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 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 ): Rule + LLM augmentation (external CLI): Rule + LLM augmentation (pre written file, for Claude Code): Resources skills/edge hint extractor/scripts/build hints.py references/hints schema.md

edge-hint-extractor