← LeaderboardThe Skill Leaderboard

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.

Published by tradermonty ↗tradermonty/claude-trading-skills

Try your first task

edge-hint-extractor

Start a fresh conversation. Replace the highlighted placeholders.

Use edge-hint-extractor to turn [market observation files] into canonical hints.yaml for downstream concept synthesis, using optional news reactions when provided.

Required inputs

  • [market observation files]

User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗

SKILL.md

name: edge-hint-extractordescription: 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):

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):

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):

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