2026-05-04 python-tools: lead_qualifier.py tech-stack: sales, outbound, CRM

Lead Researcher

Score and qualify sales leads against an Ideal Customer Profile (ICP) definition, then draft outreach hooks tied to specific ICP signals.

Table of Contents

• Keywords
• Quick Start
• Core Workflows
• Tools
• Reference Guides
• Templates
• Best Practices

Keywords

lead, leads, prospect, prospecting, sales, outbound, ICP, ideal customer profile, qualify, qualification, scoring, account list, target account, outreach, cold email, BDR, SDR, account executive

Clarify First

Before scoring leads, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• ICP definition — the must-have / nice-to-have / disqualifier attributes; this IS the scoring model
• Lead list columns — company, industry, size, country at minimum; missing fields mean no usable score
• GTM motion — PLG vs sales-led vs channel changes which signals weight highest and the outreach-hook framing

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

Score a Lead List in 10 Minutes

1. Define your ICP in icp.json using the schema in assets/icp_schema.json
2. Save your lead list as a CSV with columns: company,industry,size,country,website,signals
3. Run the qualifier:

   python scripts/lead_qualifier.py icp.json leads.csv
   

4. Review the ranked output — top 20% is your A-tier outreach list

Core Workflows

Workflow 1: ICP-Based Lead Scoring

Goal: Rank a list of candidate accounts so the top of the list reflects the best fit, not the most recent import.

Steps:

1. Build your ICP in icp.json — see assets/icp_schema.json for the full schema
2. Capture leads in a CSV with at minimum: company,industry,size,country
3. Run: python scripts/lead_qualifier.py icp.json leads.csv
4. Sort the result by score (highest first); the top 20% is your A-tier
5. Discard everything below the disqualification threshold rather than mass-emailing

Expected Output: Ranked list with score, tier (A/B/C/disqualified), and reason per lead.

Time Estimate: 10-15 minutes for a list of 200 leads.

Workflow 2: ICP Definition

Goal: Convert a fuzzy "we sell to ops teams at mid-market SaaS" intuition into a structured ICP that the qualifier can actually score against.

Steps:

1. Pull the company names of your last 20-50 best customers
2. Identify the shared signals: industry, size band, geography, tech stack, pain trigger
3. For each, decide whether it's a must-have, nice-to-have, or disqualifier
4. Encode in icp.json per references/icp_framework.md
5. Pressure-test by scoring last quarter's closed-won and closed-lost accounts — the model should rank the wins above the losses

Expected Output: A versioned icp.json that retroactively predicts your past wins.

Time Estimate: 1-2 hours for first pass, 30 minutes per quarterly refresh.

Workflow 3: Outreach Hook Drafting

Goal: Write outreach where the personalization actually mentions a real signal, not a fake "I noticed you posted on LinkedIn."

Steps:

1. Take the qualifier output for an A-tier lead
2. Read the matched ICP signals — these are your hooks
3. Use the outreach template in assets/outreach_template.md
4. Personalize the opening line with the strongest signal (e.g., recent funding, hiring spike, product launch, public quote about a pain you solve)
5. Keep the rest of the email short — sub-90 words

Expected Output: First-touch outreach email under 90 words with a real signal-based hook.

Time Estimate: 5 minutes per A-tier lead.

Tools

lead_qualifier.py

Reads an ICP JSON file and a leads CSV, returns a scored & tiered list.

# Human-readable
python scripts/lead_qualifier.py icp.json leads.csv

# JSON for programmatic use
python scripts/lead_qualifier.py icp.json leads.csv --json

Scoring model:

• Each ICP attribute has a weight (default 10) and direction (must / nice / disqualify)
• Must-have hits: full weight
• Nice-to-have hits: half weight
• Disqualifier hits: lead drops out of consideration entirely
• Score is normalized to 0-100

Reference Guides

• references/icp_framework.md — How to define an ICP that actually predicts deal velocity, with worked examples by GTM motion (PLG, sales-led, channel)

Templates

• assets/icp_schema.json — JSON schema for an ICP definition file
• assets/outreach_template.md — Cold-touch email template with placeholder slots tied to ICP signals

Best Practices

• Disqualify hard. Mediocre leads are worse than no leads — they consume rep time and damage sender reputation.
• Keep ICP versioned. When deal velocity drops, your ICP is often stale. Re-derive every quarter.
• One signal per email. Multi-signal openers feel like research dumps; one well-chosen signal feels human.
• Leads are not opportunities. A scored A-tier lead is permission to reach out, not a forecasted deal.
• Logs over feel. Track which signals correlate with closed-won — let data update the ICP, not vibes.

Integration Points

• Pairs with marketing/cold-email/ for sequence design
• Pairs with sales-success/ skills for account executive handoff
• Feeds into business-growth/ revenue forecasting