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 feature-engineeringgit 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/feature-engineering)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/feature-engineering"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/feature-engineering/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/feature-engineering"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/feature-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00025 | $0.02766 |
| Opus 5 | $0.00013 | $0.01383 |
| Sonnet 5 | $0.00005 | $0.00553 |
| Haiku 4.5 | $0.00003 | $0.00277 |
Grade A, and why
feature-engineering 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Engineering for Trading ML
Feature engineering is the single highest-leverage activity in building ML trading models. Model selection (XGBoost vs. neural net vs. logistic regression) matters far less than the quality and diversity of input features. A simple model on great features will outperform a complex model on raw prices every time.
This skill covers constructing, validating, and selecting features from market data for use in classification (signal-classification) and regression models targeting crypto/Solana token trading.
Why Features Beat Models
Raw OHLCV data is non-stationary, noisy, and high-dimensional. Models trained directly on price series will overfit. Feature engineering transforms raw data into stationary, informative signals that capture distinct aspects of market behavior:
- Compression: Reduce thousands of price bars to dozens of descriptive statistics
- Stationarity: Convert non-stationary prices into stationary returns and ratios
- Domain knowledge: Encode trader intuition (support/resistance, volume climax) as computable quantities
- Regime awareness: Features that behave differently in trending vs. ranging markets help models adapt
Feature Categories
1. Price Features
Derived purely from OHLCV price columns. These capture trend, momentum, and volatility from the price series itself.
| Feature | Formula | Lookback |
|---|---|---|
log_return |
ln(close_t / close_{t-1}) |
1 bar |
abs_return |
abs(log_return) |
1 bar |
return_volatility |
std(log_return, N) |
20 bars |
momentum_N |
close_t / close_{t-N} - 1 |
5, 10, 20 |
acceleration |
momentum_5 - momentum_5[5] |
10 bars |
high_low_range |
(high - low) / close |
1 bar |
close_position |
(close - low) / (high - low) |
1 bar |
gap |
open_t / close_{t-1} - 1 |
1 bar |
rolling_skew |
skew(log_return, N) |
20 bars |
rolling_kurtosis |
kurtosis(log_return, N) |
20 bars |
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 · 300 lines · 25 tokens per session scan A 09dcfb689627
feature-engineering is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 25 tokens to every session and 2,766 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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