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https://github.com/ywatanabe1989/scitex-python

Python toolkit for reproducible science. CLI commands, 371+ MCP tools, built-in skills. From raw data to manuscript — with reproducibility verification. For AI and human researchers.
https://github.com/ywatanabe1989/scitex-python

academic-writing ai-research automation bibtex data-visualization generative-ai latex literature-review machine-learning matplotlib mcp mcp-server pandas python pytorch reproducibility research-automation scientific-computing scitex statistics

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Python toolkit for reproducible science. CLI commands, 371+ MCP tools, built-in skills. From raw data to manuscript — with reproducibility verification. For AI and human researchers.

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README 

          



# SciTeX (scitex)





  

    SciTeX

  




Python Library for Science. For AI and Human Researchers





  PyPI version

  Python Versions

  Documentation

  License






  Docs ·

  Quick Start ·

  API ·

  pip install scitex[all]




---



This repository provides `scitex`, the orchestration layer of the SciTeX ecosystem — solving key problems in scientific research:



## Problem and Solution



| # | Problem | Solution |

|---|---------|----------|

| 1 | **Fragmented tools** -- literature search, statistics, figures, and writing each require separate tools with incompatible formats | **Unified toolkit** -- `import scitex as stx` provides 73 modules under one namespace, accessible via Python API, CLI, and MCP. These modules are standalone packages but loosely coupled through a plugin registry — each works on its own, yet composes into designed synergy (save a figure → auto-exports CSV + YAML recipe → hash-tracked by Clew → citeable in scitex-writer). |

| 2 | **No verification** -- existing tools address whether work *could* be reproduced, not whether it *has* been verified | **Cryptographic verification** -- Clew builds SHA-256 hash-chain DAGs linking every manuscript claim back to source data |

| 3 | **AI agents lack context** -- general-purpose LLMs cannot operate across the full research lifecycle without domain-specific tools | **323 MCP tools** -- AI agents run statistics, create figures, search literature, and compile manuscripts through structured tool calls |

| 4 | **No custom tooling** -- every lab needs domain-specific tools, but building and sharing them requires deep infrastructure knowledge | **App Maker and Store** -- researchers create custom apps with [scitex-app](https://github.com/ywatanabe1989/scitex-app) SDK and share via [SciTeX Cloud](https://scitex.ai) |

| 5 | **Vendor lock-in** -- cloud research tools (Overleaf, Zotero, Mendeley, Colab, GitHub Copilot) keep data on third-party servers and depend on APIs that can disappear overnight or monetize tomorrow | **Open and self-hostable** -- every SciTeX package is AGPL-3.0; the full 39-package ecosystem runs on your own hardware (or SciTeX Cloud which itself is self-hostable); cloud integrations are pluggable extras, not requirements |



## SciTeX and Research Workflow





    SciTeX Research Workflow



Figure 1. SciTeX research pipeline -- from literature search to manuscript compilation, with every step cryptographically linked.



## Demo — Automated Research from Data to Manuscript



**40 min, minimal human intervention** — an AI agent using SciTeX completed a full research cycle: literature search, statistical analysis, publication-ready figures, a 21-page manuscript, and peer review simulation. More demos are available at [https://scitex.ai/demos/](https://scitex.ai/demos/).





  

    SciTeX Demo

  




## Installation



```bash

# Recommended — uv resolver, ~3 min (10–30× faster than pip on scitex[all])

uv pip install "scitex[all]"



# Plain pip works but expect ~30–90 min — pip's resolver backtracks

# heavily on the full extras set. See Installation Tips below.

pip install "scitex[all]"

```



> **Why uv?** `scitex[all]` pulls a large transitive set

> (numpy/pandas/torch/jax/playwright/openalex-local/sphinx-rtd-theme/…).

> pip's serial resolver walks version histories trying to satisfy

> every constraint and can spend 30+ min just downloading metadata

> before installing a single wheel. uv resolves the same set in

> parallel in 1–3 min. Install uv once with

> `pip install uv` (or `curl -LsSf https://astral.sh/uv/install.sh | sh`).



