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 regime-detectiongit 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/regime-detection)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/regime-detection"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/regime-detection/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/regime-detection"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/regime-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00021 | $0.02875 |
| Opus 5 | $0.00010 | $0.01437 |
| Sonnet 5 | $0.00004 | $0.00575 |
| Haiku 4.5 | $0.00002 | $0.00287 |
Grade A, and why
regime-detection 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 13d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Regime Detection
Identify the current market regime so you can pick the right strategy, size positions correctly, and avoid deploying trend-following logic in a ranging market (or vice versa).
Why Regime Detection Matters
Every strategy has a "home regime." A momentum strategy prints money in a clean uptrend but bleeds in a choppy range. A mean-reversion grid thrives in low-volatility consolidation but gets steamrolled by a trending breakout. Regime detection tells you which playbook to use right now.
Key benefits:
- Strategy selection: Route signals to the right strategy for the current environment
- Position sizing: Reduce exposure in hostile regimes, increase in favorable ones
- Stop adaptation: Wider stops in high-vol regimes, tighter in low-vol trends
- Drawdown control: Sit out "danger zone" regimes (high vol + no trend)
Core Regime Dimensions
Two orthogonal axes define the four-quadrant regime model:
| Low Volatility | High Volatility | |
|---|---|---|
| Trending | Q1: Clean trend — best for trend following | Q2: Volatile trend — momentum with caution |
| Ranging | Q3: Quiet range — mean-reversion paradise | Q4: Choppy chaos — reduce or sit out |
A third dimension — mean-reversion tendency (Hurst exponent) — refines Q3 by telling you how reliably price reverts.
Simple Approaches (No ML Required)
1. ATR Volatility Percentile
Rank the current ATR against its own recent history to get a 0–100 percentile score.
import pandas as pd
import numpy as np
def atr_percentile(
high: pd.Series, low: pd.Series, close: pd.Series,
atr_period: int = 14, lookback: int = 100
) -> pd.Series:
"""ATR percentile rank over a rolling window."""
tr = pd.concat([
high - low,
(high - close.shift(1)).abs(),
(low - close.shift(1)).abs()
], axis=1).max(axis=1)
atr = tr.rolling(atr_period).mean()
return atr.rolling(lookback).apply(
lambda x: pd.Series(x).rank(pct=True).iloc[-1], raw=False
)
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.
- 13d ago First seen · 314 lines · 21 tokens per session scan A 6c654c23f490
regime-detection is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 21 tokens to every session and 2,875 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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