Documentation

See it in action — browse live examples on the @skua profile page.

Installation

pip install getskua

That's it. Skua uses your installed libraries to serialize your objects so there will never be any version conflicts or compatibility issues. Works with matplotlib, plotly, pandas, polars, numpy, PIL, PyTorch, TensorFlow, and any object with a string representation.

Quick Start

import skua
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 5))
ax.bar(x, revenue, label="Revenue", ...)
ax.bar(x, costs, label="Costs", ...)
ax.plot(x, profit, "o-", label="Profit")
# ...

skua.snap(fig, title="Revenue vs Costs", description="...", tags=[...])
# > https://skua.dev/s/yut0cFOSzFFU

Share the URL with anyone. Re-run skua.snap() with the same title to update in place — same URL, fresh results.

CLI

skua snap chart.png --title "Q3 Revenue"
skua snap data.csv --title "Sales Data" --public --tags "finance,q3"
skua snap plot.json --title "Interactive Chart"
cat report.txt | skua snap - --type text --title "Analysis"

Use --json for machine-readable output:

skua snap chart.png --title "Chart" --json
# {"url": "https://skua.dev/s/abc123", "id": "abc123", "visibility": "public"}

API Reference

skua.snap()

Capture and share a Python object via Skua.

objrequired

Object to snap (matplotlib/plotly figure, pandas/polars DataFrame, PIL image, PyTorch/TF tensor, list of dicts, or any object with string representation)

titlerequired, str, max 500 chars

Snapshot title. Snapping with the same title updates the existing snapshot.

descriptionoptional, str, max 1000 chars

Short description providing context (1-3 sentences recommended).

tagsoptional, list[str], max 20 tags, 30 chars each

Tags for categorization. Empty strings are ignored, whitespace is stripped.

Returns a SnapResult object with .url and .metadata attributes. In Jupyter notebooks, displays the original object inline.

skua.configure()

Set session-wide defaults and configuration.

publicoptional, bool

Default visibility for snapshots.

api_urloptional, str

Base URL for Skua API.

web_urloptional, str

Base URL for Skua web app.

tokenoptional, str

Authentication token for programmatic access.

skua.login()

Open the browser to verify your email. Verified accounts get 90-day retention (vs 7 days), 50 snapshots (vs 10), and a public profile page at skua.dev/u/username.

skua.auth()

Paste the token you receive by email after skua.login().

skua.status()

Check current authentication status. Returns a dict with authenticated, email, session_id, and retention_days.

Supported Types

Matplotlib figures

Saved as high-DPI PNG images. Pass any plt.Figure object.

import skua
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 5))
ax.scatter(*setosa.T, label="Setosa", alpha=0.7)
ax.scatter(*versicolor.T, label="Versicolor", alpha=0.7)
ax.scatter(*virginica.T, label="Virginica", alpha=0.7)
ax.set_xlabel("Sepal Length (cm)")
ax.set_ylabel("Sepal Width (cm)")
ax.set_title("Iris Dataset — Sepal Dimensions")
ax.legend()

skua.snap(fig, title="Iris Scatter Plot", description="...", tags=[...])
# > https://skua.dev/s/aFAsrP04QFpZ

Plotly figures

Rendered as fully interactive charts — zoom, pan, hover tooltips, and PNG export.

import skua
import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=years, y=anomaly, ...))

skua.snap(fig, title="Global Temperature Anomaly", description="...", tags=[...])
# > https://skua.dev/s/IGj1D9tvjEzl

Pandas DataFrames

Rendered as interactive, sortable, filterable tables.

import skua
import pandas as pd

df = df.set_index("ticker")
# df = pd.DataFrame({...})  # 30 stocks
skua.snap(df, title="S&P 500 Snapshot", description="...", tags=[...])
# > https://skua.dev/s/8qwgF8I3fWyM

Polars DataFrames

Same interactive table rendering. LazyFrames are collected automatically.

import skua
import polars as pl

# df = pl.DataFrame({...})  # 8 chips
skua.snap(df, title="CPU Benchmarks", description="...", tags=[...])
# > https://skua.dev/s/Jif2aMq39AMg

List of dicts

Common output from HuggingFace pipelines and eval loops — rendered as a table.

import skua

# results = pipeline("sentiment-analysis")(texts)
skua.snap(results, title="Sentiment Analysis", description="...", tags=[...])
# > https://skua.dev/s/K0OT1VKeqtC4

PIL Images

Any PIL.Image object, saved as PNG.

import skua
from PIL import Image
import numpy as np

w, h, max_iter = 960, 640, 80
x = np.linspace(-2.5, 1.0, w)
y = np.linspace(-1.2, 1.2, h)
C = x[np.newaxis, :] + 1j * y[:, np.newaxis]

Z = np.zeros_like(C)
M = np.zeros(C.shape, dtype=int)
for i in range(max_iter):
    mask = np.abs(Z) <= 2
    Z[mask] = Z[mask] ** 2 + C[mask]
    M[mask] = i

t = M / max_iter
r = (np.sin(3.0 * t * np.pi) * 127 + 128).astype(np.uint8)
g = (np.sin(3.0 * t * np.pi + 0.8) * 127 + 128).astype(np.uint8)
b = (np.sin(3.0 * t * np.pi + 1.6) * 127 + 128).astype(np.uint8)
rgb = np.stack([r, g, b], axis=-1)
img = Image.fromarray(rgb)

skua.snap(img, title="Mandelbrot Set", description="...", tags=[...])
# > https://skua.dev/s/CJDwwRo2lLMT

PyTorch & TensorFlow tensors

Image tensors (CHW or BCHW) are rendered as PNG. Other tensors are displayed as tables.

Text & strings

Any object with a string representation. Full Unicode support — em-dashes, accented characters, CJK, and emoji all render correctly.

import skua

skua.snap("¥127.4M", title="Q4 Revenue", description="...", tags=[...])
# > https://skua.dev/s/9f7oA3UsCVMu

Anonymous Usage

No account required. Anonymous sessions have the following limits:

Snapshots expire after 7 days
10 snapshots per session
20 uploads per hour
10 MB max per snapshot

Validated Accounts

Verify your email to unlock longer retention and higher limits:

90-day retention per snapshot — each snapshot's clock resets when you update it
1-year maximum retention per snapshot
50 snapshots per account
10 MB max per snapshot

Run skua.login() in your notebook to start the verification flow. You'll receive a token by email to paste back into your session.

Support

Questions or feedback? Email hello@skua.dev or open an issue on GitHub.