Lead Qualification Agent Skill

Lead Qualification Engine

Qualify leads against custom criteria through a structured intake process, then score lead lists in parallel with confidence ratings and reasoning.

Three Modes of Operation

Mode 1: Full Intake + Qualify

No existing qualification prompt. Run intake to build one, save it, then qualify leads.

Trigger: User provides no qualification prompt file.

Mode 2: Reuse Prompt + Qualify

User references an existing qualification prompt file — skip intake, go straight to scoring.

Trigger: User tags or references a file in skills/lead-qualification/qualification-prompts/.

Mode 3: Refine / Calibrate

User has seen results and wants to adjust criteria. Update the saved prompt, re-run.

Trigger: User says something like "refine", "adjust", "that's wrong", or provides feedback on qualification results.

Phase 1: Intake (Mode 1 Only)

The goal is to build a complete picture of who the user considers qualified vs disqualified. Present questions in bulk rounds so the user can answer efficiently.

Round 1 — Core Questions (Present All at Once)

Present these questions as a numbered list. Tell the user: *"Answer what's relevant, skip what's not. I'll follow up on anything I need to clarify."*

Product & Campaign Context:

• What's your product/service in one sentence?
• What problem does it solve and for whom?
• What's the specific campaign or outreach angle? (e.g., "targeting companies that just raised Series A", "going after teams switching from Competitor X")

Company-Level Criteria:

• What company sizes are you targeting? (e.g., 1-10, 11-50, 51-200, 201-1000, 1000+)
• What industries or verticals are a good fit?
• Any industries or company types to explicitly EXCLUDE?
• Geographic targets? Or is this global?
• Geographic exclusions?
• Does company stage matter? (e.g., seed, Series A, Series B+, public)

10. Any revenue range or funding range that matters?

Person-Level Criteria:

11. What job titles or roles are your ideal buyers?

12. What titles are explicitly disqualified?

13. Does seniority level matter? (e.g., must be Director+, VP+, C-level)

14. What departments should they be in? (e.g., growth, marketing, sales, engineering)

15. Minimum tenure at current company? (e.g., 6+ months to have buying power)

16. Does total years of experience matter?

Behavioral & Situational Signals:

17. Are there tech stack signals that qualify or disqualify? (e.g., "uses Salesforce" = good fit)

18. Does recent company activity matter? (e.g., hiring spree, funding round, product launch)

19. Are there content/posting signals? (e.g., "posted about AI" = relevant)

20. Any other signals that indicate high intent or good fit?

Dealbreakers & Instant Qualifiers:

21. What are your HARD DISQUALIFIERS — things that instantly make someone a "no" regardless of other factors?

22. What are your STRONGEST QUALIFIERS — things that make someone an almost certain "yes"?

Round 2 — Follow-Up Probes

Based on the user's answers, ask 5-10 targeted follow-ups to resolve ambiguity. Examples:

• "You said mid-market — does that mean 50-500 or 50-1000 employees?"
• "You mentioned VP of Growth — would a 'Head of Growth' also qualify, or only VP title?"
• "You didn't mention geography — should I treat this as global?"
• "For tenure, you said 6 months minimum. What about someone who's 4 months in but was promoted internally?"
• "You mentioned Series A companies. What about bootstrapped companies with equivalent revenue?"

Round 3 — Edge Case Scenarios (Optional but Recommended)

Present 3-5 hypothetical lead profiles that test boundary cases. Ask "Would you qualify this person?"

Example scenarios to construct (adapt based on the user's criteria):

• Someone who fits the title but is at a company that's slightly too large/small
• Someone at the right company but with a borderline title
• Someone who matches on everything but has low tenure
• Someone who doesn't match the title exactly but has high intent signals
• Someone at a competitor's customer

This round catches implicit criteria the user hasn't articulated.

Generate & Save Qualification Prompt

After intake is complete, synthesize all answers into a structured qualification prompt. Save it to:

skills/lead-qualification/qualification-prompts/[campaign-name].md

The saved prompt MUST follow this structure:

# Qualification Prompt: [Campaign Name]

Generated: [date]

## Campaign Context
- **Product:** [one-liner]
- **Campaign Angle:** [specific angle]
- **Problem Solved:** [what and for whom]

## Hard Disqualifiers (Instant No)
- [list each with explanation]

## Hard Qualifiers (Instant Yes)
- [list each with explanation]

## Company Criteria
| Criterion | Qualified | Disqualified | Notes |
|-----------|-----------|--------------|-------|
| Size | [range] | [range] | |
| Industry | [list] | [list] | |
| Geography | [list] | [list] | |
| Stage | [list] | [list] | |
| Funding/Revenue | [range] | [range] | |