Per-module extras



```bash

pip install scitex                     # Core only (minimal)

pip install scitex[plt,stats,scholar]  # Typical research setup

pip install scitex[plt]                # Publication-ready figures (figrecipe)

pip install scitex[stats]              # Statistical testing (23+ tests)

pip install scitex[scholar]            # Literature search, PDF download, BibTeX enrichment

pip install scitex[writer]             # LaTeX manuscript compilation

pip install scitex[audio]              # Text-to-speech

pip install scitex[ai]                 # LLM APIs (OpenAI, Anthropic, Google) + ML tools

pip install scitex[dataset]            # Scientific datasets (DANDI, OpenNeuro, PhysioNet)

pip install scitex[browser]            # Web automation (Playwright)

pip install scitex[capture]            # Screenshot capture and monitoring

pip install scitex[cloud]              # Cloud platform integration

```



Requires Python 3.10+. Prefix any of the above with `uv ` (e.g. `uv pip install scitex[plt,stats,scholar]`) for a 10–30× faster resolve.



Installation Tips — timeouts, mirrors, [all] size



`scitex[all]` pulls the full 33-package ecosystem plus heavy extras (playwright browsers, torch, jax, pymupdf, Apptainer/Docker integrations, etc.). With **plain pip** this takes **30–90 minutes** because pip's resolver thrashes on the transitive set; with **uv** it takes ~3 min. Recommended order of preference:



```bash

# 1. uv (recommended) — parallel Rust resolver, 10-30× faster

pip install uv && uv pip install "scitex[all]"



# 2. pip with extended timeouts (default 15s aborts mid-wheel on slow links)

pip install --timeout 600 --retries 5 "scitex[all]"



# 3. Install in groups if a single run keeps failing

uv pip install scitex[io,stats,plt]         # core analysis layer first

uv pip install scitex[scholar,writer]       # research layer

uv pip install scitex[audio,browser,dataset,cloud]   # heavy extras last



# 4. Mirror — for networks where pypi.org is unreliable

uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple "scitex[all]"

```



If a single dep hangs, identify it with `pip install -v` and install that package alone with `--no-deps`, then resume the full install.



Module Overview



| Category | Modules | Description |

|----------|---------|-------------|

| **Core** | `session`, `io`, `config`, `clew` | Experiment tracking, file I/O, config, cryptographic verification |

| **Analysis** | `stats`, `plt`, `dsp`, `linalg` | Statistics, plotting, signal processing, linear algebra |

| **Research** | `scholar`, `writer`, `diagram`, `canvas` | Literature, manuscripts, diagrams, figure composition |

| **ML/AI** | `ai`, `nn`, `torch`, `cv`, `benchmark` | LLM APIs, neural networks, PyTorch, computer vision |

| **Data** | `pd`, `db`, `dataset`, `schema` | Pandas utilities, databases, scientific datasets |

| **Infra** | `app`, `cloud`, `tunnel`, `container` | App SDK, cloud, SSH tunnels, containers |

| **Automation** | `browser`, `capture`, `audio`, `notification` | Web automation, screenshots, TTS, notifications |

| **Dev** | `dev`, `template`, `linter`, `introspect` | Ecosystem tools, scaffolding, code analysis |



## Architecture — Packages (3-Layer Cascade)



The 33-package ecosystem follows a strict **dependency cascade**: upstream imports middle imports downstream, never the reverse. Downstream apps must work standalone; the umbrella only orchestrates.



```

Upstream (orchestration — SOC, integration tests only)

    scitex (scitex-python), scitex-cloud

        │ imports / re-exposes

        ▼

Middle (shared infrastructure — wraps, doesn't replace)

    scitex-io, scitex-stats, scitex-app, scitex-ui, scitex-audio, scitex-dev

        │ integrates / wraps via plugin registry

        ▼

Downstream (standalone apps — own IO/GUI, unit tests)

    figrecipe, scitex-writer, scitex-scholar, scitex-clew, scitex-notebook,

    scitex-dataset, scitex-ssh, scitex-container, scitex-browser, scitex-linter,

    openalex-local, crossref-local, socialia, + utility leaves

    (scitex-{path,str,dict,logging,types,db,repro,audit,parallel,compat,gists,etc,core})

