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 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/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/skloxo/tidetrading/event-driven"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/event-driven.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 | $0.00038 | $0.02086 |
| Opus 5.5 | $0.00015 | $0.00834 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo ago First seen · 181 lines · 38 tokens per session scan A 169e5bfab7e8
event-driven is a skill published in the GitHub repository skloxo/TideTrading (13 stars, last pushed 8d ago), licensed MIT. It adds 38 tokens to every session and 2,086 once invoked, about $0.0002 per session on Opus 5.5. 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.
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