Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Teamnpx agentmods add skills/aaaaqwq/agi-super-team/backtest-expertWrote 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/aaaaqwq/agi-super-team/backtest-expert)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/backtest-expert"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/backtest-expert/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/aaaaqwq/agi-super-team/backtest-expert"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/backtest-expert.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 | $0.00081 | $0.01922 |
| Opus 5 | $0.00041 | $0.00961 |
| Sonnet 5 | $0.00016 | $0.00384 |
| Haiku 4.5 | $0.00008 | $0.00192 |
Grade A, and why
backtest-expert 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 3d 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 backtest-expert — 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backtest Expert
Systematic approach to backtesting trading strategies based on professional methodology that prioritizes robustness over optimistic results.
Core Philosophy
Goal: Find strategies that "break the least", not strategies that "profit the most" on paper.
Principle: Add friction, stress test assumptions, and see what survives. If a strategy holds up under pessimistic conditions, it's more likely to work in live trading.
When to Use This Skill
Use this skill when:
- Developing or validating systematic trading strategies
- Evaluating whether a trading idea is robust enough for live implementation
- Troubleshooting why a backtest might be misleading
- Learning proper backtesting methodology
- Avoiding common pitfalls (curve-fitting, look-ahead bias, survivorship bias)
- Assessing parameter sensitivity and regime dependence
- Setting realistic expectations for slippage and execution costs
Prerequisites
- Python 3.9+ (for evaluation script)
- No API keys required
- No external data dependencies — metrics are user-provided
Workflow
1. State the Hypothesis
Define the edge in one sentence.
Example: "Stocks that gap up >3% on earnings and pull back to previous day's close within first hour provide mean-reversion opportunity."
If you can't articulate the edge clearly, don't proceed to testing.
2. Codify Rules with Zero Discretion
Define with complete specificity:
- Entry: Exact conditions, timing, price type
- Exit: Stop loss, profit target, time-based exit
- Position sizing: Fixed $$, % of portfolio, volatility-adjusted
- Filters: Market cap, volume, sector, volatility conditions
- Universe: What instruments are eligible
Critical: No subjective judgment allowed. Every decision must be rule-based and unambiguous.
3. Run Initial Backtest
Test over:
- Minimum 5 years (preferably 10+)
- Multiple market regimes (bull, bear, high/low volatility)
- Realistic costs: Commissions + conservative slippage
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.
- 3d ago First seen · 235 lines · 81 tokens per session scan A 316c87c2d2b8
backtest-expert is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 81 tokens to every session and 1,922 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to backtest-expert, differing in 0 lines, and is treated as a copy.
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