Chdb Datastore
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
Technical analysis with 130+ indicators using pandas-ta for crypto market data
Install Pandas Ta
$ npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agiprolabs/claude-trading-skills pandas-ta --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pandas-ta .claude/skills/pandas-ta && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "pandas-ta" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta into .claude/skills/pandas-ta/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pandas-ta", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-taType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agiprolabs/claude-trading-skills pandas-ta --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pandas-ta .agents/skills/pandas-ta && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pandas-ta" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta into .agents/skills/pandas-ta/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pandas-ta", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agiprolabs/claude-trading-skills pandas-ta --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pandas-ta .cursor/skills/pandas-ta && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pandas-ta" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta into .cursor/skills/pandas-ta/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pandas-ta", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agiprolabs/claude-trading-skills.git --path skills/pandas-ta--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agiprolabs/claude-trading-skills pandas-ta --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pandas-ta .gemini/skills/pandas-ta && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pandas-ta" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta into .gemini/skills/pandas-ta/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pandas-ta", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agiprolabs/claude-trading-skills pandas-taInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pandas-ta .github/skills/pandas-ta && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pandas-ta" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta into .github/skills/pandas-ta/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pandas-ta", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agiprolabs/claude-trading-skills pandas-ta --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pandas-ta .opencode/skills/pandas-ta && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pandas-ta" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/pandas-ta into .opencode/skills/pandas-ta/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pandas-ta", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
pandas-ta- GitHub stars
- 410
- Token cost
- ~2.3k tokens
- SKILL.md length
- 555 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 68
- Repo updated
- First seen
- Licence
- MIT
At a glance
Technical analysis with 130+ indicators using pandas-ta for crypto market data
- Tasks that involve DataFrames
- SKILL.md covers Installation, Quick Start, OHLCV DataFrame Format and Core Indicator Categories, plus 5 more sections
- Runs Python scripts from its folder; calls uv
What it does
Pandas Ta is an agent skill from agiprolabs/claude-trading-skills. Technical analysis with 130+ indicators using pandas-ta for crypto market data
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/common_pitfalls.md`, `references/indicator_guide.md` and `references/strategy_patterns.md`).
It sits in Data & Analytics, covering DataFrames. It works with pandas. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.
When your agent uses it
- Tasks that involve DataFrames
Example prompts
- “/pandas-ta”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Pandas Ta loads about 2.3k tokens when it runs, and up to ~8.5k if it reads all its reference files. Until then it costs about 22 tokens; SKILL.md has 555 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 555 words, ~2,285 tokens.
Download SKILL.mdSave it as
.claude/skills/pandas-ta/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- pandas-ta
- description
- Technical analysis with 130+ indicators using pandas-ta for crypto market data
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 httpxQuick 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 |
Momentum Indicators
Measure speed and magnitude of price changes.
| Indicator | Call | Key Signal |
|---|---|---|
| RSI | df.ta.rsi(length=14) | >70 overbought, <30 oversold |
| MACD | df.ta.macd(fast=12, slow=26, signal=9) | Histogram crossover = entry |
| Stochastic | df.ta.stoch(k=14, d=3, smooth_k=3) | >80 overbought, <20 oversold |
| CCI | df.ta.cci(length=20) | >100 overbought, <-100 oversold |
| Williams %R | df.ta.willr(length=14) | >-20 overbought, <-80 oversold |
| ROC | df.ta.roc(length=10) | Positive = upward momentum |
| MFI | df.ta.mfi(length=14) | Money flow version of RSI |
Volatility Indicators
Measure price dispersion and expected range.
| Indicator | Call | Key Signal |
|---|---|---|
| Bollinger Bands | df.ta.bbands(length=20, std=2) | Squeeze = breakout pending |
| ATR | df.ta.atr(length=14) | Position sizing, stop placement |
| Keltner Channels | df.ta.kc(length=20, scalar=1.5) | BB inside KC = squeeze |
| Donchian Channels | df.ta.donchian(lower_length=20, upper_length=20) | Breakout detection |
Volume Indicators
Confirm price moves with volume analysis.
