Edge Hint Extractor

SkillCommerce & finance

This skill turns your AI's daily market observations and news reactions into structured edge hints. The hints are written to a standard hints.yaml file that downstream concept synthesis and auto detection steps can build on. Your AI can also optionally use its own reasoning to brainstorm additional hints beyond what it extracts.

Available today. Use it from your connected AI after setup.

No other account needed.

After adding it, share your daily market observations or news reactions and ask your AI to extract hints. It will produce a hints.yaml file you can pass along to your concept synthesis and auto detection steps.

Then ask your AI: use the Edge Hint Extractor skill

What your AI can do with it

Extract edge hints from daily market observations
Turn news reactions into structured hints
Brainstorm extra hint ideas using its own reasoning when you ask for more than extraction
Write hints to a canonical hints.yaml file
Prepare hints for downstream concept synthesis and auto detection

What this skill tells your AI

The instructions your AI receives, as published by baggat236/ai-trading-skills in skills/edge-hint-extractor/SKILL.md and read by ahel’s review.

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

Gather observation files (market_summary, anomalies, optional news reactions).
Run scripts/build_hints.py to 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.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

Signals

GitHub stars: 122
Forks: 960
Last commit: Sep 2026

Advanced

Catalog kind: skill
Gateway key: edge-hint-extractor
Source: github.com/baggat236/ai-trading-skills

github.com/baggat236/ai-trading-skills

More in Commerce & finance

agent-payment-x402
Skill · affaan-m customer-billing-ops
Skill · affaan-m finance-billing-ops
Skill · affaan-m disclaimers
Skill · thedaviddias Braintree Automation
Skill · composiohq post-exploitation
Skill · ed1s0nz