Outbound Prospecting Engine
SkillCommunicationEnd-to-end outbound prospecting: detect intent signals, research companies, find decision-maker contacts, personalize messaging, launch campaign.
Use Outbound Prospecting Engine in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Outbound Prospecting Engine skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by gooseworks-ai/goose-skills in skills/outreach/playbooks/outbound-prospecting-engine/SKILL.md and read by Ahel’s review.
Build and run a complete outbound prospecting system: signal detection → company research → contact finding → personalization → campaign launch.
When to Use
- "Set up outbound prospecting for [client]"
- "Build a lead gen engine targeting [ICP]"
- "Find and reach out to companies that need [solution]"
Prerequisites
- Client context.md with ICP, value props, positioning
- Signal keywords (what to monitor for intent)
- Approved messaging / email sequences (or generate them)
Steps
1. Define Signal Sources
Based on the client's ICP and motion, select which signals to monitor:
| Signal Source | Best For | Skill |
|---|---|---|
| Job postings | Companies with allocated budget | job-posting-intent |
| Funding announcements | Companies with fresh capital | funding-signal-monitor |
| LinkedIn posts/comments | Practitioners discussing the problem | linkedin-post-research + linkedin-commenter-extractor |
| Conference attendees | People actively engaged with the space | luma-event-attendees |
| Competitor customers | Companies already buying similar solutions | competitor-post-engagers |
2. Run Signal Detection
Execute selected signal skills with client-specific keywords. Run in parallel.
Output: Raw signal list — companies + signal context.
3. Qualify & Score
Skill: lead-qualification
Filter against ICP criteria. Score each lead:
- Multi-signal leads = highest priority
- Job posting + funding = strongest intent
- Single social mention = lowest (awareness only)
4. Find Decision-Maker Contacts
Skill: company-contact-finder
For top qualified companies, find the specific decision-makers:
- Target titles from client's ICP
- Get email addresses and LinkedIn URLs
5. Deduplicate
Skill: contact-cache
Check all leads against the contact cache. Add new leads to cache. Skip any that have been contacted before.
6. Personalize Outreach
For each lead, generate personalized email sequence using:
- The signal that surfaced them (the "why now")
- Their company context (what they do, their pain)
- The client's value proposition (how it solves their pain)
7. Launch Campaign
Skill: cold-email-outreach
Set up the outreach campaign in your chosen tool:
- Create campaign with name and schedule
- Upload lead list
- Configure 2-3 email sequence (personalized per lead or per segment)
- Allocate mailboxes
- Set sending schedule
8. Monitor & Iterate
- Track open rates, reply rates, meeting bookings
- A/B test subject lines and messaging
- Re-run signal detection weekly to add new leads
- Update contact cache with outcomes
Ongoing Cadence
- Weekly: Re-run signal detection, qualify new leads, add to campaign
- Bi-weekly: Review campaign metrics, adjust messaging
- Monthly: Review overall pipeline contribution, adjust signal sources
Human Checkpoints
- After Step 3: Review qualified lead list before finding contacts
- After Step 6: Review personalized email copy before launching campaign
- After Step 8: Review campaign performance metrics
Signals
- GitHub stars
- 1k
- Forks
- 211
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
outbound-prospecting-engine- Source
- github.com/gooseworks-ai/goose-skills
github.com/gooseworks-ai/goose-skills
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