## Person Criteria
| Criterion | Qualified | Disqualified | Notes |
|-----------|-----------|--------------|-------|
| Titles | [list] | [list] | |
| Seniority | [level+] | [below level] | |
| Department | [list] | [list] | |
| Tenure | [minimum] | [below minimum] | |
| Experience | [range] | [range] | |

## Behavioral & Situational Signals
- [list signals that boost qualification]
- [list signals that reduce qualification]

## Confidence Rules
- **High Confidence:** Enough data available for company size, title, tenure, and at least one signal.
- **Medium Confidence:** Missing one or two non-critical data points but core criteria are clear.
- **Low Confidence:** Missing critical data points (e.g., no company size, unclear title). Still make a yes/no call but flag it.

## Edge Case Guidance
- [specific guidance derived from Round 3 scenarios]
- [any nuanced rules from the intake conversation]

## Qualification Reasoning Instructions
When evaluating a lead, structure your reasoning as:
1. Check hard disqualifiers first — if any match, immediately disqualify.
2. Check hard qualifiers — if any match, lean strongly toward qualifying.
3. Evaluate company criteria against thresholds.
4. Evaluate person criteria against thresholds.
5. Factor in behavioral/situational signals as tiebreakers.
6. Assign confidence based on data completeness.
7. Write 2-3 sentence reasoning summarizing the decision.

Phase 2: Lead Qualification

Step 1 — Parse Input

Accept any of these input formats:

• CSV file path — Read directly from filesystem (default)
• LinkedIn profile URLs — One or more URLs provided inline
• Inline list — Names/companies listed in the message
• Google Sheet URL — If the user provides a Google Sheet, use whatever Google Sheets tool is available to read it
• Any other source — Ask the user to export as CSV or paste the data

Detect the format automatically based on what the user provides.

Step 1.5 — Batch Enrichment via Apify

When: The input contains a linkedin_url column (or LinkedIn URLs are available).

Skip when: No LinkedIn URLs are present, or the user explicitly says to skip enrichment.

Before LLM qualification, batch-enrich all leads to gather structured profile data. This is MUCH faster and cheaper than per-lead web searches during qualification.

Run the enrichment script:

python3 skills/lead-qualification/scripts/enrich_leads.py INPUT_CSV \
  --output ENRICHED_CSV \
  --cache-hours 24

Use --dry-run first to show the cost estimate without calling Apify.

What this does:

• Reads all LinkedIn URLs from the input CSV
• Checks local cache (24h default) — skips profiles already enriched
• Sends uncached URLs to Apify in batches of 50
• Returns enriched CSV with: enriched_title, enriched_company, enriched_industry, enriched_location, enriched_connections, enriched_education, enriched_experience_years, enriched_headline, enriched_about, enrichment_status
• Cost: ~$3 per 1,000 profiles (~$0.03 per 100 leads)

After enrichment, use the enriched CSV as input for Steps 2-4. The enriched data lets the LLM qualification step work from structured fields instead of doing web searches, dramatically improving speed and consistency.

If enrichment fails for some profiles: They'll have enrichment_status: failed in the output. The LLM qualification step should fall back to web search for those leads only.

Step 2 — Calibration Batch

Before processing the full list, run the first 5-10 leads and present results to the user in a table.

If batch enrichment was run (Step 1.5), use the enriched columns (enriched_title, enriched_company, etc.) as the primary data source. Only fall back to web search for leads where enrichment_status is failed or no_url.

| # | Name | Title | Company | Qualified | Confidence | Reasoning |
|---|------|-------|---------|-----------|------------|-----------|
| 1 | ... | ... | ... | Yes | High | ... |
| 2 | ... | ... | ... | No | Medium | ... |
| ... |

Ask: "Do these look right? Should I adjust any criteria before processing the full list?"

If the user flags issues:

• Discuss what needs to change
• Update the saved qualification prompt file
• Re-run the calibration batch
• Confirm again before proceeding

Repeat until the user approves.

Step 3 — Full Run (Parallelized)

Once calibration is approved, process ALL remaining leads using parallel subagents. You MUST parallelize — do NOT process leads sequentially.

