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Alphaear Deepear Lite

  • 291 installs
  • 2.8k repo stars
  • Updated March 29, 2026
  • rkiding/awesome-finance-skills

alphaear-deepear-lite is a Claude Code skill that fetches real-time financial signals, confidence scores, and transmission-chain reasoning from the DeepEar Lite JSON API for developers who need lightweight market intelli

About

alphaear-deepear-lite is a Python-backed agent skill in the RKiding Awesome-finance-skills collection that calls DeepEarLiteTools.fetch_latest_signals() to pull high-frequency market signals from https://deepear.vercel.app/latest.json. The bundled deepear_lite.py script formats each signal with title, summary, sentiment_score, confidence, intensity, reasoning text, and source links into a markdown report agents can cite in investment memos. Dependencies are requests and loguru only, with no local database setup. Developers reach for alphaear-deepear-lite when an AI workflow needs immediate DeepEar Lite dashboard context for sector scans, earnings-factor research, or thesis drafting without wiring a custom scraper. Verify connectivity with python scripts/deepear_lite.py after installing via npx skills add RKiding/Awesome-finance-skills@alphaear-deepear-lite.

  • Earnings-focused financial research
  • Lightweight deep-earnings summaries
  • Alpha signal exploration
  • Finance workflow skill from awesome-finance-skills
  • Supports early investment due diligence

Alphaear Deepear Lite by the numbers

  • 291 all-time installs (skills.sh)
  • +11 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #336 of 1,106 Finance & Trading skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/rkiding/awesome-finance-skills --skill alphaear-deepear-lite

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Listed on Skillselion
Installs291
repo stars2.8k
Last updatedMarch 29, 2026
Repositoryrkiding/awesome-finance-skills

How do you fetch DeepEar Lite financial signals in Python?

Research earnings signals and lightweight deep-earnings context to inform investment theses, sector scans, and early financial due diligence.

Who is it for?

Developers building finance agents who need a zero-database DeepEar Lite signal feed for thesis research and sector scans.

Skip if: Developers who need full OHLCV stock data, FinBERT sentiment pipelines, or Kronos forecasting should use other AlphaEar skills instead.

When should I use this skill?

User asks for DeepEar Lite signals, earnings transmission chains, market confidence scores, or real-time financial reasoning from deepear.vercel.app

What you get

Formatted markdown signal report with titles, sentiment_score, confidence, intensity, reasoning, and source URLs

  • Markdown DeepEar Lite signal report
  • Per-signal sentiment/confidence/intensity metrics
  • Source name and URL citations

By the numbers

  • Bundles 1 Python script (deepear_lite.py) exposing DeepEarLiteTools.fetch_latest_signals()
  • Uses a 10-second HTTP timeout when requesting deepear.vercel.app/latest.json
  • Reports 3 numeric metrics per signal: sentiment_score, confidence, and intensity

Files

SKILL.mdMarkdownGitHub ↗

DeepEar Lite Skill

Overview

Fetch high-frequency financial signals, including titles, summaries, confidence scores, and reasoning directly from the DeepEar Lite platform's real-time data source.

Capabilities

1. Fetch Latest Financial Signals

Use scripts/deepear_lite.py via DeepEarLiteTools.

  • Fetch Signals: fetch_latest_signals()
  • Retrieves all latest signals from https://deepear.vercel.app/latest.json.
  • Returns a formatted report of signal titles, sentiment/confidence metrics, summaries, and source links.

Dependencies

  • requests, loguru
  • No local database required for this skill.

Testing

Run the test script to verify the connection and data fetching:

python scripts/deepear_lite.py

Related skills

How it compares

Pick alphaear-deepear-lite for a lightweight live signal JSON feed; choose sibling AlphaEar skills when you need stock OHLCV, FinBERT sentiment, or Kronos forecasting.

FAQ

What API does alphaear-deepear-lite call?

alphaear-deepear-lite calls https://deepear.vercel.app/latest.json through DeepEarLiteTools.fetch_latest_signals() in scripts/deepear_lite.py, returning signal titles, summaries, sentiment_score, confidence, intensity, reasoning, and source links.

How do you test alphaear-deepear-lite after install?

alphaear-deepear-lite ships a standalone test entry point: run python scripts/deepear_lite.py from the skill directory to print the latest formatted DeepEar Lite signal report and confirm network access.

What dependencies does alphaear-deepear-lite require?

alphaear-deepear-lite depends on requests and loguru only. The skill needs no local database or extra services beyond outbound HTTP access to the DeepEar Lite JSON endpoint.

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