edge-signal-aggregator

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Edge Signal Aggregator

Overview

概述

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.

将多个上游边缘发现技能的输出合并为单一加权信念仪表盘。该技能支持可配置的信号权重、重叠主题去重、标记不同技能间的矛盾，并按综合置信度评分对复合边缘想法进行排序。最终输出带有来源链接的优先级边缘候选列表，可追溯到每个贡献技能。

When to Use

适用场景

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

运行多个边缘发现技能后需要统一视图时
需要整合edge-candidate-agent、theme-detector、sector-analyst和institutional-flow-tracker的信号时
在基于多个信号源做出投资组合分配决策前
需要识别不同分析方法之间的矛盾时
需要对哪些边缘想法值得深入研究进行优先级排序时

Prerequisites

前置要求

Python 3.9+
No API keys required (processes local JSON/YAML files from other skills)
Dependencies:
```
pyyaml
```
(standard in most environments)

Python 3.9+
无需API密钥（处理来自其他技能的本地JSON/YAML文件）
依赖：
```
pyyaml
```
（大多数环境中默认内置）

Workflow

工作流程

Step 1: Gather Upstream Skill Outputs

步骤1：收集上游技能输出

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

收集你想要聚合的上游技能输出文件：

```
reports/edge_candidate_*.json
```
来自 edge-candidate-agent
```
reports/edge_concepts_*.yaml
```
来自 edge-concept-synthesizer
```
reports/theme_detector_*.json
```
来自 theme-detector
```
reports/sector_analyst_*.json
```
来自 sector-analyst
```
reports/institutional_flow_*.json
```
来自 institutional-flow-tracker
```
reports/edge_hints_*.yaml
```
来自 edge-hint-extractor

Step 2: Run Signal Aggregation

步骤2：运行信号聚合

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/

执行聚合器脚本，传入上游输出的路径：

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/

可选：使用自定义权重配置：

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

步骤3：查看聚合仪表盘

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

打开生成的报告查看以下内容：

已排序边缘想法 - 按综合信念评分排序
信号来源 - 每个想法由哪些技能贡献
矛盾提示 - 标记冲突信号供人工审核
去重日志 - 合并的重叠主题

Step 4: Act on High-Conviction Signals

步骤4：基于高信念信号采取行动

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/

按最低信念阈值过滤候选列表：

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

输出格式

JSON Report

JSON报告

json

{
  "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
    }
  ]
}

json

{
  "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

Markdown报告

The markdown report provides a human-readable dashboard:

markdown

undefined

Markdown报告提供人类易读的仪表盘：

markdown

undefined

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

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

...

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

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`

14 signals merged into 8 unique themes
Average similarity of merged signals: 0.89


报告保存到 `reports/` 目录，文件名如下：
- `edge_signal_aggregator_YYYY-MM-DD_HHMMSS.json`
- `edge_signal_aggregator_YYYY-MM-DD_HHMMSS.md`

Resources

资源

```
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

```
scripts/aggregate_signals.py
```
-- 带CLI界面的主聚合脚本
```
references/signal-weighting-framework.md
```
-- 默认权重和评分方法的原理说明
```
assets/default_weights.yaml
```
-- 默认技能权重配置

Key Principles

核心原则

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

来源可追溯 -- 每个聚合信号都可以回溯到其源技能和原始参考
矛盾透明化 -- 冲突信号会被标记而非隐藏，支持做出知情决策
权重可配置 -- 默认权重反映典型可靠性，但用户可自定义
去重不丢失信息 -- 合并后的信号保留所有原始来源的引用
输出可落地 -- 排序列表附带每个想法明确的股票代码、方向和时间范围