Goose-skills inbound-lead-qualification

install

source · Clone the upstream repo

git clone https://github.com/gooseworks-ai/goose-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/gooseworks-ai/goose-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/composites/inbound-lead-qualification" ~/.claude/skills/gooseworks-ai-goose-skills-inbound-lead-qualification && rm -rf "$T"

manifest: skills/composites/inbound-lead-qualification/SKILL.md

Inbound Lead Qualification

Takes a set of inbound leads and validates each against your full ICP criteria. Not a fast-pass triage (that's

inbound-lead-triage

) — this is the thorough qualification step that determines whether a lead is genuinely worth pursuing, and produces a scored CSV for the team.

When to Auto-Load

Load this composite when:

User says "qualify these inbound leads", "check if these leads are ICP", "score my inbound"
An upstream triage has been completed and leads need deeper qualification
User has a batch of leads and wants a qualified/disqualified verdict on each

Architecture

[Inbound Leads] → Step 1: Load ICP & Config → Step 2: CRM/Pipeline Check → Step 3: Company Qualification → Step 4: Person Qualification → Step 5: Use Case Fit → Step 6: Score & Verdict → Step 7: Output CSV

Step 0: Configuration (Once Per Client)

On first run, establish the ICP definition and CRM access. Save to the current working directory or wherever the user prefers (e.g.,

config/lead-qualification.json

{
  "icp_definition": {
    "company_size": {
      "min_employees": null,
      "max_employees": null,
      "sweet_spot": "",
      "notes": ""
    },
    "industry": {
      "target_industries": [],
      "excluded_industries": [],
      "notes": ""
    },
    "use_case": {
      "primary_use_cases": [],
      "secondary_use_cases": [],
      "anti_use_cases": [],
      "notes": ""
    },
    "company_stage": {
      "target_stages": [],
      "excluded_stages": [],
      "notes": ""
    },
    "geography": {
      "target_regions": [],
      "excluded_regions": [],
      "notes": ""
    }
  },
  "buyer_personas": [
    {
      "name": "",
      "titles": [],
      "seniority_levels": [],
      "departments": [],
      "is_economic_buyer": false,
      "is_champion": false,
      "is_user": false
    }
  ],
  "hard_disqualifiers": [],
  "hard_qualifiers": [],
  "crm_access": {
    "tool": "HubSpot | Salesforce | CSV export | none",
    "access_method": "",
    "tables_or_objects": []
  },
  "existing_customer_source": {
    "tool": "HubSpot | Salesforce | CSV | none",
    "access_method": ""
  },
  "qualification_prompt_path": "path/to/lead-qualification/prompt.md or null"
}

lead-qualification

capability already has a saved qualification prompt: Reference it directly — don't rebuild ICP criteria from scratch.

On subsequent runs: Load config silently.

Step 1: Load ICP Criteria & Parse Leads

Process

Load the client's ICP config (or qualification prompt from
```
lead-qualification
```
capability)
Parse the inbound lead list — accept any format:
- Output from
```
inbound-lead-triage
```
  (already normalized)
- Raw CSV with any column structure
- Pasted list of names/emails/companies
- CRM export
Identify what data is available vs. missing per lead:
- Have: Fields present in the input
- Need: Fields required for qualification but missing
- Gap report: "X leads have company name, Y have title, Z have nothing but email"

Output

Parsed lead list with available/missing field inventory
Gap report for the user

Human Checkpoint

If >50% of leads are missing critical fields (company name or person title), recommend running

inbound-lead-enrichment

first. Ask: "Many leads are missing company/title data. Want me to enrich them first, or qualify with what's available?"

Step 2: CRM & Pipeline Check

Process

For each lead, check against existing data sources to identify overlaps:

Check 1 — Existing customer?

Search customer database by company domain/name
If match found: Flag as
```
existing_customer
```
with customer details (plan, account owner, contract status)
This is NOT a disqualification — it's a routing flag (upsell vs. new business)

Check 2 — Already in pipeline?

