Track Stock Buzz Across Reddit, X, News, and Polymarket with finance-sentiment

Where this fits

Imagine a user asks: "Is NVDA still hot on X.com?" or "Compare sentiment on AMD vs NVDA across Reddit and news." Without a dedicated skill, your agent would need to scrape multiple platforms, parse unstructured text, and normalize signals—hours of work. The finance-sentiment skill collapses that into a single API call, returning structured sentiment scores, mention counts, and Polymarket bet volumes. It's a research layer that turns noisy social chatter into actionable data points.

Why agents benefit

• Instant cross-source aggregation: Fetches sentiment from Reddit, X.com, news, and Polymarket in one request, eliminating the need for multiple scrapers.
• Normalized metrics: Returns bullish percentage, buzz score, and mention counts, so agents can compare tickers or sources without building custom parsers.
• Alignment detection: Answers questions like "Is Reddit aligned with X on META?" by comparing sentiment direction across platforms.
• Read-only safety: No trade execution or state modification—perfect for research-only agent workflows.
• Trigger-aware: Recognizes natural language cues like "how hot is" or "stock buzz," making integration seamless.

Practical scenarios

Pre-trade research

A user asks: "Should I look into TSLA? How's the sentiment on Reddit and news?" The agent calls the skill, gets a bullish percentage and mention count, and presents a concise summary without exposing raw posts.

Competitor comparison

"Compare sentiment on AMD vs NVDA across all sources." The skill returns side-by-side metrics, letting the agent highlight which ticker has stronger social momentum and where the divergence lies.

Polymarket pulse check

"How many Polymarket bets on Microsoft?" The skill retrieves the number of active bets and the implied probability, giving the agent a quick read on market expectations.

Add it to your agent workflow

Install the skill via npx:

npx skills add himself65/finance-skills --skill finance-sentiment

Then invoke it with a natural language trigger. Example agent call:

User: "What's the social sentiment on TSLA?"
Agent: [calls finance-sentiment with ticker=TSLA]
Agent: "TSLA has 2,340 Reddit mentions (62% bullish), 1,890 X posts (58% bullish), and 1,200 Polymarket bets with a 54% chance of price increase."

For a deeper dive into the skill's schema and examples, check the OpenAgentSkill listing.

Compare before adopting

Before adding this skill, evaluate:

• Data freshness: Does the Adanos API update in real-time or with delay? Test with a known volatile ticker.
• Coverage: Does it handle all major exchanges and tickers? Some APIs miss small caps.
• Alternatives: Compare with other sentiment APIs (e.g., StockTwits, Yahoo Finance) for accuracy and cost.
• Workflow fit: Ensure the skill's output schema aligns with your agent's existing data models.

Why it is worth tracking

With 3,278 stars and an active author, this skill shows strong community adoption. Its read-only design and multi-source aggregation make it a valuable addition to any research-oriented agent. Track it now to see if it becomes a standard in your toolkit—especially if your users frequently ask about stock buzz or cross-source sentiment alignment.