| Indicator | Call | Key Signal |
|---|---|---|
| OBV | df.ta.obv() | Divergence from price = reversal |
| VWAP | df.ta.vwap() | Intraday fair value (needs DatetimeIndex) |
| CMF | df.ta.cmf(length=20) | >0 accumulation, <0 distribution |
| AD | df.ta.ad() | Accumulation/Distribution line |
Strategy Class
Run multiple indicators in a single call using ta.Strategy:
import pandas_ta as ta
# Built-in "All" strategy runs every indicator
df.ta.strategy(ta.AllStrategy)
# Custom strategy
my_strategy = ta.Strategy(
name="Crypto Scalp",
description="Fast indicators for crypto scalping",
ta=[
{"kind": "ema", "length": 9},
{"kind": "ema", "length": 21},
{"kind": "rsi", "length": 7},
{"kind": "stoch", "k": 5, "d": 3, "smooth_k": 3},
{"kind": "atr", "length": 7},
{"kind": "bbands", "length": 10, "std": 2.0},
{"kind": "obv"},
]
)
df.ta.strategy(my_strategy)Named Strategy Patterns
# Trend following
trend_strategy = ta.Strategy(
name="Trend",
ta=[
{"kind": "ema", "length": 20},
{"kind": "ema", "length": 50},
{"kind": "adx", "length": 14},
{"kind": "supertrend", "length": 10, "multiplier": 3},
{"kind": "atr", "length": 14},
]
)
# Mean reversion
reversion_strategy = ta.Strategy(
name="Mean Reversion",
ta=[
{"kind": "rsi", "length": 14},
{"kind": "bbands", "length": 20, "std": 2.0},
{"kind": "stoch", "k": 14, "d": 3, "smooth_k": 3},
{"kind": "cci", "length": 20},
]
)
# Momentum
momentum_strategy = ta.Strategy(
name="Momentum",
ta=[
{"kind": "macd", "fast": 12, "slow": 26, "signal": 9},
{"kind": "rsi", "length": 14},
{"kind": "obv"},
{"kind": "roc", "length": 10},
{"kind": "mfi", "length": 14},
]
)Crypto-Specific Considerations
24/7 Markets
- No session gaps — indicators that rely on open/close of sessions behave differently
- VWAP resets at midnight UTC by default; consider anchored VWAP for custom periods
- Weekend data is continuous — no Monday gap effects
Show full SKILL.md (232 more words)Show less
High Volatility Adjustments
- Bollinger Bands: Use 2.5-3x standard deviation instead of the default 2x
- RSI periods: Shorter periods (7-10) capture faster crypto cycles
- ATR: Use for dynamic stop-losses; crypto ATR is typically 2-5x equity ATR
- SuperTrend multiplier: 3-4x for crypto vs 2-3x for equities
Low-Cap Token Considerations
- Volume indicators (OBV, CMF, MFI) are unreliable with thin order books
- Prefer price-based indicators (RSI, BBands, SuperTrend) for low-liquidity tokens
- ATR-based position sizing is critical — wide spreads amplify losses
- Wash trading inflates volume; cross-reference with on-chain data
Timeframe Selection
| Timeframe | Use Case | Recommended Indicators |
|---|---|---|
| 1m-5m | Scalping, PumpFun | RSI(5-7), EMA(5,13), ATR(5) |
| 15m-1h | Day trading | MACD, RSI(14), BBands, EMA(20,50) |
| 4h-1d | Swing trading | SuperTrend, ADX, EMA(50,200) |
| 1w | Position trading | SMA(20,50), RSI(14), monthly VWAP |
Common Indicator Combinations
Trend Following
# EMA crossover + ADX confirmation + SuperTrend direction
ema_fast = df.ta.ema(length=20)
ema_slow = df.ta.ema(length=50)
adx_df = df.ta.adx(length=14)
st_df = df.ta.supertrend(length=10, multiplier=3)
bullish = (
(ema_fast > ema_slow) &
(adx_df["ADX_14"] > 25) &
(st_df["SUPERTd_10_3.0"] == 1)
)Mean Reversion
# RSI oversold + price at lower BB + Stochastic oversold
rsi = df.ta.rsi(length=14)
bb = df.ta.bbands(length=20, std=2.5)
stoch = df.ta.stoch(k=14, d=3, smooth_k=3)
buy_signal = (
(rsi < 30) &
(df["close"] <= bb["BBL_20_2.5"]) &
(stoch["STOCHk_14_3_3"] < 20)
)Momentum Confirmation
# MACD histogram positive + RSI above 50 + OBV rising
macd = df.ta.macd(fast=12, slow=26, signal=9)
rsi = df.ta.rsi(length=14)
obv = df.ta.obv()
momentum_bull = (
(macd["MACDh_12_26_9"] > 0) &
(rsi > 50) &
(obv > obv.shift(1))
)Volatility Breakout (BB Squeeze)
# Bollinger Band width contracting + volume spike
bb = df.ta.bbands(length=20, std=2.0)
atr = df.ta.atr(length=14)
vol_sma = df["volume"].rolling(20).mean()
bb_width = (bb["BBU_20_2.0"] - bb["BBL_20_2.0"]) / bb["BBM_20_2.0"]
squeeze = bb_width < bb_width.rolling(120).quantile(0.1)
vol_spike = df["volume"] > (vol_sma * 2.0)
