This skill should be used when developing, debugging, refactoring, or building Genesis itself — tasks like "fix this in Genesis", "add a new MCP tool", "wire up the runtime", "Genesis won't start", "create a worktree", "debug the bridge", or "add a capability". Applies to any task modifying files under src/, .claude/, or tests/. Do NOT load for Genesis-as-tool work ("summarize this", "write a LinkedIn post", "research X") or general questions unrelated to Genesis internals.

2026-07-1889

voice-master

WingedGuardian/GENesis-AGI

Foundational voice authority and AI humanizer — writes content in the user's authentic voice with built-in AI detection, and supports stealth / anti- attribution writing (forum personas, anonymous posts, "write as not-me"). Use when asked to write/draft/generate content, invoke /voice, /write-as-me, or /humanize, run voice calibration, check "does this sound like me?", "make this sound human" / "de-AI this", "write a forum post as [persona]", or run AI detection ("does this sound like AI?", "check for AI patterns", "anti-slop check"). Do NOT use this skill for code, technical docs, or any output the user has not asked to be written in their voice — code styling defers to the separate code-voice skill.

2026-07-1489

aws-fde-delivery

WingedGuardian/GENesis-AGI

Forward Deployed Engineer delivery contract for AWS engagements, build-first artifacts, grounded cost estimates, Well-Architected review, evolution roadmap

2026-07-0789

subsystem-map

WingedGuardian/GENesis-AGI

This skill should be used before answering "does Genesis have X", "does Genesis lack X", auditing Genesis capabilities, comparing Genesis to an external system, or reviewing/summarizing the architecture. It routes to the canonical judgment-layer subsystem map so audits start from the map, not from a cold grep. Also fires after changing a subsystem's capabilities, to keep the map current.

2026-07-0789

onboarding

WingedGuardian/GENesis-AGI

First-run onboarding — guides new users through Genesis setup on their first CC session. Configures user profile, essential API keys, Telegram, GitHub backup, and service verification. Triggered automatically when ~/.genesis/setup-complete is absent. Re-runnable by asking Genesis to "run setup" or "reconfigure [section]".

2026-07-0689

genesis-voice

WingedGuardian/GENesis-AGI

Apply when Genesis writes as itself — outreach, public content, community posts, emails, DMs. Not for writing in the user's voice (that's voice-master). Activate when Genesis is the author, not the ghostwriter.

2026-07-0489

name	lead-generation
description	Prospect discovery, enrichment, scoring, and reporting against an Ideal Customer Profile
consumer	cc_background_task
phase	7
skill_type	uplift

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 lists
site: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]" OR site:builtwith.com → tech stack
Job board signals (what roles are they hiring for?)
Recent news (funding, launches, partnerships)

Deep (add targeted investigation):

Funding history (Crunchbase, press releases)
Company news (last 6 months)
Social profiles (public LinkedIn via site:linkedin.com, Twitter/X)
Competitive positioning
Consider triggering OSINT skill for high-value targets

Phase 4: Deduplication

Before scoring, deduplicate against known leads:

Normalization rules:

Company: strip legal suffixes (Inc, LLC, Ltd, Corp, Co, GmbH, AG, SA), lowercase, remove "The" prefix
Person: lowercase, remove middle names, handle common nicknames (Bob=Robert, Mike=Michael, Bill=William, Jim=James)

Match criteria (any = duplicate):

Exact normalized company name + person name
Fuzzy match (Levenshtein distance < 2)
Domain match (same company website)

Phase 5: Scoring

Score each lead 0-100 on this rubric:

Category	Max Points	Breakdown
ICP Match	30	Industry match +10, Company size +5, Geography +5, Role/title match +10
Growth Signals	20	Recent funding +8, Actively hiring +6, Product launch +3, Press coverage +3
Enrichment Quality	20	Email pattern found +5, LinkedIn found +5, Full company data +5, Tech stack known +5
Recency	15	Active this month +15, This quarter +10, This year +5
Accessibility	15	Direct contact info +15, Company contact page +10, Social only +5

Score grades:

A (80-100): Hot lead — high ICP match, strong signals, accessible
B (60-79): Warm lead — good match, some gaps
C (40-59): Cool lead — partial match, needs more enrichment
D (0-39): Cold lead — weak match, archive but don't pursue

Phase 6: Report Generation

# Lead Report: [ICP Description]

**Date:** YYYY-MM-DD
**Leads discovered:** N (after dedup)
**Grade distribution:** A: N, B: N, C: N, D: N

## Hot Leads (A-Grade)

| # | Name | Title | Company | Score | Key Signal |
|---|------|-------|---------|-------|-----------|

## Warm Leads (B-Grade)

| # | Name | Title | Company | Score | Key Signal |
|---|------|-------|---------|-------|-----------|

## Summary
- Total new leads: N
- Duplicates filtered: N
- Top industries represented: ...
- Common growth signals: ...

## Recommended Next Steps
- <which leads to prioritize>
- <what enrichment to run next>
- <ICP refinements based on findings>

Phase 7: State Persistence

Store leads as observations via MemoryStore
Tag with ICP profile for future cycle dedup
Record discovery metadata for report generation

LinkedIn Approach

Compliance-safe pattern:

Use "[name]" "[company]" site:linkedin.com via search engine
Only access publicly visible profile information
Do NOT use LinkedIn API for scraping
Do NOT bypass login walls
Do NOT send unsolicited connection requests or messages
LinkedIn Organization Messaging API (OAuth2) is available for messaging IF the user has proper API credentials and authorization

Email pattern discovery (reference only):

firstname@domain
firstname.lastname@domain
f.lastname@domain
firstname.l@domain
Never send unsolicited emails. Pattern discovery is for user reference.

Output Format

report_id: <LEAD-YYYY-MM-DD-NNN>
date: <YYYY-MM-DD>
icp_summary: <one-line ICP description>
leads_discovered: <count>
duplicates_filtered: <count>
leads:
  - name: <person name>
    title: <job title>
    company: <company name>
    score: <0-100>
    grade: <A | B | C | D>
    icp_match:
      industry: <match | partial | no>
      size: <match | partial | no>
      geography: <match | partial | no>
      role: <match | partial | no>
    growth_signals:
      - <signal description>
    enrichment:
      linkedin: <URL or null>
      website: <URL or null>
      tech_stack: [<technologies>]
      recent_funding: <description or null>
    source_urls:
      - <where this lead was found>

References

src/genesis/skills/osint/SKILL.md — Deep enrichment for high-value leads
src/genesis/skills/research/SKILL.md — General research methodology
src/genesis/memory/ — MemoryStore for lead persistence