Lead Gen

Lead generation, prospecting, and qualification for B2B sales. Use when asked to find leads, build prospect lists, research target companies, qualify leads a...

Security Scan

The skill's files, instructions, and requested resources are coherent with a B2B lead-generation purpose and do not request unrelated credentials or perform hidden network exfiltration.

Purpose & Capability

Name/description match the included assets: SKILL.md describes web-based prospecting and enrichment and the repo contains a local enrichment/scoring script. No unexpected binaries, env vars, or config paths are requested.

Instruction Scope

SKILL.md instructs the agent to use web_search and manual sources (LinkedIn, Crunchbase, G2, job boards) and to run scripts/enrich_leads.py to format/score results. Instructions do not tell the agent to read unrelated system files or to send collected data to hidden endpoints. Note: the doc references references/email-formats.md which is not present in the file manifest — small documentation inconsistency.

Install Mechanism

No install spec; this is instruction-plus-script only. The included Python script uses only stdlib modules and will not fetch or execute remote code during normal runs.

Credentials

The skill declares no required environment variables or credentials. The instructions mention third-party web services (hunter.io, Crunchbase) as search sources but do not require API keys or other secrets in the package; if automated usage of those services is added later, credentials would be needed.

Persistence & Privilege

always is false and the skill does not request permanent agent presence or modify other skills. It runs as-invoked and uses a local script for processing.

Assessment

This skill appears to do what it claims: collect and score prospects using web search and a local Python scoring script. Before installing/use: (1) be aware that automated scraping of LinkedIn, Crunchbase, G2, etc. may violate those sites' terms of service and could trigger rate-limits — prefer API use where allowed; (2) if you plan to automate lookups with services like hunter.io or Crunchbase APIs, you'll need to provision and protect API keys (the skill currently requests none); (3) the repo references an email-formats doc that is missing — check the author-provided docs for completeness; (4) review privacy/regulatory obligations for storing and contacting personal data (GDPR/CCPA) and ensure consent where required; and (5) since the included script only uses the Python standard library and works on local input JSON, inspect how the agent's web_search tool is implemented in your environment to understand any network or data-exfiltration risks.

Lead Generation Skill

Build targeted, qualified prospect lists and enrich them with decision-maker data. Output is pipeline-ready — not raw noise.

Workflow

1. Define the ICP (Ideal Customer Profile)

Ask if not provided:

• Industry/vertical: (e.g. SaaS, e-commerce, professional services)
• Company size: (employees or revenue range)
• Geography: (country, region, or global)
• Job titles to target: (e.g. Head of Marketing, VP Sales, Founder)
• Pain point or trigger event: (e.g. recently funded, hiring for X role, using competitor Y)

2. Build the Target Company List

Use web_search with precision queries. See references/search-playbook.md for query templates.

Effective discovery sources:

• LinkedIn Sales Navigator signals (via search queries)
• G2/Capterra category pages (companies using specific software)
• Crunchbase (funded companies by stage, industry, date)
• Job boards (companies hiring = growing = buying)
• Industry directories and association member lists
• Subreddits and communities where ICP hangs out

3. Find Decision Makers

For each target company:

• Search "[company] [job title] LinkedIn" to identify names
• Use web_search to find personal/professional profiles
• Cross-reference company About/Team pages

4. Enrich Contacts

For each contact, gather:

• Full name, title, company
• LinkedIn URL
• Email format (guess from pattern: [e-mail], [e-mail])
• Company size, industry, location
• Recent trigger events (new role, funding, product launch)

Run scripts/enrich_leads.py to format and score the list.

5. Score & Prioritize

Score each lead 1–10 using the rubric in references/scoring-rubric.md:

• ICP fit (industry, size, title match)
• Buying signals (trigger events, tech stack, intent)
• Reachability (email confidence, LinkedIn activity)
• Timing (recently funded, new hire, Q1 budget cycle)

6. Output Format

Deliver as a CSV-ready table + summary:

Name	Title	Company	Industry	Size	Email (est.)	LinkedIn	Score	Notes

Include:

• Total leads found
• Score distribution
• Top 10 "strike now" leads highlighted
• Suggested outreach angle per segment

See references/search-playbook.md for advanced search query patterns. See references/email-formats.md for common company email format patterns.