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 pandas-tagit 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/pandas-ta)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/pandas-ta"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/pandas-ta/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/pandas-ta"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/pandas-ta.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 | $0.00018 | $0.02683 |
| Opus 5 | $0.00009 | $0.01341 |
| Sonnet 5 | $0.00004 | $0.00537 |
| Haiku 4.5 | $0.00002 | $0.00268 |
Grade A, and why
pandas-ta 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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pandas-ta — Technical Analysis for Crypto Markets
pandas-ta is a Python library that extends pandas DataFrames with 130+ technical analysis indicators accessible via df.ta. It covers trend, momentum, volatility, volume, and overlap indicator categories — all callable with a single method on any OHLCV DataFrame.
Installation
uv pip install pandas-ta pandas httpx
Quick Start
import pandas as pd
import pandas_ta as ta
# Assume df is a DataFrame with columns: open, high, low, close, volume
# All lowercase column names required
# Single indicator
df["rsi"] = df.ta.rsi(length=14)
df["atr"] = df.ta.atr(length=14)
# Multiple indicators via strategy
df.ta.strategy(ta.Strategy(
name="Quick Check",
ta=[
{"kind": "rsi", "length": 14},
{"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
{"kind": "bbands", "length": 20, "std": 2.0},
]
))
OHLCV DataFrame Format
pandas-ta expects a DataFrame with lowercase column names:
import pandas as pd
df = pd.DataFrame({
"open": [...],
"high": [...],
"low": [...],
"close": [...],
"volume": [...]
}, index=pd.DatetimeIndex([...]))
Important: Set the index to a DatetimeIndex for time-aware indicators like VWAP. Column names must be lowercase (close, not Close).
Handling Missing Data
# Drop rows with NaN in OHLCV columns
df = df.dropna(subset=["open", "high", "low", "close", "volume"])
# Forward-fill small gaps (1-2 bars max)
df = df.ffill(limit=2)
# Verify no zero-volume bars for volume indicators
df = df[df["volume"] > 0]
Core Indicator Categories
Trend Indicators
Identify market direction and trend strength.
| Indicator | Call | Key Signal |
|---|---|---|
| SMA | df.ta.sma(length=20) |
Price above = bullish |
| EMA | df.ta.ema(length=20) |
Faster than SMA, less lag |
| SuperTrend | df.ta.supertrend(length=10, multiplier=3) |
Direction column: 1=bull, -1=bear |
| Ichimoku | df.ta.ichimoku() |
Returns tuple of (span, lines) DataFrames |
| VWMA | df.ta.vwma(length=20) |
Volume-weighted price trend |
| HMA | df.ta.hma(length=20) |
Minimal lag, smooth trend |
| ADX | df.ta.adx(length=14) |
>25 = trending, <20 = ranging |
What ships with it
5 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 · 293 lines · 18 tokens per session scan A 692a50c3ceb9
pandas-ta is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 18 tokens to every session and 2,683 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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