edge-signal-aggregator

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesNot pinnedSome versions float
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

examples/daily-market-dashboard/.claude/skills

return hashlib.sha1(raw.encode("utf-8"), usedforsecurity=False).hexdigest()

return hashlib.sha1(raw.encode("utf-8"), usedforsecurity=False).hexdigest()

contrib["evidence_id"] = hashlib.sha1(

contrib["event_fingerprint"] = hashlib.sha1(

PyYAML

Audit 0.4.1 · source sha af617d125b54 · 31 Oct 2026Report an issue or request a re-scan

• Publishertradermonty GitHub userOwner of the repository this skill is read from
• Source foldertradermonty/claude-trading-skills/skills/edge-signal-aggregatorThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictaf617d125b54A new audit runs when the source changes
• Repository statusNot archivedPushed today
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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,…

---
name: edge-signal-aggregator
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.
---

# 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/
```

Optional: Use a custom weights configuration:

```bash
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:
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:

```bash
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

```json
{
  "schema_version": "1.1",
  "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,
    "invalid_signals_dropped": 2,
    "idempotency_check": {
      "total_input_signals": 42,
      "unique_evidence_ids": 28,
      "duplicate_copies_merged": 14,
      "independent_units_total": 30
    }
  },
  "ranked_signals": [
    {
      "rank": 1,
      "signal_id": "sig_001",
      "title": "AI Infrastructure Capex Acceleration",
      "composite_score": 0.87,
      "evidence_id": "ticket_2026-03-01_001",
      "score_scale_assumed": false,
      "contributing_skills": [
        {
          "skill": "edge_candidate_agent",
          "signal_ref": "ticket_2026-03-01_001",
          "raw_score": 0.92,
          "weighted_contribution": 0.23,
          "evidence_id": "ticket_2026-03-01_001",
          "event_fingerprint": "sha1-of-title-and-tickers"
        },
        {
          "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": "edge_candidate_agent:ticket_001",
      "duplicates_removed": ["edge_candidate_agent:ticket_001@reports/ticket_001.json"],
      "similarity_score": 1.0,
      "reason": "same evidence re-ingested",
      "selection_rule": "newest timestamp wins; ties, missing, or unparsable timestamps keep the highest raw_score, then a stable key",
      "retained_copy": {
        "signal_ref": "ticket_001",
        "source_file": "reports/rerun.json",
        "raw_score

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/tradermonty/claude-trading-skills
git -C claude-trading-skills checkout af617d125b54

Works in

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source af617d125b54

Questions about edge-signal-aggregator

• github.com/tradermonty/claude-trading-skills/tree/main/skills/ed — the audited source at commit af617d125b54 and every finding aboveObserved · 31 Oct 2026

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