Search your CRM (HubSpot, Salesforce, CSV) for the company in active deals
If match found: Flag as
```
in_pipeline
```
with deal details (stage, owner, last activity)
Critical: Sales rep should know before reaching out that a colleague already has this account

Check 3 — Previous engagement?

Search outreach logs for the email/company
If match found: Flag as
```
previously_contacted
```
with history summary (when, what channel, outcome)

Check 4 — Known from signal composites?

Search your CRM or signal tracking system for the company
If match found: Flag as
```
signal_flagged
```
with signal type and date

Output

Each lead tagged with:

pipeline_status

new

existing_customer

in_pipeline

previously_contacted

```
pipeline_detail
```
: One sentence explaining the overlap (or null)
```
signal_flags
```
: Any signal composite matches

Handling Overlaps

Existing customer: Don't disqualify. Mark separately. Might be expansion/upsell.
In pipeline: Don't disqualify. Flag for sales rep coordination. Note the existing deal owner.
Previously contacted but no response: Still qualify. The inbound signal means they're now warmer.
Previously contacted and rejected: Still qualify the inbound. People change their minds. Note the prior context.

Step 3: Company Qualification

Process

For each lead's company, evaluate against every ICP company dimension:

Dimension 1 — Company Size

Check employee count against ICP range
Sources: enrichment data, LinkedIn company page, web search
Score:
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```
Note: If the lead is from a subsidiary or division, evaluate the relevant unit, not the parent company

Dimension 2 — Industry

Check against target and excluded industry lists
Be smart about classification: "AI-powered HR platform" matches both "AI/ML" and "HR Tech"
Score:
```
match
```
|
```
adjacent
```
(related but not core target) |
```
mismatch
```
|
```
unknown
```

Dimension 3 — Company Stage

Seed, Series A, Series B+, Growth, Public, Bootstrapped
Sources: SixtyFour or Orthogonal, news, enrichment data
Score:
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```

Dimension 4 — Geography

Check HQ location and/or the specific person's location
For remote-first companies, check where the majority of the team is
Score:
```
match
```
|
```
borderline
```
|
```
mismatch
```
|
```
unknown
```

Dimension 5 — Use Case Fit

Based on what the company does, could they plausibly use the product?
This is the most nuanced dimension — requires understanding both the product and the company's operations
Sources: company website, product description, job postings (hint at internal tools/processes)

Score:

strong_fit

moderate_fit

weak_fit

no_fit

unknown

Output

Each lead gets a

company_qualification

block:

{
  "company_size": { "score": "", "value": "", "reasoning": "" },
  "industry": { "score": "", "value": "", "reasoning": "" },
  "stage": { "score": "", "value": "", "reasoning": "" },
  "geography": { "score": "", "value": "", "reasoning": "" },
  "use_case": { "score": "", "value": "", "reasoning": "" },
  "company_verdict": "qualified | borderline | disqualified | insufficient_data"
}

Step 4: Person Qualification

Process

For each lead's contact person, evaluate against buyer persona criteria:

Dimension 1 — Title/Role Match

Check title against buyer persona title lists
Handle variations: "VP of Marketing" = "Vice President, Marketing" = "VP Marketing"
Be smart about title inflation at small companies (a "Director" at a 10-person startup ≠ "Director" at a 10,000-person enterprise)

Score:

exact_match

close_match

adjacent

mismatch

unknown

Dimension 2 — Seniority Level

Map to: Individual Contributor, Manager, Director, VP, C-Level, Founder
Check against ICP seniority requirements
Score:
```
match
```
|
```
too_junior
```
|
```
too_senior
```
|
```
unknown
```

Dimension 3 — Department

Engineering, Product, Marketing, Sales, Operations, Finance, HR, etc.
Check against ICP department targets
Score:
```
match
```
|
```
adjacent
```
|
```
mismatch
```
|
```
unknown
```

Dimension 4 — Authority Type

Based on title + seniority, classify:
- ```
economic_buyer
```
  — Can sign the check
- ```
champion
```
  — Wants it, can influence the decision
- ```
user
```
  — Would use it daily, can validate need
- ```
evaluator
```
  — Tasked with research, limited decision power
- ```
gatekeeper
```
  — Can block but not approve
- ```
unknown
```

