Alphaear Predictor
Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
Install
$ agentstack add skill-rkiding-awesome-finance-skills-alphaear-predictor ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-rkiding-awesome-finance-skills-alphaear-predictor) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AlphaEar Predictor Skill
Overview
This skill utilizes the Kronos model (via KronosPredictorUtility) to perform time-series forecasting and adjust predictions based on news sentiment.
Capabilities
1. Forecast Market Trends
1. Forecast Market Trends
Workflow:
- Generate Base Forecast: Use
scripts/kronos_predictor.py(viaKronosPredictorUtility) to generate the technical/quantitative forecast. - Adjust Forecast (Agentic): Use the Forecast Adjustment Prompt in
references/PROMPTS.mdto subjectively adjust the numbers based on latest news/logic.
Key Tools:
KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text): ReturnsList[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)Configuration
This skill requires the Kronos model and an embedding model.
- Kronos Model:
- Ensure
exports/modelsdirectory exists in the project root. - Place trained news projector weights (e.g.,
kronos_news_v1.pt) inexports/models/. - Or depend on the base model (automatically downloaded).
> [!CAUTION] > Model Security: This skill loads model weights from exports/models. We use weights_only=True and only scan for the kronos_news_*.pt pattern. Ensure you only place trusted checkpoints in this directory.
- Environment Variables:
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.
Dependencies
torchtransformerssentence-transformerspandasnumpyscikit-learn
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: RKiding
- Source: RKiding/Awesome-finance-skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.