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edge-hint-extractor

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Edge Hint Extractor

Edge Hint Extractor

Overview

概述

Convert raw observation signals (
market_summary
, 
anomalies
, 
news reactions
) into structured edge hints. This skill is the first stage in the split workflow: 
observe -> abstract -> design -> pipeline
.
将原始观察信号（
market_summary
、
anomalies
、
news reactions
）转换为结构化的边缘提示。 该技能是拆分工作流的第一阶段：
observe -> abstract -> design -> pipeline
。

When to Use

适用场景

  • You want to turn daily market observations into reusable hint objects.
  • You want LLM-generated ideas constrained by current anomalies/news context.
  • You need a clean 
    hints.yaml
    input for concept synthesis or auto detection.
  • 希望将每日市场观察转换为可复用的提示对象时。
  • 希望在当前异常/新闻背景约束下，由LLM生成创意时。
  • 需要为概念合成或自动检测提供干净的
    hints.yaml
    输入时。

Prerequisites

前置条件

  • Python 3.9+
  • PyYAML
  • Optional inputs from detector run: 
    • market_summary.json
    • anomalies.json
    • news_reactions.csv
      or 
      news_reactions.json
  • Python 3.9+
  • PyYAML
  • 检测器运行产生的可选输入： 
    • market_summary.json
    • anomalies.json
    • news_reactions.csv
      或 
      news_reactions.json

Output

输出

  • hints.yaml
    containing: 
    • hints
      list
    • generation metadata
    • rule/LLM hint counts
  • hints.yaml
    文件，包含： 
    • hints
      列表
    • 生成元数据
    • 规则/LLM提示计数

Workflow

工作流

  1. Gather observation files (
    market_summary
    , 
    anomalies
    , optional news reactions).
  2. Run 
    scripts/build_hints.py
    to generate deterministic hints.
  3. Optionally add 
    --llm-ideas-cmd
    to augment hints.
  4. Pass 
    hints.yaml
    into concept synthesis or auto detection.
  1. 收集观察文件（
    market_summary
    、
    anomalies
    ，可选新闻反馈）。
  2. 运行
    scripts/build_hints.py
    生成确定性提示。
  3. 可选添加
    --llm-ideas-cmd
    参数来扩充提示。
  4. 将
    hints.yaml
    传入概念合成或自动检测环节。

Quick Commands

快速命令

Rule-based only:
bash
python3 skills/edge-hint-extractor/scripts/build_hints.py \
  --market-summary /tmp/edge-auto/market_summary.json \
  --anomalies /tmp/edge-auto/anomalies.json \
  --news-reactions /tmp/news_reactions.csv \
  --as-of 2026-02-20 \
  --output /tmp/edge-hints/hints.yaml
Rule + LLM augmentation:
bash
python3 skills/edge-hint-extractor/scripts/build_hints.py \
  --market-summary /tmp/edge-auto/market_summary.json \
  --anomalies /tmp/edge-auto/anomalies.json \
  --llm-ideas-cmd "python3 /path/to/llm_ideas_cli.py" \
  --output /tmp/edge-hints/hints.yaml
仅基于规则：
bash
python3 skills/edge-hint-extractor/scripts/build_hints.py \
  --market-summary /tmp/edge-auto/market_summary.json \
  --anomalies /tmp/edge-auto/anomalies.json \
  --news-reactions /tmp/news_reactions.csv \
  --as-of 2026-02-20 \
  --output /tmp/edge-hints/hints.yaml
规则 + LLM扩充：
bash
python3 skills/edge-hint-extractor/scripts/build_hints.py \
  --market-summary /tmp/edge-auto/market_summary.json \
  --anomalies /tmp/edge-auto/anomalies.json \
  --llm-ideas-cmd "python3 /path/to/llm_ideas_cli.py" \
  --output /tmp/edge-hints/hints.yaml

Resources

资源

  • skills/edge-hint-extractor/scripts/build_hints.py
  • skills/edge-hint-extractor/references/hints_schema.md
  • skills/edge-hint-extractor/scripts/build_hints.py
  • skills/edge-hint-extractor/references/hints_schema.md