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Edge Signal Aggregator Agent Skill

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.

22k tokens
context cost
the whole folder, loaded on every use
6
files
ships runnable scripts
1
copies elsewhere
how many repositories repackaged it
118
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/BaggaT236/AI-Trading-Skills --skill edge-signal-aggregator

What comes with it

79 247 bytes besides the instruction
assets/default_weights.yaml
references/signal-weighting-framework.md
scripts/aggregate_signals.py
scripts/tests/conftest.py
scripts/tests/test_aggregate_signals.py

The instruction itself

14 sections, as written by the author

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:

  • 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/

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,
    "unique_signals_after_dedup": 28,
    "contradictions_found": 3,
    "signals_above_threshold": 12
  },
  "ranked_signals": [
    {
      "rank": 1,
      "signal_id": "sig_001",
      "title": "AI Infrastructure Capex Acceleration",
      "composite_score": 0.87,
      "contributing_skills": [
        {
          "skill": "edge_candidate_agent",
          "signal_ref": "ticket_2026-03-01_001",
          "raw_score": 0.92,
          "weighted_contribution": 0.23
        },
        {
          "skill": "theme_detector",
          "signal_ref": "theme_ai_infra",
          "raw_score": 0.85,
          "weighted_contribution": 0.13
        }
      ],
      "tickers": ["NVDA", "AMD", "AVGO"],
      "direction": "LONG",
      "time_horizon": "3-6 months",
      "confidence_breakdown": {
        "multi_skill_agreement": 0.30,
        "signal_strength": 0.35,
        "recency": 0.22
      }
    }
  ],
  "contradictions": [
    {
      "contradiction_id": "contra_001",
      "description": "Conflicting sector view on Energy",
      "skill_a": {
        "skill": "sector_analyst",
        "signal": "Energy sector bearish rotation",
        "direction": "SHORT"
      },
      "skill_b": {
        "skill": "institutional_flow_tracker",
        "signal": "Heavy institutional buying in XLE",
        "direction": "LONG"
      },
      "resolution_hint": "Check timeframe mismatch (short-term vs long-term)"
    }
  ],
  "deduplication_log": [
    {
      "merged_into": "sig_001",
      "duplicates_removed": ["theme_detector:ai_compute", "edge_hints:datacenter_demand"],
      "similarity_score": 0.92
    }
  ]
}

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

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

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How to use it

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Take baggat236/edge-signal-aggregator from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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