# Edge Signal Aggregator

Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent...

- Repository: https://github.com/tradermonty/claude-trading-skills (3536 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add tradermonty/claude-trading-skills --skill edge-signal-aggregator -g -a claude-code -y`
Codex: `npx skills add tradermonty/claude-trading-skills --skill edge-signal-aggregator -g -a codex -y`
Cursor: `npx skills add tradermonty/claude-trading-skills --skill edge-signal-aggregator -g -a cursor -y`
Gemini CLI: `npx skills add tradermonty/claude-trading-skills --skill edge-signal-aggregator -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:

```bash
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/
```

The rest: https://github.com/tradermonty/claude-trading-skills/blob/HEAD/skills/edge-signal-aggregator/SKILL.md