inbound-lead-qualification

inbound-lead-qualification is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 77 tokens per session (4,266 once invoked), scanned A, original, MIT.

A workflow that checks inbound leads against your ideal customer profile, or ICP—the description of the companies and people most likely to buy. It also checks for duplicates and existing customer or sales relationships, then produces a scored CSV.

In plain words
What is it for?
Use it to assess company size, industry, use-case fit, role, and seniority, with a qualification result and explanation for each lead.
Why use it?
It separates genuinely suitable prospects from leads that only look promising because they submitted a form.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to assess company size, industry, use-case fit, role, and seniority, with a qualification result and explanation for each lead.

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Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/inbound-lead-qualification
About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,202 stars · on GitHub · gooseworks.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gooseworks-ai/goose-skills --skill inbound-lead-qualification
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for inbound-lead-qualification

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/inbound-lead-qualification/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/inbound-lead-qualification)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/inbound-lead-qualification"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/inbound-lead-qualification/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for inbound-lead-qualification

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/inbound-lead-qualification"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/inbound-lead-qualification.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,266 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.04266
Opus 5 $0.00039 $0.02133
Sonnet 5 $0.00015 $0.00853
Haiku 4.5 $0.00008 $0.00427

Measured 9d ago against content hash e924a997cf47, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

inbound-lead-qualification scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/lead-generation/composites/inbound-lead-qualification/SKILL.md · 451 lines

How it starts

The opening of the file, as written. The whole thing — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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"
}

Read the full file on GitHub · 451 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 451 lines · 77 tokens per session scan A e924a997cf47

Subscribe to this mod's changes

inbound-lead-qualification is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 77 tokens to every session and 4,266 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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