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: 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. Deduplication Log Merged overlapping themes Step 4: Act on High Conviction Signals Filter the shortlist by minimum conviction threshold: Output Format JSON Report Markdown Report The markdown report provides a human readable dashboard: 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

edge-signal-aggregator