Parallelization protocol (mandatory):

• Calculate batch count:
• Total remaining leads / 15 = number of batches (round up)
• Target: ~15 leads per batch
• Minimum: 2 batches (even for small lists, to validate parallelism works)
• Maximum: 10 concurrent batches (to avoid overwhelming context)

• Prepare batch inputs:

For each batch, create a self-contained context package:

• The full qualification prompt (from qualification-prompts/ file)
• The batch of lead rows (with ALL columns including enriched data from Step 1.5)
• Instructions for output format: Name, Qualified (Yes/No), Confidence (High/Medium/Low), Reasoning (2-3 sentences)
• Instruction: "For leads with enrichment_status=failed or no_url, do a quick web search. Spend no more than 30 seconds per lead on search."

• Launch parallel Task agents:

Use the Task tool to launch ALL batches simultaneously in a single message with multiple tool calls:

   Task: "Qualify leads batch 1/N"
   Context: [qualification prompt] + [batch 1 lead rows]

   Task: "Qualify leads batch 2/N"
   Context: [qualification prompt] + [batch 2 lead rows]

   ... (launch ALL at once — do NOT wait for batch 1 before launching batch 2)

• Collect and merge results:
• Wait for all Task agents to complete
• Merge all batch results into a single list
• Preserve original row order from the input
• If any batch fails, retry that batch once. If it fails again, flag those leads as "qualification_failed" and proceed.

• Validate completeness:
• Count: total qualified + disqualified + failed = total input leads
• If any leads are missing, identify and re-process them

Per-lead processing (within each batch agent):

• Read all available data from the input row (including enriched columns from Step 1.5)
• If enriched data is present (enrichment_status: success or cached): use enriched_title, enriched_company, etc.
• If enriched data is missing (enrichment_status: failed or no_url): do a quick web search (max 30 seconds)
• Apply the qualification prompt:
• Check hard disqualifiers first — if any match, immediately disqualify
• Check hard qualifiers — if any match, lean strongly toward qualifying
• Evaluate all criteria
• Determine: Qualified (Yes/No), Confidence (High/Medium/Low)
• Write 2-3 sentence reasoning
• Return the result

Step 4 — Output Results

Default: CSV

• Write a CSV with all original columns PLUS three new columns:
• Qualified — Yes / No
• Confidence — High / Medium / Low
• Reasoning — 2-3 sentence explanation
• Save to the current working directory or wherever the user prefers
• Tell the user the file path

If the user prefers Google Sheets or another destination:

• Write to Google Sheets if tools are available
• Write to Notion if requested
• Export in any format the user asks for

Step 5 — Summary

After output is complete, present a summary:

## Qualification Results: [Campaign Name]

**Total leads processed:** X
**Qualified:** X (Y%)
**Disqualified:** X (Y%)

**Confidence breakdown:**
- High: X leads
- Medium: X leads
- Low: X leads (may need manual review)

**Top disqualification reasons:**
1. [reason] — X leads
2. [reason] — X leads
3. [reason] — X leads

**Output:** [Google Sheet link or CSV path]
**Qualification prompt saved to:** skills/lead-qualification/qualification-prompts/[campaign-name].md

Tools Required

The qualification agent should have access to:

• Apify LinkedIn Enrichment — scripts/enrich_leads.py for batch profile enrichment before qualification
• Uses harvestapi~linkedin-profile-scraper Apify actor ($3/1k profiles, no cookies)
• Requires APIFY_API_TOKEN environment variable
• Run with --dry-run first to preview cost
• Web Search — to research leads when enrichment data is sparse or missing
• Fetch (web page) — to pull LinkedIn profiles, company pages, etc.
• Read/Write — for CSV I/O and saving qualification prompts
• Glob/Grep — to find existing qualification prompt files
• Optional: Google Sheets tools — if the user wants to read from or write to Google Sheets

Example Usage

Full intake + qualify from CSV:

Qualify leads for our outbound campaign. Here's the lead list: leads.csv

→ Agent detects no saved prompt, starts intake, builds prompt, then qualifies.

Reuse existing prompt:

Qualify these leads using @skills/lead-qualification/qualification-prompts/series-a-founders.md
— lead list: leads.csv

→ Agent skips intake, goes straight to calibration + qualification.

Qualify LinkedIn profiles directly:

Using the series-a-founders qualification prompt, qualify these people:
- https://linkedin.com/in/person1
- https://linkedin.com/in/person2
- https://linkedin.com/in/person3

Refine after seeing results:

Those results look off — also disqualify anyone at a consulting firm, and lower the
tenure minimum to 3 months for Director+ titles.

→ Agent updates the saved prompt and re-runs.