```



**One-line contract**: downstream does not know upstream exists; upstream does not duplicate downstream logic. See [01_ecosystem_01_upstream-and-downstream.md](https://github.com/ywatanabe1989/scitex-dev/blob/main/src/scitex_dev/_skills/general/01_ecosystem_01_upstream-and-downstream.md) for full rules (testing, cascade, interfaces) and [01_ecosystem_02_dependency-and-version-pinning.md](https://github.com/ywatanabe1989/scitex-dev/blob/main/src/scitex_dev/_skills/general/01_ecosystem_02_dependency-and-version-pinning.md) for dep-pinning.



## Quick Start



@scitex.session -- Reproducible Experiment Tracking



One decorator gives you: auto-CLI, YAML config injection, random seed fixation, structured output, and logging.



```python

import scitex as stx

import numpy as np



@stx.session

def main(

    data_path: str = "./data.csv",   # --data-path data.csv

    n_samples: int = 100,            # --n-samples 200

    CONFIG=stx.session.INJECTED,     # Aggregated ./config/*.yaml

    plt=stx.session.INJECTED,        # Pre-configured matplotlib

    logger=stx.session.INJECTED,     # Session logger

):

    """Analyze data. Docstring becomes --help text."""

    

    # Load

    data = stx.io.load(data_path)

    

    # Demo data

    x = np.linspace(0, 2 * np.pi, n_samples)

    y = np.sin(x) + np.random.randn(n_samples) * 0.1

    

    # FigRecipe Plot

    fig, ax = stx.plt.subplots()

    ax.plot(x, y)

    ax.set_xyt("Time", "Amplitude", "Noisy Sine Wave")

    

    # Save sine.png + sine.csv with logging message

    stx.io.save(fig, "sine.png")

    

    return 0



if __name__ == "__main__":

    main()

```



```bash

$ python script.py --data-path experiment.csv --n-samples 200

$ python script.py --help

# usage: script.py [-h] [--data-path DATA_PATH] [--n-samples N_SAMPLES]

# Analyze data. Docstring becomes --help text.

```



```

script_out/FINISHED_SUCCESS/2026-03-18_14-30-00_Z5MR/

├── sine.png, sine.csv         # Figure + auto-exported plot data

├── CONFIGS/CONFIG.yaml        # Frozen parameters

└── logs/{stdout,stderr}.log   # Execution logs

```



The injected `CONFIG` is a `DotDict` merging YAML user configs with session-resolved keys:



| Key | Meaning |

|-----|---------|

| `CONFIG.ID` | Session identifier, e.g. `2026-04-23T21-30-00_Z5MR` |

| `CONFIG.PID` | Python process ID |

| `CONFIG.START_DATETIME` | When the session started |

| `CONFIG.FILE` | Path to caller script |

| `CONFIG.SDIR_OUT` | Base output dir, e.g. `analysis_out/` |

| `CONFIG.SDIR_RUN` | This run's dir, e.g. `analysis_out/FINISHED_SUCCESS//` |

| `CONFIG.ARGS` | Parsed CLI args |

| `CONFIG.MODEL.*` | Values from `./config/MODEL.yaml` (one namespace per YAML file) |



Use `CONFIG.SDIR_RUN / "results.csv"` to re-load a file saved earlier in the same session. A frozen copy of `CONFIG` is persisted to `CONFIG.SDIR_RUN/CONFIGS/{CONFIG.yaml,CONFIG.pkl}` so any run is fully auditable. See the [Session config docs](https://scitex-python.readthedocs.io/en/latest/api/session.html) for the full reference.



