This skill utilizes the Kronos model (via KronosPredictorUtility) to perform time-series forecasting and adjust predictions based on news sentiment.
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
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
Submit your Claude Code skill and start earning
epx add rkiding/awesome-finance-skills --skill alphaear-predictorCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionalphaear-predictorExecute the skills CLI command in your project's root directory to begin installation:
epx add rkiding/awesome-finance-skills --skill alphaear-predictorFetches alphaear-predictor 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:
.cursor/skills/alphaear-predictor
Restart Cursor to activate alphaear-predictor. Access via /alphaear-predictorin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
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.
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We added alphaear-predictor from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
alphaear-predictor reduced setup friction for our internal harness; good balance of opinion and flexibility.
alphaear-predictor has been reliable in day-to-day use. Documentation quality is above average for community skills.
alphaear-predictor has been reliable in day-to-day use. Documentation quality is above average for community skills.
alphaear-predictor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for alphaear-predictor matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: alphaear-predictor is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: alphaear-predictor is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for alphaear-predictor matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in alphaear-predictor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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