Edge Hint Extractor

Overview

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.

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

Rule-based only (default output to reports/edge_hint_extractor/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 \
  --news-reactions /tmp/news_reactions.csv \
  --as-of 2026-02-20 \
  --output-dir reports/

Rule + LLM augmentation:

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-dir reports/

Resources

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