Edge Signal Aggregator

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.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview
2. When to Use
3. Prerequisites
4. Quick Start
5. Workflow
6. Resources

1. 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.

2. 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

3. Prerequisites

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

4. Quick Start

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/

5. 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:

Ranked Edge Ideas - Sorted by composite conviction score
Signal Provenance - Which skills contributed to each idea
Contradictions - Conflicting signals flagged for manual review
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/

6. Resources

References:

skills/edge-signal-aggregator/references/signal-weighting-framework.md

Scripts:

skills/edge-signal-aggregator/scripts/aggregate_signals.py