Agent skill · data analytics · tradermonty
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.
Why this skill is useful
Adds a comprehensive aggregation script that combines and ranks signals from multiple edge-finding skills, providing new analytical capabilities.
What it needs
Requires python, pyyaml installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
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/edgecandidate.json from edge-candidate-agent reports/edgeconcepts.yaml from edge-concept-synthesizer reports/themedetector.json from theme-detector reports/sectoranalyst.json from sector-analyst reports/institutionalflow.json from institutional-flow-tracker reports/edgehints.yaml from edge-hint-extractor Step 2: Run Signal Aggregation Execute the aggregator script with paths to upstream outputs: Optional: Use a custom weights configuration: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/edge-signal-aggregator