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 alpacahq/alpaca-skills --skill backtestgit clone --depth 1 https://github.com/alpacahq/alpaca-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/alpacahq/alpaca-skills/backtest)<a href="https://agentmods.dev/skills/alpacahq/alpaca-skills/backtest"><img src="https://agentmods.dev/badge/skills/alpacahq/alpaca-skills/backtest/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/alpacahq/alpaca-skills/backtest"><img src="https://agentmods.dev/badge/skills/alpacahq/alpaca-skills/backtest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Output Handling · line 351 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
- medium Output Handling · line 354 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00063 | $0.03384 |
| Opus 5 | $0.00032 | $0.01692 |
| Sonnet 5 | $0.00013 | $0.00677 |
| Haiku 4.5 | $0.00006 | $0.00338 |
Grade A, and why
alpaca-trading-backtest 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 10d 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 — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trading API Backtesting
Use this skill when you want your AI agent to run a specific historical backtest with the Alpaca CLI and local workspace code. This version is optimized for run-specific execution: your agent writes the minimum readable code needed for the confirmed strategy, stores the exact artifacts, and reports the results back to you.
This skill is written for you, the person invoking it through your AI agent. You means the trader, developer, researcher, or operator asking your agent to run the backtest. Your agent should address you directly, restate assumptions clearly, and make every interpretation choice visible.
strategy idea -> formalized rules -> confirmed assumptions -> CLI data fetch -> local script -> artifacts -> report
It is not a promise that a strategy will work in live markets. It is a reproducible research workflow.
Required disclosures
Every report, notes.md, report.md, notebook, dashboard, or exported result should include:
Important disclosure
This backtest is a hypothetical historical simulation and does not represent actual trading performance. Backtested results do not guarantee future results. Results depend on market-data quality, data feed selection, corporate-action handling, fees, slippage, liquidity, taxes, execution assumptions, and implementation details. This material is for research and educational purposes only and is not investment advice, a recommendation, an offer, or a solicitation to buy or sell securities, options, cryptocurrencies, or any other financial product. All investments involve risk and may lose value. Review Alpaca's disclosures and agreements at alpaca.markets/disclosures.
When paper trading appears in the workflow, add:
Paper trading is a simulated environment. It does not involve real money or actual securities transactions. Paper results may differ from live trading because of fill assumptions, market impact, liquidity, latency, data differences, order handling, fees, and other market conditions.
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.
- 10d ago First seen · 422 lines · 63 tokens per session scan A 830db169959a
alpaca-trading-backtest is a skill published in the GitHub repository alpacahq/alpaca-skills (146 stars, last pushed yesterday), licensed Apache-2.0. It adds 63 tokens to every session and 3,384 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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