name: deal-sourcing-agent description: Run the Discovery Engine pipeline to find new consumer companies. Use when the user asks to "find deals", "source companies", "run the pipeline", "discover startups", or "search for prospects" in CPG, health tech, travel, or marketplaces. allowed-tools:

• Bash
• Read license: MIT metadata: version: 1.0.0 category: workflow-automation author: Press On Ventures

Deal Sourcing Agent

Transform CLI-based discovery into a guided, conversational workflow for sourcing consumer companies.

When to Use This Skill

This skill activates when you want to:

• Find new investment prospects
• Run the discovery pipeline
• Source companies in specific sectors (CPG, health tech, travel, marketplaces)
• Review and push qualified signals to Notion CRM

Trigger phrases:

• "Help me find new deals"
• "Run the discovery pipeline"
• "Source companies in [sector]"
• "What new prospects do we have*"
• "Check for new SEC filings"

Quick Start

Simplest invocation:

User: "Find me some new deals"

The skill will guide you through 6 steps: Configuration → Collection → Processing → Review → Push → Health Check.

Workflow

Step 1: Configuration

Ask which collectors to run and which sectors to focus on.

Collector Presets:

Validation Gate: User confirms configuration before proceeding.

Step 2: Collection

Execute collectors and report results.

python run_pipeline.py collect --collectors <preset>

Output: Signals collected, duplicates found, collector status (✓/✗)

Decision Point:

• A) Proceed to processing
• B) Adjust collectors
• C) Cancel

Step 3: Processing

Run verification gate and thesis filter on collected signals.

python run_pipeline.py process

Output:

• Qualified signals (ready for push)
• Held signals (need review)
• Rejected signals (excluded by thesis)

Decision Point:

• A) Review qualified signals
• B) Push all qualified to Notion
• C) Review held signals
• D) Show metrics

Step 4: Review (Optional)

Display top qualified signals with confidence scores.

python run_pipeline.py pipeline qualified --limit 20

Output: Table with company name, canonical key, confidence, signal types, why now.

Decision Point:

• A) Push all to Notion
• B) Filter further
• C) Exclude specific companies

Step 5: Push to Notion

Sync qualified signals to Notion CRM.

# Dry run preview
python run_pipeline.py pipeline push --dry-run

# Actual push (after confirmation)
python run_pipeline.py pipeline push --confirm

Output:

• Prospects created (Status: "Source" or "Tracking")
• Prospects updated
• Prospects skipped (duplicates)
• Notion URLs

Step 6: Health Check

Run post-execution diagnostics and recommend next steps.

python run_pipeline.py health --json

Output:

• Component health (Database, APIs, cache)
• Anomaly detection results
• Rate limit status
• Recommendations (review held signals, schedule next run)

Collector Guide

See references/collector-guide.md for complete list of 16 collectors, API requirements, and signal strengths.

Fast Preset Collectors:

• github - Trending repos, spike detection (0.5-0.7 confidence)
• sec_edgar - SEC Form D filings (0.6-0.8 confidence)
• companies_house - UK incorporations (0.6-0.8 confidence)

Error Handling

Missing API Keys

If a collector fails due to missing credentials:

⚠ Missing API Key: GITHUB_TOKEN

Setup:
1. Generate token at: https://github.com/settings/tokens
2. Add to .env file: GITHUB_TOKEN=ghp_xxx
3. Restart skill

Alternative: Run other collectors without GitHub

Rate Limits

If rate limited:

⚠ GitHub Rate Limit Exceeded

Resets at: 2026-01-31 14:30 UTC (in 42 minutes)

Options:
  A) Wait 42 minutes, then retry
  B) Run other collectors
  C) Use authenticated token (increases limit)

Zero Qualified Signals

If no signals pass verification:

ℹ No Qualified Signals

Results: 0 qualified, 12 held, 3 rejected

Next Steps:
  A) Review held signals
  B) Adjust thesis filters
  C) Run different collectors

For complete troubleshooting guide, see references/troubleshooting.md.

Notion Schema

The pipeline pushes to Notion with these fields:

• Status: "Source" (multi-source, high confidence) or "Tracking" (single source)
• Discovery ID: Unique identifier for tracking
• Canonical Key: Deduplication key (domain:example.com)
• Confidence Score: 0.0-1.0 routing score
• Signal Types: Multi-select (github, sec_edgar, etc.)
• Why Now: Narrative explaining timing

See references/notion-schema.md for complete schema and routing logic.

Examples

Basic Usage

See examples/basic-usage.md

User: "Find me some new deals" → Guided through Fast preset (2 min) → 12 qualified signals pushed to Notion

Sector-Specific

See examples/sector-specific.md

User: "Source consumer CPG companies" → Collectors filtered by CPG keywords → Thesis filter emphasizes food/beverage/beauty

Advanced Options

See examples/advanced-options.md

• Run single collector
• Dry-run mode for testing
• View detailed metrics
• Import from CSV

Common Scenarios

Related Skills

• thesis_matching.md - Investment thesis evaluation criteria
• founder_evaluation.md - Founder intelligence assessment
• signal_quality.md - Quality tiers and freshness scoring

Technical Details

For pipeline architecture, stage-by-stage flow, and PipelineStats schema, see references/pipeline-architecture.md.

Success Criteria

You'll know this skill is working when:

• Time to first qualified signal < 3 minutes
• You complete workflow without errors
• Qualified signals appear in Notion CRM
• You understand what was found and why

Metrics:

• Typical batch: 5-20 qualified signals
• Collector success rate: >85%
• False positive rate: <15%