Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-hint-extractorPrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
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.yamlinput for concept synthesis or auto detection.
Prerequisites
- Python 3.9+
PyYAML- Optional inputs from detector run:
market_summary.jsonanomalies.jsonnews_reactions.csvornews_reactions.json
Output
hints.yamlcontaining:hintslist- generation metadata
- rule/LLM hint counts
Workflow
- Gather observation files (
market_summary,anomalies, optional news reactions). - Run
scripts/build_hints.pyto generate deterministic hints. - 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).
- Pass
hints.yamlinto 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.pyreferences/hints_schema.md
