Edge Hint Extractor

Overview

market_summary

anomalies

news reactions

observe -> abstract -> design -> pipeline

When to Use

• 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.

Prerequisites

• Python 3.9+

• PyYAML

• Optional inputs from detector run:

  • market_summary.json

  • anomalies.json

  • news_reactions.csv

    or

    news_reactions.json

Output

• hints.yaml

  containing:

  • hints

    list
  • generation metadata
  • rule/LLM hint counts

Workflow

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.

Quick Commands

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

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

• skills/edge-hint-extractor/scripts/build_hints.py

• skills/edge-hint-extractor/references/hints_schema.md