Quick Start

Three Interfaces

SciTeX exposes the same capabilities through three interfaces: Python API, CLI commands, and MCP tools for AI agents.

Python API

@stx.session – Reproducible Experiment Tracking

import scitex as stx

@stx.session
def main(filename="demo.jpg"):
    fig, ax = stx.plt.subplots()
    ax.plot_line(t, signal)
    ax.set_xyt("Time (s)", "Amplitude", "Title")
    stx.io.save(fig, filename)
    return 0

Output:

script_out/FINISHED_SUCCESS/2025-01-08_12-30-00_AbC1/
|-- demo.jpg                    # Figure with embedded metadata
|-- demo.csv                    # Auto-exported plot data
|-- CONFIGS/CONFIG.yaml         # Reproducible parameters
+-- logs/{stdout,stderr}.log    # Execution logs

stx.io – Universal File I/O (30+ formats)

stx.io.save(df, "output.csv")
stx.io.save(fig, "output.jpg")
df = stx.io.load("output.csv")

stx.stats – Publication-Ready Statistics (23 tests)

result = stx.stats.test_ttest_ind(group1, group2, return_as="dataframe")
# Includes: p-value, effect size, CI, normality check, power

stx.plt – Publication-Ready Figures

fig, ax = stx.plt.subplots()
ax.plot_line(x, y, label="signal")
ax.set_xyt("Time (s)", "Amplitude", "Sine Wave")
stx.io.save(fig, "plot.png")
# Creates: plot.png + plot.csv + plot.yaml (recipe for reproduction)

stx.scholar – Literature Management

# Enrich BibTeX with abstracts, DOIs, citation counts
stx.scholar.enrich_bibtex("refs.bib", add_abstracts=True)

CLI Commands

scitex --help-recursive              # Show all commands
scitex scholar fetch "10.1038/..."   # Download paper by DOI
scitex scholar bibtex refs.bib       # Enrich BibTeX
scitex stats recommend               # Suggest statistical tests
scitex audio speak "Done"            # Text-to-speech
scitex capture snap                  # Screenshot

MCP Tools (120+ tools for AI agents)

Turn AI agents into autonomous scientific researchers.

Category	Tools	Description
writer	28	LaTeX manuscript compilation
scholar	23	PDF download, metadata enrichment
capture	12	Screen monitoring and capture
introspect	12	Python code introspection
audio	10	Text-to-speech, audio playback
stats	10	Automated statistical testing
plt	9	Matplotlib figure creation
diagram	9	Mermaid and Graphviz diagrams
dataset	8	Neuroimaging/physiology datasets
social	7	Social media posting
canvas	7	Figure composition
template	6	Project scaffolding
verify	6	Reproducibility verification
dev	6	Version checking
ui	5	Notifications
linter	3	Code pattern checking

Claude Code Setup — add .mcp.json to your project root. Use SCITEX_ENV_SRC to load all configuration from a .src file — this keeps .mcp.json static across environments:

{
  "mcpServers": {
    "scitex": {
      "command": "scitex",
      "args": ["mcp", "start"],
      "env": {
        "SCITEX_ENV_SRC": "${SCITEX_ENV_SRC}"
      }
    }
  }
}

Switch environments via your shell profile:

# Local machine
export SCITEX_ENV_SRC=~/.scitex/scitex/local.src

# Remote server
export SCITEX_ENV_SRC=~/.scitex/scitex/remote.src

Generate a template .src file:

scitex env-template -o ~/.scitex/scitex/local.src

Or install globally:

scitex mcp install

Complete Example

A typical research script combining session, I/O, plotting, and statistics:

import scitex as stx

@stx.session
def main(
    n=200,
    noise=0.1,
    CONFIG=stx.INJECTED,
    plt=stx.INJECTED,
    logger=stx.INJECTED,
):
    """Analyze noisy sine wave."""
    import numpy as np

    # Generate data
    x = np.linspace(0, 4 * np.pi, n)
    y_clean = np.sin(x)
    y_noisy = y_clean + np.random.normal(0, noise, n)

    # Save raw data
    stx.io.save({"x": x, "y_clean": y_clean, "y_noisy": y_noisy}, "data.npz")

    # Visualize
    fig, ax = plt.subplots()
    ax.plot_scatter(x, y_noisy, alpha=0.3, label="Noisy")
    ax.plot_line(x, y_clean, color="red", label="True")
    ax.set_xyt("x", "y", f"Sine Wave (noise={noise})")
    stx.io.save(fig, "sine.png")

    # Correlation between noisy and clean
    result = stx.stats.test_pearson(y_clean, y_noisy)
    logger.info(f"Pearson r={result['effect_size']:.3f}, p={result['pvalue']:.1e}")

    # Save summary
    stx.io.save({"r": result["effect_size"], "p": result["pvalue"]}, "stats.yaml")

    return 0

Next Steps

• Core Concepts – Architecture and design principles

• Module Overview – Detailed module documentation

• Plot Gallery – Plot examples