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AlphaEar Predictor

AlphaEar Predictor is a skill for Claude Code, Codex from wangfe/awesome-finance-skills. It costs 23 tokens per session (477 once invoked), scanned A, original, MIT.

A market-forecasting skill that predicts future open, high, low, and close prices from time-series data, then adjusts the forecast using news sentiment. It uses the Kronos prediction model and requires model files and an embedding model.

In plain words
What is it for?
Use it to forecast market trends for a chosen asset and time horizon, such as a seven-day prediction. It is intended for OHLC price forecasting with news-aware adjustments.
Why use it?
It combines numerical price history with relevant news so forecasts are not based on market data alone.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 5repo 6mo ago A scan 23 tokens original MIT

Good fit Use it to forecast market trends for a chosen asset and time horizon, such as a seven-day prediction. It is intended for OHLC price forecasting with news-aware adjustments.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wangfe/awesome-finance-skills/alphaear-predictor
View source ↗ wangfe/awesome-finance-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add wangfe/awesome-finance-skills --skill alphaear-predictor
Clone the repo
git clone --depth 1 https://github.com/wangfe/awesome-finance-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for AlphaEar Predictor

README.md
[![agentmods](https://agentmods.dev/badge/skills/wangfe/awesome-finance-skills/alphaear-predictor/github.svg)](https://agentmods.dev/skills/wangfe/awesome-finance-skills/alphaear-predictor)
Your own site
<a href="https://agentmods.dev/skills/wangfe/awesome-finance-skills/alphaear-predictor"><img src="https://agentmods.dev/badge/skills/wangfe/awesome-finance-skills/alphaear-predictor/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for AlphaEar Predictor

Your own site · 80×15
<a href="https://agentmods.dev/skills/wangfe/awesome-finance-skills/alphaear-predictor"><img src="https://agentmods.dev/badge/skills/wangfe/awesome-finance-skills/alphaear-predictor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 477 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.00477
Opus 5.5 $0.00009 $0.00191
Sonnet 5.5 $0.00005 $0.00095
Haiku 4.5 $0.00002 $0.00048

Measured 2mo ago against content hash db491bedbe1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

AlphaEar Predictor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/financial-modeling/forecasting/alphaear-predictor/SKILL.md · 60 lines

What it actually says

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

Workflow:

  1. Generate Base Forecast: Use scripts/kronos_predictor.py (via KronosPredictorUtility) to generate the technical/quantitative forecast.
  2. Adjust Forecast (Agentic): Use the Forecast Adjustment Prompt in 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)

Configuration

This skill requires the Kronos model and an embedding model.

  1. Kronos Model:

    • Ensure exports/models directory exists in the project root.
    • Place trained news projector weights (e.g., kronos_news_v1.pt) in exports/models/.
    • Or depend on the base model (automatically downloaded).

  2. 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

  • torch
  • transformers
  • sentence-transformers
  • pandas
  • numpy
  • scikit-learn
Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 60 lines · 23 tokens per session scan A db491bedbe1d

Subscribe to this mod's changes

AlphaEar Predictor is a skill published in the GitHub repository wangfe/awesome-finance-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 23 tokens to every session and 477 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsFinance and fintechfinancial-modeling/forecastingforecastingkronosquantitativetime-series
Related

AlphaEar Predictor compared with skills from other repositories

AlphaEar Predictor next to 6 skills from other repositories
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