---
name: edge-hint-extractor
description: Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection.
---

# 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:

```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
```

## Resources

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