# Edge Hint Extractor

> 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.

- Skill: `jadegate/edge-hint-extractor` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jadegate/edge-hint-extractor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jadegate/edge-hint-extractor/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jadegate (https://skillmd.com/u/jadegate)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jadegate/edge-hint-extractor

---


# 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 augment hints with LLM ideas via one of two methods:
   - a. `--llm-ideas-cmd` — pipe data to an external LLM CLI (subprocess).
   - b. `--llm-ideas-file PATH` — load pre-written hints from a YAML file (for Claude Code workflows where Claude generates hints itself).
4. Pass `hints.yaml` into concept synthesis or auto detection.

Note: `--llm-ideas-cmd` and `--llm-ideas-file` are mutually exclusive.

## Quick Commands

Rule-based only (default output to `reports/edge_hint_extractor/hints.yaml`):

```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-dir reports/
```

Rule + LLM augmentation (external CLI):

```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-dir reports/
```

Rule + LLM augmentation (pre-written file, for Claude Code):

```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-file /tmp/llm_hints.yaml \
  --output-dir reports/
```

## Resources

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