This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionalphaear-sentimentExecute the skills CLI command in your project's root directory to begin installation:
Fetches alphaear-sentiment from rkiding/awesome-finance-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate alphaear-sentiment. Access via /alphaear-sentiment in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
1.6K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
1.6K
stars
This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
Use scripts/sentiment_tools.py for high-speed, local sentiment analysis using FinBERT.
Key Methods:
analyze_sentiment(text): Get sentiment score and label using localized FinBERT model.
{'score': float, 'label': str, 'reason': str}.batch_update_news_sentiment(source, limit): Batch process unanalyzed news in the database (FinBERT only).For higher accuracy or reasoning capabilities, YOU (the Agent) should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary.
Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required.
请分析以下金融/新闻文本的情绪极性。
返回严格的 JSON 格式:
{"score": <float: -1.0到1.0>, "label": "<positive/negative/neutral>", "reason": "<简短理由>"}
文本: {text}
Scoring Guide:
update_single_news_sentiment(id, score, reason): Use this to save your manual analysis to the database.torch (for FinBERT)transformers (for FinBERT)sqlite3 (built-in)Ensure DatabaseManager is initialized correctly.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Registry listing for alphaear-sentiment matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: alphaear-sentiment is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for alphaear-sentiment matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: alphaear-sentiment is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in alphaear-sentiment — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
alphaear-sentiment reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend alphaear-sentiment for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added alphaear-sentiment from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: alphaear-sentiment is the kind of skill you can hand to a new teammate without a long onboarding doc.
alphaear-sentiment fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
showing 1-10 of 35