Awesome Finance Skills is a collection of add-ons that give AI agents tools for financial news, market data, sentiment analysis, forecasting, investment signals, and market-impact diagrams. It is for people who want agents to analyze stocks and financial events.
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 RKiding/Awesome-finance-skills --skill alphaear-sentimentgit clone --depth 1 https://github.com/RKiding/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/rkiding/awesome-finance-skills/alphaear-sentiment)<a href="https://agentmods.dev/skills/rkiding/awesome-finance-skills/alphaear-sentiment"><img src="https://agentmods.dev/badge/skills/rkiding/awesome-finance-skills/alphaear-sentiment/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/rkiding/awesome-finance-skills/alphaear-sentiment"><img src="https://agentmods.dev/badge/skills/rkiding/awesome-finance-skills/alphaear-sentiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00041 | $0.00522 |
| Opus 5 | $0.00020 | $0.00261 |
| Sonnet 5 | $0.00008 | $0.00104 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
alphaear-sentiment 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 13d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- alphaear-sentiment — 100% identical, 0 lines differ
- AlphaEar Sentiment — 91% identical, 8 lines differ
What it actually says
AlphaEar Sentiment Skill
Overview
This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
Capabilities
Capabilities
1. Analyze Sentiment (FinBERT / Local)
Use scripts/sentiment_tools.py for high-speed, local sentiment analysis using FinBERT.
Key Methods:
analyze_sentiment(text): Get sentiment score and label using localized FinBERT model.- Returns:
{'score': float, 'label': str, 'reason': str}. - Score Range: -1.0 (Negative) to 1.0 (Positive).
- Returns:
batch_update_news_sentiment(source, limit): Batch process unanalyzed news in the database (FinBERT only).
2. Analyze Sentiment (LLM / Agentic)
For higher accuracy or reasoning capabilities, YOU (the Agent) should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary.
Sentiment Analysis Prompt
Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required.
请分析以下金融/新闻文本的情绪极性。
返回严格的 JSON 格式:
{"score": <float: -1.0到1.0>, "label": "<positive/negative/neutral>", "reason": "<简短理由>"}
文本: {text}
Scoring Guide:
- Positive (0.1 to 1.0): Optimistic news, profit growth, policy support, etc.
- Negative (-1.0 to -0.1): Losses, sanctions, price drops, pessimism.
- Neutral (-0.1 to 0.1): Factual reporting, sideways movement, ambiguous impact.
Helper Methods
update_single_news_sentiment(id, score, reason): Use this to save your manual analysis to the database.
Dependencies
torch(for FinBERT)transformers(for FinBERT)sqlite3(built-in)
Ensure DatabaseManager is initialized correctly.
What ships with it
7 files 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.
- 13d ago First seen · 58 lines · 41 tokens per session scan A 81147eefc1e4
alphaear-sentiment is a skill published in the GitHub repository RKiding/Awesome-finance-skills (3,002 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 522 once invoked, about $0.0002 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 2026-08-30.
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