edge-hint-extractorlisted

# 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/scr