scitex.io -- Unified File I/O (50+ Formats)



```python

import scitex as stx



# Save and load -- format detected from extension.

# symlink_from_cwd=True drops a symlink at cwd so round-trip by filename works;

# without it, save() routes to _out/ and load() must use an absolute path.

stx.io.save(df, "results.csv", symlink_from_cwd=True)

df = stx.io.load("results.csv")



stx.io.save(arr, "data.npy", symlink_from_cwd=True)

arr = stx.io.load("data.npy")



stx.io.save(fig, "figure.png")       # Also exports figure data as CSV

stx.io.save(config, "config.yaml")

stx.io.save(model, "model.pkl")



# Aggregate ./config/*.yaml into a single DotDict

CONFIG = stx.io.load_configs(config_dir="./config")

print(CONFIG.MODEL.hidden_size)      # Dot-notation access



# Register custom formats

@stx.io.register_saver(".custom")

def save_custom(obj, path, **kw):

    with open(path, "w") as f:

        f.write(str(obj))



@stx.io.register_loader(".custom")

def load_custom(path, **kw):

    with open(path) as f:

        return f.read()

```



Supports: CSV, JSON, YAML, TOML, HDF5, NPY, NPZ, PKL, PNG, JPG, SVG, PDF, Excel, Parquet, Zarr, INI, TXT, MAT, WAV, MP3, BibTeX, and more.



**Built-in features**: Auto directory creation, path resolution to `<script_name>_out/`, symlinks (`symlink_from_cwd=True`), save logging with file size, and Clew hash tracking.

</details>



<details>

<summary><strong><code>scitex.plt</code> -- Reproducible, Restylable Figures</strong></summary>



Powered by [figrecipe](https://github.com/ywatanabe1989/figrecipe). Figures are **reproducible nodes** in the Clew verification DAG -- scientific data and visual style are decomposed, so figures can be restyled (fonts, colors, layout) without altering the underlying data hash. Every figure auto-exports its data as CSV + a YAML recipe for exact reproduction.



```python

import scitex as stx

fig, axes = stx.plt.subplots(1, 3)

axes[0].stx_line(x, y)

axes[0].set_xyt("Time", "Value", "Line")



axes[1].stx_violin([g1, g2, g3])

axes[1].set_xyt("Group", "Score", "Violin")



axes[2].stx_heatmap(corr_matrix)

axes[2].set_xyt("X", "Y", "Heatmap")

stx.io.save(fig, "analysis.png")  # Saves analysis.png + analysis.csv + analysis.yaml



# Restyle without changing data (hash stays valid for Clew verification)

stx.plt.reproduce("analysis.yaml", style="nature")

```

</details>



<details>

<summary><strong><code>scitex.stats</code> -- Publication-Ready Statistics (23+ Tests)</strong></summary>



```python

import scitex as stx

result = stx.stats.run_test("ttest_ind", group1, group2, return_as="dataframe")

# Returns: p-value, effect size (Cohen's d), CI, normality check, power

recommendations = stx.stats.recommend_tests(data)

stx.stats.annotate(ax, test=result, style="apa")   # stars + "t(58) = 2.34, p = .021, d = 0.60" on a matplotlib Axes

```

</details>



<details>

<summary><strong><code>scitex.scholar</code> -- Literature Management</strong></summary>



Search, download, enrich papers. Backed by local CrossRef (167M+) and OpenAlex (250M+) databases.



```python

import scitex as stx

scholar = stx.scholar.Scholar()                             # lazy-load library

papers = scholar.process_papers(["neural oscillations working memory"])

scholar.download_pdfs_from_dois(["10.1038/s41586-024-07804-3"])

scholar.enrich_papers(bibtex_path="references.bib")

```



```bash

scitex scholar crossref-scitex search "neural oscillations" --abstracts

scitex scholar fetch --from-bibtex references.bib --project myproject