Dimension 5 — Right Person, Wrong Company (or Vice Versa)

If company qualifies but person doesn't: Flag as
```
right_company_wrong_person
```
— this is a referral opportunity
If person qualifies but company doesn't: Flag as
```
right_person_wrong_company
```
— rare for inbound, but possible with job changers

Output

Each lead gets a

person_qualification

block:

{
  "title_match": { "score": "", "value": "", "reasoning": "" },
  "seniority": { "score": "", "value": "", "reasoning": "" },
  "department": { "score": "", "value": "", "reasoning": "" },
  "authority_type": "",
  "person_verdict": "qualified | borderline | disqualified | insufficient_data",
  "mismatch_type": "null | right_company_wrong_person | right_person_wrong_company"
}

Step 5: Use Case Fit Assessment

Process

This step connects the company's likely needs to your product's actual capabilities. It goes deeper than Step 3's company-level use case check.

Infer the lead's intent from their inbound action:
- Demo request message → What did they say they need?
- Content downloaded → What topic were they researching?
- Webinar attended → What problem were they trying to solve?
- Free trial signup → What feature did they try first?
- Chatbot conversation → What questions did they ask?
Map intent to product capabilities:
- Does the product actually solve what they seem to need?
- Is this a primary use case or a stretch?
- Are there known limitations that would disappoint them?
Assess implementation feasibility:
- Based on company size and stage, can they realistically implement?
- Do they likely have the technical resources / team to adopt?
- Any known blockers for companies like this? (e.g., "banks need SOC2 and we don't have it yet")

Output

{
  "inferred_intent": "",
  "intent_source": "",
  "product_fit": "strong | moderate | weak | unknown",
  "product_fit_reasoning": "",
  "implementation_feasibility": "easy | moderate | complex | unlikely",
  "known_blockers": []
}

Step 6: Score & Verdict

Scoring Logic

Combine all dimensions into a final qualification verdict.

Composite Score Calculation:

Dimension	Weight	Possible Values
Company Size	15%	match=100, borderline=50, mismatch=0, unknown=30
Industry	20%	match=100, adjacent=60, mismatch=0, unknown=30
Company Stage	10%	match=100, borderline=50, mismatch=0, unknown=30
Geography	10%	match=100, borderline=50, mismatch=0, unknown=30
Use Case Fit	25%	strong=100, moderate=60, weak=20, no_fit=0, unknown=30
Person Title/Role	15%	exact=100, close=75, adjacent=40, mismatch=0, unknown=30
Person Seniority	5%	match=100, too_junior=20, too_senior=60, unknown=30

Hard overrides (bypass the score):

Any hard disqualifier present →
```
disqualified
```
regardless of score
Any hard qualifier present →
```
qualified
```
regardless of score (but still show the full breakdown)
Existing customer → Route separately, don't score as new lead

Verdict thresholds:

Score ≥ 75:
```
qualified
```
— Pursue actively
Score 50-74:
```
borderline
```
— Qualified with caveats, may need manual review
Score 30-49:
```
near_miss
```
— Not qualified now, but close enough to consider (referral or nurture)
Score < 30:
```
disqualified
```
— Does not fit ICP

Sub-verdicts for routing:

```
qualified_hot
```
— Score ≥ 75 AND Tier 1/2 urgency from triage
```
qualified_warm
```
— Score ≥ 75 AND Tier 3/4 urgency
```
borderline_review
```
— Score 50-74, needs human judgment call
```
near_miss_referral
```
— Score 30-49 AND right_company_wrong_person (referral opportunity)
```
near_miss_nurture
```
— Score 30-49, might fit in the future
```
disqualified_polite
```
— Score < 30, needs polite decline
```
disqualified_competitor
```
— Competitor employee
```
existing_customer_upsell
```
— Existing customer with expansion signal

Output

Each lead gets:

{
  "composite_score": 0-100,
  "verdict": "",
  "sub_verdict": "",
  "top_qualification_reasons": [],
  "top_disqualification_reasons": [],
  "summary": "One sentence: why this lead is/isn't a fit"
}

