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 agiprolabs/claude-trading-skills --skill slippage-modelinggit clone --depth 1 https://github.com/agiprolabs/claude-trading-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/agiprolabs/claude-trading-skills/slippage-modeling)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/slippage-modeling"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/slippage-modeling/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/agiprolabs/claude-trading-skills/slippage-modeling"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/slippage-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00024 | $0.02021 |
| Opus 5 | $0.00012 | $0.01010 |
| Sonnet 5 | $0.00005 | $0.00404 |
| Haiku 4.5 | $0.00002 | $0.00202 |
Grade A, and why
slippage-modeling 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 12d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Slippage Modeling
Estimate execution costs, model slippage curves from AMM mechanics and empirical quotes, and determine optimal trade sizes that keep costs within acceptable thresholds.
What Is Slippage?
Slippage is the difference between the expected price at the time you decide to trade and the actual execution price you receive. On decentralized exchanges, slippage is deterministic and measurable — unlike CEX slippage, which depends on hidden order book dynamics.
Example: You expect to buy a token at 0.001 SOL. Your trade executes at 0.00105 SOL. That 5% difference is slippage — it directly reduces your profit and increases your break-even threshold.
Sources of Slippage
1. AMM Price Impact (Primary Source)
Automated market makers use bonding curves that move price as liquidity is consumed. On a constant-product AMM (x * y = k):
price_impact = Δx / (x + Δx)
Where x is the reserve of the input token and Δx is your trade size. A 1 SOL trade against a pool with 100 SOL reserves produces ~1% price impact. Against 10 SOL reserves, it produces ~10%.
See references/slippage_math.md for full derivations and CLMM adjustments.
2. DEX Fees
Every swap incurs a fee taken from the trade:
| DEX | Fee | Notes |
|---|---|---|
| Raydium | 0.25% | Standard AMM pools |
| Orca | 0.30% | Whirlpool concentrated pools |
| Meteora | 0.1–2.0% | Dynamic fees based on volatility |
| PumpFun | 1.0% | Bonding curve phase |
3. Priority Fees
Solana validators prioritize transactions with higher compute unit prices. During congestion or for time-sensitive trades:
- Normal: 0.0001 SOL (negligible)
- Competitive: 0.001–0.01 SOL
- High congestion: 0.01–0.1 SOL
4. MEV (Sandwich Attacks)
Searchers detect pending swaps and sandwich them — buying before your trade (raising the price) and selling after (capturing the difference). MEV cost depends on:
- Trade size (larger = more attractive target)
- Token liquidity (thin pools = easier to manipulate)
- Slippage tolerance setting (higher tolerance = more extractable)
What ships with it
4 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.
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.
- 12d ago First seen · 199 lines · 24 tokens per session scan A 030f50938f4d
slippage-modeling is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 24 tokens to every session and 2,021 once invoked, about $0.0001 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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