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market-sentiment

Skill36 starsChanged 10 months agoMITView on GitHub

Aggregate news from popular cryptocurrency RSS feeds, analyze sentiment of articles, and calculate an overall market sentiment score with detailed explanation. Use when assessing crypto market sentiment for trading decisions, research, or monitoring trends from RSS sources.

4 other projects use a version of this.

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  • Other versions4 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 4

    1. nirholas/cryptocurrency.cv★ 333same name
    2. nirholas/free-crypto-news★ 333same name
    3. ianalloway/openclaw-skills★ 4same name
    4. wwumit/skills-stock★ 0same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository5 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Follow these steps to analyze crypto market sentiment:

    line 14

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Crypto Market Sentiment

Overview

This skill enables aggregation of news from popular cryptocurrency RSS feeds, performs sentiment analysis on the articles, and computes a market sentiment score ranging from -1 (highly negative) to +1 (highly positive), along with evidence-based explanations.

Workflow

Follow these steps to analyze crypto market sentiment:

  1. Select RSS Feeds: Choose popular crypto RSS feeds (see references/rss_feeds.md for a curated list).
  2. Fetch News: Retrieve recent articles from the selected feeds.
  3. Analyze Sentiment: Classify each article's sentiment as positive (+1), negative (-1), or neutral (0) based on content keywords and context.
  4. Calculate Score: Compute the average sentiment score across all articles.
  5. Generate Explanation: Provide evidence from the news items supporting the score.

Sentiment Classification Guidelines

  • Positive (+1): News about adoption, launches, partnerships, ETF approvals, price rallies, regulatory wins, or technological breakthroughs.
  • Negative (-1): News about hacks, crashes, regulatory crackdowns, liquidations, delays, or criticisms.
  • Neutral (0): Factual updates, mixed outcomes, or speculative content without clear bias.

Output Format

The skill outputs:

  • Sentiment Score: Numerical value between -1 and 1.
  • Explanation: Breakdown by feed/source, key positive/negative drivers, and overall market implications.

Resources

scripts/

  • sentiment_analyzer.py: Python script to fetch RSS feeds, parse articles, and compute sentiment score. Run with python sentiment_analyzer.py to get automated results.

references/

  • rss_feeds.md: List of popular crypto RSS feeds with URLs and descriptions.
  • sentiment_examples.md: Examples of sentiment classification for common news types.

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