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 execution-modelgit 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/execution-model)<a href="https://agentmods.dev/skills/skloxo/tidetrading/execution-model"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/execution-model/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/execution-model"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/execution-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.02984 |
| Opus 5.5 | $0.00016 | $0.01194 |
| Sonnet 5.5 | $0.00008 | $0.00597 |
| Haiku 4.5 | $0.00004 | $0.00298 |
Grade A, and why
execution-model 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.
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trade Execution Modeling
Overview
Provide more realistic execution assumptions for backtests, including slippage models, market-impact estimation, and execution-algorithm principles. This skill is for backtest simulation only and does not involve live order execution.
Slippage Models
Why Slippage Models Are Needed
Idealized backtest: filled at the close, zero slippage
Real world:
1. The order book has a bid-ask spread
2. Large orders push prices (market impact)
3. Execution is delayed (there is latency from signal to fill)
No slippage model -> overly optimistic backtest -> losses in live trading
1. Fixed Slippage Model
def fixed_slippage(price: float, direction: int, bps: float = 5.0) -> float:
"""
Args:
price: Original price
direction: 1=buy, -1=sell
bps: Slippage in basis points (1bp = 0.01%), default 5bp
Returns:
Execution price after slippage
"""
slippage = price * bps / 10000
return price + direction * slippage
Reference fixed-slippage assumptions by market:
| Market | Instrument | Suggested Slippage (bps) | Notes |
|---|---|---|---|
| China A-share large cap | CSI 300 constituents | 3-5 | Good liquidity |
| China A-share small cap | CSI 1000 constituents | 5-10 | Average liquidity |
| China micro-cap | market cap < 5 billion RMB | 10-30 | Poor liquidity |
| US large cap | AAPL / MSFT | 1-3 | Excellent liquidity |
| Hong Kong stocks | Hang Seng constituents | 5-10 | Less liquid than A / US |
| BTC spot | BTC-USDT | 2-5 | Good OKX liquidity |
| ETH spot | ETH-USDT | 3-8 | Slightly worse than BTC |
| Small altcoins | other -USDT pairs |
10-50 | Liquidity varies widely |
2. Linear Impact Model
def linear_impact(price: float, direction: int,
volume_traded: float, adv: float,
impact_coeff: float = 0.1) -> float:
"""
Linear market impact: impact ∝ traded volume / ADV
Args:
price: Original price
direction: 1=buy, -1=sell
volume_traded: Trade size (shares or notional)
adv: Average Daily Volume
impact_coeff: Impact coefficient, usually 0.05-0.2
Returns:
Execution price after impact
"""
participation_rate = volume_traded / adv
impact = impact_coeff * participation_rate
return price * (1 + direction * impact)
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 · 350 lines · 41 tokens per session scan A 1d42ba51319d
execution-model is a skill published in the GitHub repository skloxo/TideTrading (13 stars, last pushed 9d ago), licensed MIT. It adds 41 tokens to every session and 2,984 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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