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-reportergit 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-reporter)<a href="https://agentmods.dev/skills/rkiding/awesome-finance-skills/alphaear-reporter"><img src="https://agentmods.dev/badge/skills/rkiding/awesome-finance-skills/alphaear-reporter/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-reporter"><img src="https://agentmods.dev/badge/skills/rkiding/awesome-finance-skills/alphaear-reporter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00031 | $0.00241 |
| Opus 5 | $0.00015 | $0.00120 |
| Sonnet 5 | $0.00006 | $0.00048 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
alphaear-reporter 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
1 near-identical copy found in the catalogue:
- alphaear-reporter — 100% identical, 0 lines differ
What it actually says
AlphaEar Reporter Skill
Overview
This skill provides a structured workflow for generating professional financial reports. It includes planning, writing, editing, and creating visual aids (charts).
Capabilities
Capabilities
1. Generate Structured Reports (Agentic Workflow)
YOU (the Agent) are the Report Generator. Use the prompts in references/PROMPTS.md to progressively build the report.
Workflow:
- Cluster Signals: Read input signals and use the Cluster Signals Prompt to group them.
- Write Sections: For each cluster, use the Write Section Prompt to generate analysis.
- Assemble: Use the Final Assembly Prompt to compile the report.
2. Visualization Tools
Use scripts/visualizer.py to generate chart configurations if needed manually, though the Writer Prompt usually handles this via json-chart blocks.
Dependencies
sqlite3(built-in)
What ships with it
35 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.
- references/PROMPTS.md 2.2 KB
- scripts/__init__.py 0 B runs code
- scripts/prompts/fin_agent.py 6.6 KB runs code
- scripts/prompts/forecast_analyst.py 1.9 KB runs code
- scripts/prompts/intent_agent.py 2.2 KB runs code
- scripts/prompts/isq_prompt_generator.py 1.8 KB runs code
- scripts/prompts/report_agent.py 18 KB runs code
- scripts/prompts/trend_agent.py 7.4 KB runs code
- scripts/prompts/visualizer.py 2.0 KB runs code
- scripts/report_agent.py 6.7 KB runs code
- scripts/schema/isq_template.py 14 KB runs code
- scripts/schema/models.py 6.1 KB runs code
- scripts/tools/__init__.py 448 B runs code
- scripts/tools/toolkits.py 19 KB runs code
- scripts/utils/__init__.py 25 B runs code
- scripts/utils/content_extractor.py 4.7 KB runs code
- scripts/utils/database_manager.py 21 KB runs code
- scripts/utils/hybrid_search.py 8.0 KB runs code
- scripts/utils/json_utils.py 6.2 KB runs code
- scripts/utils/llm/capability.py 2.8 KB runs code
- scripts/utils/llm/factory.py 3.6 KB runs code
- scripts/utils/llm/router.py 2.8 KB runs code
- scripts/utils/logging_setup.py 1.0 KB runs code
- scripts/utils/news_tools.py 9.9 KB runs code
- scripts/utils/predictor/evaluation.py 5.6 KB runs code
- scripts/utils/predictor/kline_generate.py 7.6 KB runs code
- scripts/utils/predictor/model/__init__.py 394 B runs code
- scripts/utils/predictor/model/kronos.py 30 KB runs code
- scripts/utils/predictor/model/module.py 22 KB runs code
- scripts/utils/predictor/training.py 18 KB runs code
- scripts/utils/search_tools.py 29 KB runs code
- scripts/utils/sentiment_tools.py 9.9 KB runs code
- scripts/utils/stock_tools.py 9.8 KB runs code
- scripts/visualizer.py 19 KB runs code
- tests/test_reporter.py 830 B runs code
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 · 33 lines · 31 tokens per session scan A a249c53cfb07
alphaear-reporter 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 31 tokens to every session and 241 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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