```

</details>



<details>

<summary><strong><code>scitex.writer</code> -- LaTeX Manuscript Compilation</strong></summary>



```python

import scitex as stx

stx.writer.compile.manuscript("paper/")                     # latexmk wrapper

stx.writer.figures.add("paper/", "results.png", caption="Main results")

stx.writer.tables.add("paper/", "stats.csv", caption="Statistical summary")

```

</details>



<details>

<summary><strong><code>scitex.notification</code> -- Multi-Backend Notifications</strong></summary>



Get notified when experiments finish -- via desktop, phone call, SMS, or email -- with automatic fallback.



```python

import scitex as stx

stx.notification.alert("Experiment complete: accuracy = 94.2%")

stx.notification.call("Training diverged -- loss is NaN")

stx.notification.sms("GPU job finished on node-42")



@stx.session(notify=True)   # Notifies on completion or failure

def main(CONFIG=stx.session.INJECTED): ...

```

</details>



<details>

<summary><strong><code>scitex.clew</code> -- Cryptographic Verification for AI-Driven Science</strong></summary>



As AI agents produce research at scale, the question shifts from *"could this be reproduced?"* to *"has this been verified?"*. Clew builds a **SHA-256 hash-chain DAG** linking every manuscript claim back to source data.



```python

import scitex as stx



# Every stx.io.load/save automatically records file hashes -- zero config

stx.clew.status()                          # {'verified': 12, 'mismatched': 0, 'missing': 0}

stx.clew.chain("results/figure1.png")      # Trace one file back to source data

stx.clew.dag(claims=True)                  # Verify all manuscript claims



# Register traceable assertions

stx.clew.add_claim(

    file_path="paper/main.tex", claim_type="statistic", line_number=142,

    claim_value="t(58) = 2.34, p = .021",

    source_session="2026-03-18_14-30-00_Z5MR", source_file="results/stats.csv",

)



stx.clew.mermaid(claims=True)              # Visualize provenance DAG

```



| Mode | Function | Answers |

|------|----------|---------|

| **Project** | `clew.dag()` | Is the whole project intact? |

| **File** | `clew.chain("output.csv")` | Can I trust this specific file? |

| **Claim** | `clew.verify_claim("Fig 1")` | Is this manuscript assertion valid? |



**L1** hash comparison (ms) / **L2** sandbox re-execution (min) / **L3** registered timestamp proof (optional).



<p align="center">

  <img src="docs/clew-dag.png" alt="Clew DAG" width="300">

</p>

<p align="center"><sub><b>Figure 2.</b> Clew verification DAG -- green nodes are verified (hash match), red nodes have mismatches. Each node shows its SHA-256 hash prefix.</sub></p>



</details>



<details>

<summary><strong><code>scitex.audio</code> -- Text-to-Speech (ElevenLabs / LuxTTS / gTTS / pyttsx3)</strong></summary>



```python

import scitex as stx

stx.audio.speak("Training complete. Accuracy ninety-four percent.")

stx.audio.speak("Offline only", backend="pyttsx3")                  # force offline

stx.audio.speak("Report", output_path="report.mp3", play=False)     # TTS → file

```

Backends fall back automatically: ElevenLabs (paid, highest) → LuxTTS (offline, 48 kHz, voice-cloning) → gTTS (free online) → pyttsx3 (offline espeak).

</details>



<details>

<summary><strong><code>scitex.dataset</code> -- OpenNeuro / DANDI / PhysioNet / Zenodo Fetcher</strong></summary>



```python

import scitex as stx

ds = stx.dataset.neuroscience.openneuro.fetch_all_datasets(max_datasets=10)

stx.dataset.neuroscience.dandi.fetch_all_datasets(max_datasets=10)

hits = stx.dataset.search_datasets(ds, text_query="phase-amplitude coupling")

```

Uniform API across neuroscience / biomedical / clinical-trial repositories.

