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.
What this skill does
# 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`
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