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 agentmods add skills/skloxo/tidetrading/event-drivennpx skills add skloxo/TideTrading --skill event-drivengit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/event-driven)<a href="https://agentmods.dev/skills/skloxo/tidetrading/event-driven"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/event-driven.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.02086 |
| Opus 5 | $0.00019 | $0.01043 |
| Sonnet 5 | $0.00008 | $0.00417 |
| Haiku 4.5 | $0.00004 | $0.00209 |
Grade A, and why
event-driven 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 6d 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.
This is a copy
100% identical to event-driven — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Event-Driven Strategy
Purpose
Uses event information such as news, announcements, and macro policy updates. The LLM analyzes sentiment and impact magnitude to generate event-driven trading signals. Event data is managed in CSV format, and technical signals are combined with event signals through weighted aggregation to form the final trading decision.
Workflow
- Data collection: use the
read_urltool to fetch the full text of news and announcements - LLM analysis: the LLM reads the news and scores it from
-1.0to1.0with a standardized prompt (extremely bearish to extremely bullish) - Generate the event CSV: write data in the
date,event_type,score,source,summaryschema - Signal aggregation:
signal_engine.pyreads the event CSV, applies time decay, and combines it with the technical signal
Key principle: the event CSV is the data layer, and signal_engine.py is the logic layer. Keep them decoupled.
Event CSV Schema
date,event_type,score,source,summary
2024-01-15,earnings,0.8,read_url,Q4 revenue beat expectations by 30%
2024-01-20,macro,-0.5,read_url,Central bank raised rates by 25bp
2024-02-01,policy,0.3,read_url,New-energy subsidies extended
2024-02-10,sentiment,-0.7,read_url,Bearish sentiment surged on social media
2024-03-05,insider,0.4,read_url,CEO bought 5 million shares
Field descriptions:
| Field | Type | Description |
|---|---|---|
| date | str (YYYY-MM-DD) |
Date when the event became knowable (publication date, not occurrence date. If released after market close → use the next trading day) |
| event_type | str | earnings / macro / policy / sentiment / insider / technical_break |
| score | float | -1.0 ~ 1.0 (standardized LLM score) |
| source | str | Data-source tag (such as read_url) |
| summary | str | Event summary (one sentence, no commas) |
Event Type Details
| Type | Meaning | Typical Impact | Duration |
|---|---|---|---|
| earnings | Earnings release | Short-term shock | 1-5 days |
| macro | Macro data / central-bank policy | Medium-term impact | 5-20 days |
| policy | Industry policy / regulatory change | Long-term impact | 20-60 days |
| sentiment | Market sentiment / public opinion | Short-term shock | 1-3 days |
| insider | Insider trading / block trade | Medium-term signal | 5-10 days |
| technical_break | Break of a key technical level | Short-term catalyst | 1-5 days |
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.
- 6d ago First seen · 181 lines · 38 tokens per session scan A 169e5bfab7e8
event-driven is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 2,086 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to event-driven, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
daily-deep-brief
8 点这个时点：HK 开盘前 90 分钟，US 已收盘 4 小时。盘前是 deep think 最好的窗口 — 有完整夜间消息面，没有盘中执行压力。.
invest-analyst
全能证券分析师工作台——把零散金融工具串成机构级工作流。覆盖个股IC研报、主题策略、事件驱动（电话会/业绩/政策/并购）、一致预期整合、行业比较、市场日报六大场景。 触发：「出一份茅台的IC报告」「写一份XX的行业深度」「XX业绩电话会纪要」「怎么看待XX政策/并购」「这个产业链有哪些标的」「分析师一致预期」「今天市场怎么样」「出个日报晨报」「这个投资论点怎么写」。 与invest系列区别：invest-stock/fund做「买不买」的判断，invest-analyst做「怎么写/怎么产出」的机构级内容交付。invest-industry做「行业是什么」，invest-analyst做「这个行业怎么投」。.
invest-stock
Skill "invest-stock" from taxueseek/fund-investment-guide, covering invest-stock：统一个股分析, 模式自动选择, 快速识别, 模式 1：三关审查（默认，a股/港股） and 哲学锚点.
invest-cli
投资分析 CLI — 数据获取 + 分析框架一体化，dual-mode 运行。 触发：「/cli」「/投资cli」「invest cli」「终端分析」「命令行分析」 或：「用cli分析」「终端查一下」「命令行看看」 命令入口：invest-cli stock 600519（PATH 无 invest-cli 时兜底：python3 "$HOME/.agents/skills/invest-cli/scripts/investcli.py" stock 600519）.
invest-allocation
Skill "invest-allocation" from taxueseek/fund-investment-guide, covering invest-allocation：资产配置, 哲学锚点, 分析前提, 战略资产配置（saa） and 参考配置矩阵.
portfolio-swarm-review
Multi-agent swarm review of kcn's current holdings. Inspired by TauricResearch/TradingAgents framework already in workspace — three-tier analysis (analysts → bull/bear debate → risk debate + judge) with confidence scoring. Use for post-close reviews, holiday/next-session planning, pre-add sizing decisions, and any…