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/edge-hint-extractor

@78742c1

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.

Use this Skill: https://skilld.dev/gh/tradermonty/claude-trading-skills/edge-hint-extractor

This session only. Nothing lands on disk.

SKILL.md

≈51 tokens always: the name and description. ≈606 when used: this file. ≈489 more on demand in 2 files.

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

Source: SKILL.md on GitHub

2 warnings16d5 checks · Risk MEDIUM
  • Gen Agent Trust Hub16d

    The skill allows execution of external commands provided via a CLI argument, which can be risky if the agent is tricked into running malicious code. It also processes external market and news data, which could contain instructions intended to influence the agent or downstream tools.

  • Socket16d

    2 alerts: gptAnomaly

  • Snyk16d

    Risk: LOW · No issues

  • Runlayer7mo

    2/6 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 78742c1. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 7 months ago

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