edge-signal-aggregator

name	edge-signal-aggregator
description	Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Edge Signal Aggregator

Overview

Combine outputs from multiple upstream edge-finding skills into a single weighted conviction dashboard. This skill applies configurable signal weights, deduplicates overlapping themes, flags contradictions between skills, and ranks composite edge ideas by aggregate confidence score. The result is a prioritized edge shortlist with provenance links to each contributing skill.

When to Use

• After running multiple edge-finding skills and wanting a unified view
• When consolidating signals from edge-candidate-agent, theme-detector, sector-analyst, and institutional-flow-tracker
• Before making portfolio allocation decisions based on multiple signal sources
• To identify contradictions between different analysis approaches
• When prioritizing which edge ideas deserve deeper research

Prerequisites

• Python 3.9+
• No API keys required (processes local JSON/YAML files from other skills)
• Dependencies: pyyaml (standard in most environments)

Workflow

Step 1: Gather Upstream Skill Outputs

Collect output files from the upstream skills you want to aggregate:

• reports/edge_candidate_*.json from edge-candidate-agent
• reports/edge_concepts_*.yaml from edge-concept-synthesizer
• reports/theme_detector_*.json from theme-detector
• reports/sector_analyst_*.json from sector-analyst
• reports/institutional_flow_*.json from institutional-flow-tracker
• reports/edge_hints_*.yaml from edge-hint-extractor

Step 2: Run Signal Aggregation

Execute the aggregator script with paths to upstream outputs:

python3 skills/edge-signal-aggregator/scripts/aggregate_signals.py \
  --edge-candidates reports/edge_candidate_agent_*.json \
  --edge-concepts reports/edge_concepts_*.yaml \
  --themes reports/theme_detector_*.json \
  --sectors reports/sector_analyst_*.json \
  --institutional reports/institutional_flow_*.json \
  --hints reports/edge_hints_*.yaml \
  --output-dir reports/

Optional: Use a custom weights configuration:

python3 skills/edge-signal-aggregator/scripts/aggregate_signals.py \
  --edge-candidates reports/edge_candidate_agent_*.json \
  --weights-config skills/edge-signal-aggregator/assets/custom_weights.yaml \
  --output-dir reports/

Step 3: Review Aggregated Dashboard

Open the generated report to review:

1. Ranked Edge Ideas - Sorted by composite conviction score
2. Signal Provenance - Which skills contributed to each idea
3. Contradictions - Conflicting signals flagged for manual review
4. Deduplication Log - Merged overlapping themes

Step 4: Act on High-Conviction Signals

Filter the shortlist by minimum conviction threshold:

python3 skills/edge-signal-aggregator/scripts/aggregate_signals.py \
  --edge-candidates reports/edge_candidate_agent_*.json \
  --min-conviction 0.7 \
  --output-dir reports/

Output Format

JSON Report

{
  "schema_version": "1.0",
  "generated_at": "2026-03-02T07:00:00Z",
  "config": {
    "weights": {
      "edge_candidate_agent": 0.25,
      "edge_concept_synthesizer": 0.20,
      "theme_detector": 0.15,
      "sector_analyst": 0.15,
      "institutional_flow_tracker": 0.15,
      "edge_hint_extractor": 0.10
    },
    "min_conviction": 0.5,
    "dedup_similarity_threshold": 0.8
  },
  "summary": {
    "total_input_signals": 42,
     
     
     
  
   
    
       
       
       
       
       
        
           
           
           
           
        
        
           
           
           
           
        
      
         
       
       
       
         
         
         
      
    
  
   
    
       
       
       
         
         
         
      
       
         
         
         
      
       
    
  
   
    
       
        
       
    
  

Markdown Report

The markdown report provides a human-readable dashboard:

# Edge Signal Aggregator Dashboard
**Generated:** 2026-03-02 07:00 UTC

## Summary
- Total Input Signals: 42
- Unique After Dedup: 28
- Contradictions: 3
- High Conviction (>0.7): 12

## Top 10 Edge Ideas by Conviction

### 1. AI Infrastructure Capex Acceleration (Score: 0.87)
- **Tickers:** NVDA, AMD, AVGO
- **Direction:** LONG | **Horizon:** 3-6 months
- **Contributing Skills:**
  - edge-candidate-agent: 0.92 (ticket_2026-03-01_001)
  - theme-detector: 0.85 (theme_ai_infra)
- **Confidence Breakdown:** Agreement 0.30 | Strength 0.35 | Recency 0.22

...

## Contradictions Requiring Review

### Energy Sector Conflict
- **sector-analyst:** Bearish rotation (SHORT)
- **institutional-flow-tracker:** Heavy buying XLE (LONG)
- **Hint:** Check timeframe mismatch

## Deduplication Summary
- 14 signals merged into 8 unique themes
- Average similarity of merged signals: 0.89

Reports are saved to reports/ with filenames:

• edge_signal_aggregator_YYYY-MM-DD_HHMMSS.json
• edge_signal_aggregator_YYYY-MM-DD_HHMMSS.md

Resources

• scripts/aggregate_signals.py -- Main aggregation script with CLI interface
• references/signal-weighting-framework.md -- Rationale for default weights and scoring methodology
• assets/default_weights.yaml -- Default skill weights configuration

Key Principles

1. Provenance Tracking -- Every aggregated signal links back to its source skill and original reference
2. Contradiction Transparency -- Conflicting signals are flagged, not hidden, to enable informed decisions
3. Configurable Weights -- Default weights reflect typical reliability but can be customized per user
4. Deduplication Without Loss -- Merged signals retain references to all original sources
5. Actionable Output -- Ranked list with clear tickers, direction, and time horizon for each idea