How do I install the Market Sentiment skill?

Run npx skillmds@latest add kukapay/market-sentiment in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Market Sentiment safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Market Sentiment?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Market Sentiment free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Market Sentiment?

kukapay (@kukapay) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Market Sentiment by kukapay — Agent Skill

Name: Market Sentiment
Rating: 4.2 (5 reviews)
Author: kukapay

Market Sentiment

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.

kukapay Updated Sep 10, 2026

File contents

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:

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

---name: market-sentimentdescription: 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.---# Crypto Market Sentiment## OverviewThis 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.## WorkflowFollow 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 FormatThe 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.

Frequently asked questions