Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
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 HKUDS/Vibe-Trading --skill market-microstructuregit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote 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/hkuds/vibe-trading/market-microstructure)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/market-microstructure"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/market-microstructure/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/hkuds/vibe-trading/market-microstructure"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/market-microstructure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00055 | $0.03257 |
| Opus 5 | $0.00028 | $0.01629 |
| Sonnet 5 | $0.00011 | $0.00651 |
| Haiku 4.5 | $0.00006 | $0.00326 |
Grade A, and why
market-microstructure 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 11d 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:
- market-microstructure — 94% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Microstructure
Overview
Study the micro-level mechanisms of price formation: who is trading, how they are trading, and how trades affect prices. For quantitative strategies, this matters because it improves transaction-cost estimation, identifies informed trading, and optimizes execution.
Applicable scenarios:
- Precise estimation of strategy trading costs (instead of simply assuming a flat 0.1% fee)
- Designing large-order execution strategies (
TWAP / VWAP / IS) - Detecting order-flow toxicity (avoid time windows dominated by informed traders)
- Quantifying liquidity risk (flash-crash warning)
- Capturing China A-share-specific microstructure features (call auction / closing auction / block trades)
Core Concepts
Bid-Ask Spread
Three measurements:
| Metric | Formula | Meaning |
|---|---|---|
| Quoted spread | Ask - Bid |
Best spread shown in the limit order book |
| Effective spread | `2 × | trade price - mid price |
| Realized spread | 2 × direction × (trade price - mid price 5min later) |
True market-maker profit |
China A-share example:
Instrument: 600519.SH Kweichow Moutai
Best bid: 1680.00 Best ask: 1680.50
Quoted spread: 0.50 RMB = 0.03%
Instrument: 000001.SZ Ping An Bank
Best bid: 11.05 Best ask: 11.06
Quoted spread: 0.01 RMB = 0.09%
Spread decomposition (Roll):
Spread = adverse-selection cost + inventory cost + order-processing cost
In China A-shares: adverse selection accounts for 60-70% (mixture of retail and informed traders)
Spread drivers:
- Larger market cap -> smaller spread (Moutai 0.03% vs small-cap 0.5%)
- Higher volatility -> wider spread (market-maker risk premium)
- Higher volume -> narrower spread (greater competition)
- Higher information asymmetry -> wider spread (adverse selection)
Order-Flow Toxicity Metrics
VPIN (Volume-Synchronized Probability of Informed Trading):
Principle: replace clock time with volume time to measure the probability of informed trading
Calculation steps:
1. Bucket trades by fixed volume (Volume Bucket)
Bucket size V = average daily volume / 50 (about 5-10 minutes per bucket)
2. Classify buy and sell volume in each bucket (Bulk Volume Classification):
buy_volume = V × Φ(ΔP / σ) (standard normal CDF)
sell_volume = V - buy_volume
3. Compute order-flow imbalance:
OI_i = |buy_volume_i - sell_volume_i|
4. VPIN = Σ(OI_i) / (n × V) (n=50-bucket rolling window)
Interpretation:
VPIN < 0.3 -> normal, low informed-trading share
VPIN 0.3-0.5 -> caution, informed trading rising
VPIN > 0.5 -> dangerous, high probability that major information is about to be released
China A-share usage:
A sudden VPIN spike in a stock may foreshadow:
- insider trading ahead of a major announcement
- institutional position building / distribution
Before the 2015 China A-share flash crashes, VPIN stayed above 0.6 for a prolonged period
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.
- 11d ago First seen · 308 lines · 55 tokens per session scan A 35dbbb3a9a53
market-microstructure is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 3,257 once invoked, about $0.0003 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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