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](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](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](references/notion-schema.md) for complete schema and routing logic.

Examples

Basic Usage

[See examples/basic-usage.md](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](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](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](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%