</details>



<details>

<summary><strong><code>scitex.container</code> -- Apptainer / Docker Management</strong></summary>



```python

import scitex as stx

stx.container.apptainer.build(def_name="recipe")        # versioned SIF

stx.container.apptainer.switch_version("2.19.5")        # atomic active-SIF flip

stx.container.apptainer.rollback()                      # revert to previous

snap = stx.container.env_snapshot()                     # full env for papers

```

Reproducible HPC containers — build, version, rollback, env-snapshot for manuscripts.

</details>



<details>

<summary><strong><code>scitex.tunnel</code> -- Persistent SSH Reverse Tunnels</strong></summary>



```python

import scitex as stx

stx.tunnel.setup(port=8888, bastion_server="gw.example.com")

stx.tunnel.status()                                     # {"8888": "active"}

```

NAT traversal for lab machines — autossh-backed systemd service.

</details>



<details>

<summary><strong><code>scitex.linter</code> -- 47-Rule Convention Checker</strong></summary>



```python

import scitex as stx

issues = stx.linter.lint_file("src/")

for i in issues:

    print(f"{i.filepath}:{i.line} [{i.rule.id}] {i.message}")

```

Lints SciTeX projects for ecosystem conventions (`stx.io.save` usage, CONFIGS naming, matplotlib prefs, import hygiene). Complements ruff/flake8.

</details>



<details>

<summary><strong><code>scitex.repro</code> -- Seed Everything + Array Hashing</strong></summary>



```python

import scitex as stx

rng = stx.repro.RandomStateManager(seed=42)             # seeds random + numpy + torch + tf

run_id = stx.repro.gen_ID()                             # "20260423_2155_abc12345"

digest = stx.repro.hash_array(np_array)                 # deterministic SHA

```

One call seeds every RNG; generates experiment-run IDs; hashes arrays for fingerprinting.

</details>



<details>

<summary><strong><code>scitex.parallel</code> -- Threaded Map with tqdm</strong></summary>



```python

import scitex as stx

results = stx.parallel.run(download, [(u,) for u in urls], n_jobs=-1)

```

Drop-in parallel map for I/O-bound work — HTTP fetches, file reads, API calls. tqdm progress bar built-in.

</details>



<details>

<summary><strong><code>scitex.path</code> -- Project-Aware Paths &amp; Session Dirs</strong></summary>



```python

import scitex as stx

root = stx.path.find_git_root()                     # walk up for .git/

out = stx.path.get_spath("results.csv")             # → {script}_out/results.csv

stx.path.create_relative_symlink(src, dst)          # relative (portable) symlink

latest = stx.path.find_latest(".", "model_", ".pt") # model_v003.pt (highest version)

stx.path.fix_broken_symlinks("dir/", remove=True)   # cleanup dangling links

```

Auto-routes saves to `{script}_out/` and resolves session-scoped paths so `@stx.session` scripts produce dated, hash-trackable output dirs with no boilerplate.

</details>



<details>

<summary><strong><code>scitex.logging</code> -- Extended Logging + Exception Hierarchy + Tee</strong></summary>



```python

import scitex as stx

logger = stx.logging.getLogger(__name__)

logger.success("Training converged at epoch 87")    # SUCCESS level (custom)

logger.fail("Validation loss diverged")             # FAIL level (custom)



# Structured warnings with SciTeX categories

stx.logging.warn_deprecated("old_api", replacement="new_api", version="3.0")

stx.logging.warn_data_loss("NaN values dropped in column 'bp'")



# Typed exceptions (30+ subclasses of SciTeXError)

raise stx.logging.ShapeError("expected (N, 2), got (N, 3)")



# Tee stdout/stderr to a log file

with stx.logging.Tee("run.log"):

    main()                                           # prints go to screen + file

```

Extends stdlib `logging` with SUCCESS/FAIL levels, a 30+ class exception tree (`IOError`/`ShapeError`/`ConfigKeyError`/...), structured warning categories, and tee-to-file. `SCITEX_LOGGING_LEVEL` env var sets default at import.