Step 7: Output CSV

CSV Structure

Produce a CSV with ALL input fields preserved plus qualification columns appended:

Core qualification columns:

```
qualification_verdict
```
— qualified | borderline | near_miss | disqualified
```
qualification_sub_verdict
```
— qualified_hot | qualified_warm | borderline_review | near_miss_referral | near_miss_nurture | disqualified_polite | disqualified_competitor | existing_customer_upsell
```
composite_score
```
— 0-100
```
summary
```
— One sentence qualification reasoning

Pipeline check columns:

```
pipeline_status
```
— new | existing_customer | in_pipeline | previously_contacted
```
pipeline_detail
```
— One sentence on the overlap
```
signal_flags
```
— Any signal composite matches

Company qualification columns:

```
company_size_score
```
— match | borderline | mismatch | unknown
```
industry_score
```
— match | adjacent | mismatch | unknown
```
stage_score
```
— match | borderline | mismatch | unknown
```
geography_score
```
— match | borderline | mismatch | unknown
```
use_case_score
```
— strong | moderate | weak | no_fit | unknown

Person qualification columns:

```
title_match_score
```
— exact_match | close_match | adjacent | mismatch | unknown
```
seniority_score
```
— match | too_junior | too_senior | unknown
```
authority_type
```
— economic_buyer | champion | user | evaluator | gatekeeper | unknown
```
mismatch_type
```
— null | right_company_wrong_person | right_person_wrong_company

Use case columns:

```
inferred_intent
```
— What they seem to need
```
product_fit
```
— strong | moderate | weak | unknown
```
implementation_feasibility
```
— easy | moderate | complex | unlikely

Save Location

The current working directory or wherever the user prefers (e.g.,

leads/inbound-qualified-[date].csv

Summary Report

After producing the CSV, present a summary:

## Inbound Lead Qualification: [Period]

**Total leads processed:** X
**Qualified:** X (Y%) — X hot, X warm
**Borderline (manual review):** X (Y%)
**Near miss:** X (Y%) — X referral opportunities, X nurture
**Disqualified:** X (Y%)

**Pipeline overlaps:**
- Existing customers: X (route to CS)
- Already in pipeline: X (coordinate with deal owner)
- Previously contacted: X (now warmer — re-engage)

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

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

**Data quality:**
- Leads with full data: X
- Leads with partial data (some dimensions scored as 'unknown'): X
- Leads needing enrichment: X

**CSV saved to:** [path]

Handling Edge Cases

Lead with only an email (no name, no company):

Extract company domain from email
If corporate domain: look up the company, proceed with company qualification (person qualification will be mostly "unknown")
If personal email (gmail, yahoo): Score as
```
insufficient_data
```
, recommend enrichment or manual review

Same company, multiple leads:

Qualify the company once, apply to all leads from that company
Person qualification runs individually for each
Flag the multi-contact opportunity: "3 people from [Company] came inbound — potential committee buy"

Contradictory signals:

Company is strong fit but person is completely wrong (e.g., intern at a perfect company)
Score honestly. The sub-verdict
```
right_company_wrong_person
```
routes this to referral handling in
```
disqualification-handling
```

Borderline calls:

When the score is 50-74 and could go either way, lean toward qualifying for inbound leads
Rationale: they came to YOU. The intent signal tips borderline cases toward "worth a conversation"
Note this lean in the reasoning: "Borderline on [dimension], but inbound intent suggests pursuing"

Scoring with missing data:

Unknown dimensions score at 30 (not 0, not 50) — absence of data is mildly negative but not disqualifying
If >3 dimensions are "unknown", the lead is
```
insufficient_data
```
regardless of score — recommend enrichment first

Tools Required

CRM access — to check pipeline, existing customers, outreach history
Web search — for company research when enrichment data is sparse
Enrichment tools — SixtyFour or Orthogonal, LinkedIn scraper, or similar (optional, enhances accuracy)
Read/Write — for CSV I/O and config management