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edge-hint-extractor

edge-hint-extractor

github.com/mphinanceData & databases3284 installsUpdated 5d ago
Repository

About edge-hint-extractor

A skill that turns market summaries, anomalies, and optional news reactions into structured edge hints. It supports deterministic rule-based generation and optional LLM-provided ideas, producing hints.yaml for downstream concept synthesis or auto detection.

Key features

  1. 01Build hints from market observations
  2. 02Use anomaly and news reaction inputs
  3. 03Generate deterministic rule-based hints
  4. 04Optionally add LLM ideas
  5. 05Write hints.yaml with metadata

Use cases

  1. 01Turning daily observations into reusable hints
  2. 02Grounding LLM ideas in market anomalies
  3. 03Preparing inputs for concept synthesis
  4. 04Providing hints to auto detection

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mphinance/alpha-skills --skill edge-hint-extractor

After installing, type/edge-hint-extractorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

README.md
agents/
openai.yaml
references/
hints_schema.md
scripts/
build_hints.py
scripts/tests/
conftest.py
test_build_hints.py

Details

From the project README

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

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