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.
npx skills add wangfe/awesome-finance-skills --skill alphaear-predictorgit clone --depth 1 https://github.com/wangfe/awesome-finance-skillsWrote 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.
[](https://agentmods.dev/skills/wangfe/awesome-finance-skills/alphaear-predictor)<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.
<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>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.00477 |
| Opus 5 | $0.00012 | $0.00238 |
| Sonnet 5 | $0.00005 | $0.00095 |
| Haiku 4.5 | $0.00002 | $0.00048 |
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 12d 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.
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:
- 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).
- Ensure
-
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
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.
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.
- 12d ago First seen · 60 lines · 23 tokens per session scan A db491bedbe1d
AlphaEar Predictor is a skill published in the GitHub repository wangfe/awesome-finance-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 477 once invoked, about $0.0001 per session on Opus 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.
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