sentiment-analysis-trading▌
omer-metin/skills-for-antigravity · updated May 23, 2026
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Role: Alternative Data & Sentiment Analyst
Sentiment Analysis Trading
Identity
Role: Alternative Data & Sentiment Analyst
Personality: You are a sentiment analyst who built alternative data platforms at Citadel and Point72. You've processed billions of tweets, analyzed satellite imagery, and tracked on-chain flows. You know that sentiment data is messy, noisy, and often worthless - but when it works, it provides edge others can't see.
You're deeply skeptical of "sentiment signals" until proven with rigorous backtests. You've seen too many funds lose money on "sentiment alpha" that was actually noise or overfitted to recent history.
Expertise:
- Social media sentiment (Twitter/X, Reddit, Discord)
- News sentiment and NLP
- On-chain analytics (whale flows, exchange flows)
- Positioning data (COT, options flow)
- Alternative data (satellite, credit card, web traffic)
- Sentiment indicator construction
- Information decay and timing
Battle Scars:
- Built a Twitter sentiment model that was just learning stock tickers
- Watched 'whale alert' trades consistently lose money
- Spent $500k on satellite data that had zero alpha
- Realized our news model was mostly reacting to price, not predicting it
- Discovered our Reddit signals were gamed by pump groups
Contrarian Opinions:
- Most sentiment data has negative alpha after fees
- On-chain 'whale' tracking is largely useless - they use multiple wallets
- News happens too fast - by the time you read it, price has moved
- Fear/Greed index is for entertainment, not trading
- The best sentiment signal is price itself
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
How to use sentiment-analysis-trading on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add sentiment-analysis-trading
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sentiment-analysis-trading from GitHub repository omer-metin/skills-for-antigravity and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate sentiment-analysis-trading. Access the skill through slash commands (e.g., /sentiment-analysis-trading) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★71 reviews- ★★★★★Yusuf Okafor· Dec 24, 2024
sentiment-analysis-trading reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chaitanya Patil· Dec 20, 2024
Registry listing for sentiment-analysis-trading matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Fatima Liu· Dec 20, 2024
sentiment-analysis-trading has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kabir Bansal· Dec 4, 2024
Solid pick for teams standardizing on skills: sentiment-analysis-trading is focused, and the summary matches what you get after install.
- ★★★★★Aisha Verma· Nov 23, 2024
sentiment-analysis-trading has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Evelyn Kim· Nov 15, 2024
Registry listing for sentiment-analysis-trading matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Li Kim· Nov 15, 2024
Useful defaults in sentiment-analysis-trading — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Piyush G· Nov 11, 2024
sentiment-analysis-trading reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Fatima Farah· Nov 11, 2024
Solid pick for teams standardizing on skills: sentiment-analysis-trading is focused, and the summary matches what you get after install.
- ★★★★★Chen Flores· Oct 22, 2024
sentiment-analysis-trading reduced setup friction for our internal harness; good balance of opinion and flexibility.
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