breakout_setup = squeeze & vol_spikeIntegration with Other Skills
- birdeye-api: Fetch OHLCV data → feed into pandas-ta for indicator computation
- vectorbt: Use pandas-ta indicators as signal inputs for backtesting
- trading-visualization: Plot indicator overlays on price charts
- slippage-modeling: Combine ATR with slippage estimates for realistic execution modeling
- position-sizing: Use ATR-based sizing from pandas-ta output
Files
References
references/indicator_guide.md— Top 20 crypto indicators with syntax, parameters, and interpretationreferences/strategy_patterns.md— Pre-built strategy combinations for scalping, day trading, and swing tradingreferences/common_pitfalls.md— Common mistakes with technical indicators in crypto markets
Scripts
scripts/compute_indicators.py— Fetch OHLCV data and compute standard indicator set with signal summaryscripts/multi_indicator_scan.py— Run multiple strategy profiles and score current signal alignment
© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 5 other files (scripts, references) in skills/pandas-ta of agiprolabs/claude-trading-skills.
- SKILL.md
- references/common_pitfalls.md
- references/indicator_guide.md
- references/strategy_patterns.md
- scripts/compute_indicators.py
- scripts/multi_indicator_scan.py
Open the folder on GitHubat commit 981e1d7
Compare with similar skills
Pandas Ta next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pandas Ta this skillagiprolabs/claude-trading-skills | 410 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Chdb Datastorevemetric/vemetric | 395 | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| CSV Data Summarizercoffeefuelbump/csv-data-summarizer-claude-skill | 468 | 2 repos | ~1.4k | Automated safety check: Pass | None | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Retentioneering Product Analyticsretentioneering/retentioneering-tools | 927 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 |
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Related
Works with
Categories
Questions about Pandas Ta
What does Pandas Ta do?
Technical analysis with 130+ indicators using pandas-ta for crypto market data. Pandas Ta is an agent skill from agiprolabs/claude-trading-skills.
When should I use Pandas Ta?
Pandas Ta fits situations like: tasks that involve DataFrames.
How do I install Pandas Ta in Claude Code?
Run `npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a claude-code`. Or copy the skill folder (skills/pandas-ta in agiprolabs/claude-trading-skills) into .claude/skills/pandas-ta in your project. Claude Code loads it when a task matches its description.
How do I install Pandas Ta in Codex?
Run `npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a codex`. Or copy the skill folder (skills/pandas-ta in agiprolabs/claude-trading-skills) into .agents/skills/pandas-ta in your project. Codex loads it when a task matches its description.
Can I use Pandas Ta in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill pandas-ta -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pandas-ta, .gemini/skills/pandas-ta, .github/skills/pandas-ta and .opencode/skills/pandas-ta in your project.
What does Pandas Ta need to run?
Going by SKILL.md and its folder, Pandas Ta needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Pandas Ta access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Pandas Ta safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Pandas Ta use?
Pandas Ta is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pandas Ta use?
About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.2k tokens, read only when the agent opens those files.
What are the alternatives to Pandas Ta?
Skills that share tags, products or a category with Pandas Ta: Chdb Datastore (vemetric/vemetric, 395 stars), CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars), Pandas Pro (Jeffallan/claude-skills, 12k stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pandas Ta?
agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.
Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.