edge-hint-extractor

Files (7)

• README.md
• SKILL.md
• openai.yaml · agents/
• hints_schema.md · references/
• build_hints.py · scripts/
• conftest.py · scripts/tests/
• test_build_hints.py · scripts/tests/

SKILL.md (first lines)

# 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`

From mphinance/alpha-skills, MIT.

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