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.
npx agentmods add skills/wingedguardian/genesis-agi/lead-generationnpx skills add WingedGuardian/GENesis-AGI --skill lead-generationgit clone --depth 1 https://github.com/WingedGuardian/GENesis-AGIWrote 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.
[](https://agentmods.dev/skills/wingedguardian/genesis-agi/lead-generation)<a href="https://agentmods.dev/skills/wingedguardian/genesis-agi/lead-generation"><img src="https://agentmods.dev/badge/skills/wingedguardian/genesis-agi/lead-generation.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.01641 |
| Opus 5 | $0.00009 | $0.00821 |
| Sonnet 5 | $0.00004 | $0.00328 |
| Haiku 4.5 | $0.00002 | $0.00164 |
Grade A, and why
lead-generation 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Generation
Purpose
Discover prospects matching an Ideal Customer Profile (ICP), enrich with publicly available data, score on a 0-100 rubric, deduplicate against known leads, and produce structured reports. Pairs naturally with the OSINT skill for deep enrichment on high-scoring leads.
When to Use
- User defines a target market, role, or company profile to prospect.
- A scheduled lead generation cycle triggers.
- An OSINT investigation surfaces a company worth prospecting.
- Strategic reflection identifies a market opportunity to explore.
Pipeline
Phase 1: ICP Construction
Build the Ideal Customer Profile from user requirements:
icp:
industry: <target industry or industries>
role: <decision-maker titles (e.g., CTO, VP Engineering, Head of AI)>
company_size: <startup(1-50) | smb(50-500) | enterprise(500+) | any>
geography: <region or country focus>
growth_signals:
- <what indicates a good prospect (hiring, funding, product launch)>
tech_stack: <relevant technologies they should use>
exclusions:
- <companies or categories to skip>
Phase 2: Discovery Queries
Generate 5-10 search queries combining ICP dimensions:
"[industry]" "[role]" hiring— active demand signal"[industry]" companies "series A" OR "series B" OR "series C"— funded companies"top [industry] startups" [year]— curated listssite:crunchbase.com "[industry]" "[geography]"— structured data"[industry]" "[role]" interview OR podcast— visible decision-makers"[industry]" companies "[tech_stack]"— technology fit"[industry]" "fastest growing" OR "Inc 5000" OR "emerging"— growth signals
Target: discover 2-3x the desired lead count to allow for filtering.
Phase 3: Enrichment
Three tiers based on configured depth:
Basic (from discovery):
- Person name and title
- Company name
- Source URL
Standard (add web research):
- Company website → employee count, industry, product description
site:stackshare.io "[company]"ORsite:builtwith.com→ tech stack- Job board signals (what roles are they hiring for?)
- Recent news (funding, launches, partnerships)
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.
- 6d ago First seen · 203 lines · 18 tokens per session scan A f54b0cdbeae0
lead-generation is a skill published in the GitHub repository WingedGuardian/GENesis-AGI (95 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,641 once invoked, about $0.0001 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 2026-08-30.
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