
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-liteAdd your badge
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| Installs | 291 |
|---|---|
| repo stars | ★ 2.8k |
| Last updated | March 29, 2026 |
| Repository | rkiding/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
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.pyimport requests
from loguru import logger
class DeepEarLiteTools:
"""
Tools for fetching signals from DeepEar Lite (https://deepear.vercel.app/lite).
"""
LATEST_JSON_URL = "https://deepear.vercel.app/latest.json"
def fetch_latest_signals(self):
"""
Fetch the newest financial signals from DeepEar Lite.
Returns a formatted summary of the latest signals.
"""
try:
logger.info(f"Fetching data from {self.LATEST_JSON_URL}")
headers = {
"User-Agent": "DeepEar-Skill-Agent/1.0 (Awesome-Finance-Skills)",
"Referer": "https://deepear.vercel.app/lite"
}
response = requests.get(self.LATEST_JSON_URL, headers=headers, timeout=10)
response.raise_for_status()
data = response.json()
generated_at = data.get("generated_at", "Unknown")
signals = data.get("signals", [])
if not signals:
return "No signals found in the latest data."
report = [f"### DeepEar Lite Signal Report (Updated: {generated_at})\n"]
for i, signal in enumerate(signals, 1):
title = signal.get("title", "No Title")
summary = signal.get("summary", "No Summary")
sentiment = signal.get("sentiment_score", 0)
confidence = signal.get("confidence", 0)
intensity = signal.get("intensity", 0)
reasoning = signal.get("reasoning", "No Reasoning")
report.append(f"#### {i}. {title}")
report.append(f"**Sentiment**: {sentiment} | **Confidence**: {confidence} | **Intensity**: {intensity}")
report.append(f"\n**Summary**: {summary}")
report.append(f"\n**Reasoning**: {reasoning}")
# Check for sources/links
sources = signal.get("sources", [])
if sources:
report.append("\n**Sources**:")
for src in sources:
name = src.get("name", "Link")
url = src.get("url", "#")
report.append(f"- [{name}]({url})")
report.append("\n" + "-"*40 + "\n")
return "\n".join(report)
except Exception as e:
error_msg = f"Error fetching DeepEar Lite data: {str(e)}"
logger.error(error_msg)
return error_msg
if __name__ == "__main__":
tools = DeepEarLiteTools()
print(tools.fetch_latest_signals())
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.