Skip to main content
skill CX Codex

edge-hint-extractor — Claude Code skill: market hints to YAML

Turns daily market observations and news reactions into structured edge hints, written out as hints.yaml.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a claude-code
Open on GitHub

What edge-hint-extractor does

The skill reads raw signals — market_summary, anomalies, news reactions — and produces hint objects from them. It sits first in the chain observe -> abstract -> design -> pipeline, so the resulting hints.yaml feeds concept synthesis or auto detection.

One script does the work: scripts/build_hints.py. Rule-based hints come out deterministic. Model-generated ideas arrive by either of two routes, and you pick one: --llm-ideas-cmd pipes data to an external LLM CLI as a subprocess, while --llm-ideas-file PATH loads hints you already wrote into a YAML file — the route meant for Claude, where the agent writes the hints itself. Passing both flags at once fails.

Python 3.9+ and PyYAML are required. Observation files are optional, though there is nothing to build without them: market_summary.json and anomalies.json come from a detector run, and news reactions live in news_reactions.csv or news_reactions.json. Output defaults to reports/edge_hint_extractor/hints.yaml; the schema sits in references/hints_schema.md.

What it helps with

  • turn daily market observations into hint objects
  • add model-written ideas on top of anomalies and news
  • produce a clean hints.yaml for concept synthesis
  • prepare input for auto detection
  • consolidate detector signals into one structured file

Example prompts for the agent

  • Build hints from market_summary.json and anomalies.json as of 2026-02-20 into reports/
  • Add your own ideas about the current anomalies and save hints.yaml
  • Run build_hints.py with --llm-ideas-file /tmp/llm_hints.yaml

How to install edge-hint-extractor

1Skills CLI (Vercel)

npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a claude-code

2Manually: put the files in place

~/.claude/skills/edge-hint-extractor/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/edge-hint-extractor/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about edge-hint-extractor

How do I install edge-hint-extractor in Claude Code?

Run npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor -a claude-code. The skill lands at ~/.claude/skills/edge-hint-extractor/SKILL.md.

What does edge-hint-extractor do?

It converts market observations and news reactions into structured edge hints and writes them to hints.yaml, along with generation metadata and rule/LLM hint counts.

How do --llm-ideas-cmd and --llm-ideas-file differ?

The first sends data to an external LLM CLI through a subprocess; the second reads hints you prepared in a YAML file. They are mutually exclusive.

Source text

SKILL.md · MIT
---
name: edge-hint-extractor
description: 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`):

```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`

© tradermonty · MIT · tradermonty/claude-trading-skills

Similar skills

Everything for Finance and business →

From the same set73

pair-trade-screener This skill screens stocks for cointegrated pairs inside a sector, computes spread z-scores and returns entry and exit signals. CX Codex +1 3,400installs finviz-screener Turns a plain-language request into a FinViz screener URL and opens it in Chrome. Handy when you need to filter stocks by fundamentals or technicals fast. CX Codex +1 3,400installs market-breadth-analyzer Quantifies market breadth health on a 0–100 scale using TraderMonty's public CSV data. Useful when deciding equity exposure based on market participation. CX Codex +1 3,500installs dual-axis-skill-reviewer Reviews skills with deterministic checks and an LLM axis, then combines both into one score. Use it to inspect quality before a merge or find concrete fixes. CX Codex +1 3,300installs edge-concept-synthesizer Abstracts raw detector tickets and hints into reusable edge concepts before drafting strategies. Helps prevent overfitting by grouping repeating market conditions. CX Codex +1 3,300installs trader-memory-core This skill keeps investment theses from a screener idea through a closed position with a postmortem. Useful when trade decisions get lost between chats. CX Codex +1 3,300installs pead-screener Screens gap-up stocks after earnings for PEAD follow-through, reading weekly candles for red-candle pullbacks and breakouts. Handy for post-report drift setups. CX Codex +1 3,300installs kanchi-dividend-us-tax-accounting Classifies US dividend streams for stocks, REITs, and BDCs while evaluating holding periods and placement across taxable and retirement accounts. CX Codex +1 3,300installs

Project sponsors

Companies that keep this analytics open
Gold sponsor

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor