This skill utilizes the Kronos model (via KronosPredictorUtility) to perform time-series forecasting and adjust predictions based on news sentiment.
Works with
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:
Fetches 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:
Restart Cursor to activate alphaear-predictor. Access via /alphaear-predictor 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.
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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
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This skill utilizes the Kronos model (via KronosPredictorUtility) to perform time-series forecasting and adjust predictions based on news sentiment.
Workflow:
scripts/kronos_predictor.py (via KronosPredictorUtility) to generate the technical/quantitative forecast.references/PROMPTS.md to subjectively adjust the numbers based on latest news/logic.Key Tools:
KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text): Returns List[KLinePoint].Example Usage (Python):
from scripts.utils.kronos_predictor import KronosPredictorUtility
from scripts.utils.database_manager import DatabaseManager
db = DatabaseManager()
predictor = KronosPredictorUtility()
# Forecast
forecast = predictor.predict("600519", horizon="7d")
print(forecast)
This skill requires the Kronos model and an embedding model.
exports/models directory exists in the project root.kronos_news_v1.pt) in exports/models/.[!CAUTION] Model Security: This skill loads model weights from
exports/models. We useweights_only=Trueand only scan for thekronos_news_*.ptpattern. Ensure you only place trusted checkpoints in this directory.
EMBEDDING_MODEL: Path or name of the embedding model (default: sentence-transformers/all-MiniLM-L6-v2).KRONOS_MODEL_PATH: Optional path to override model loading.torchtransformerssentence-transformerspandasnumpyscikit-learnMake 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
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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