</details>



<details>

<summary><strong><code>scitex.db</code> -- SQLite3 / PostgreSQL with ndarray BLOB Storage</strong></summary>



```python

import scitex as stx, numpy as np



db = stx.db.SQLite3("experiments.db")

with db:                                             # context-manager transaction

    db.execute("CREATE TABLE IF NOT EXISTS runs (id TEXT, acc REAL)")

    db.save_array("weights_epoch_87", np.random.rand(1024, 1024))   # compressed BLOB



df = db.to_df("runs")                                # pandas round-trip

w = db.load_array("weights_epoch_87")                # typed ndarray back

db.check_health()                                    # integrity + schema drift

stx.db.delete_duplicates(conn, "runs", columns=["id"])

```

SQLite / PostgreSQL clients with first-class compressed-ndarray BLOBs, dataframe round-trips, health checks, and duplicate removal. Drop-in replacement for hand-rolling `pickle → BLOB` storage or SQLAlchemy Core when you don't need an ORM.

</details>



<details>

<summary><strong><code>scitex.browser</code> -- Playwright Helpers for Scientific Scraping</strong></summary>



```python

import scitex as stx, asyncio



async def grab_pdf():

    async with stx.browser.SyncBrowserSession() as session:

        page = await session.new_page()

        await page.goto("https://journal.example/article/123")

        await stx.browser.click_with_fallbacks_async(

            page, ["button.download-pdf", "a[href$='.pdf']"]   # fall through selectors

        )

        await stx.browser.save_as_pdf_async(page, "article.pdf")



asyncio.run(grab_pdf())

```

Playwright wrappers with: Chrome-PDF-viewer download helper, popup/cookie dismissers (`close_popups_async`, `PopupHandler`), cursor/click/step overlays for debug video recording, console-log collectors, test-failure artifact capture. Drop-in replacement for raw Playwright scripts + stealth plugins.

</details>



<details>

<summary><strong>Utility modules — lower-level helpers</strong></summary>



| Module | Purpose | Key API |

|--------|---------|---------|

| `stx.str` | Text / LaTeX fallback / colored prints | `printc`, `safe_latex_render`, `grep` |

| `stx.dict` | `DotDict` + safe merge / flatten | `DotDict`, `safe_merge`, `flatten` |

| `stx.types` | Union type aliases + predicates | `ArrayLike`, `ColorLike`, `is_array_like` |

| `stx.audit` | Unified security scan (bandit / shellcheck / pip-audit) | `audit()` |

| `stx.compat` | Deprecation shims | `@deprecated`, `notify` legacy alias |

| `stx.etc` | Terminal keypress helpers | `wait_key`, `count` |



See [docs/05_ADDITIONAL_MODULES.md](./docs/05_ADDITIONAL_MODULES.md) for full examples.

</details>



> **[Agentic usage](./docs/06_AGENTIC_USAGE.md)** — MCP setup, example prompts, real one-shot outputs, and skill-trigger testing.



> **[Full API reference](https://scitex-python.readthedocs.io/en/latest/api/index.html)** &middot; **[Examples](./examples/)** &middot; **[Module status](./docs/04_MODULE_STATUS.md)**



<details>

<summary><strong>CLI Commands</strong></summary>



```bash

scitex --help-recursive                  # Show all commands

scitex scholar crossref-scitex search "topic"   # Search literature (CrossRef 167M+)

scitex scholar fetch "10.1038/..."       # Download paper by DOI

scitex stats recommend                   # Suggest statistical tests

scitex clew status                       # Project verification overview

scitex clew dag --claims                 # Verify all manuscript claims

scitex audio speak "Analysis complete"   # Text-to-speech

scitex notification send "Job finished"  # Multi-backend notification

scitex template clone research my_proj   # Scaffold a project

scitex dev ecosystem list                # Check ecosystem versions

scitex mcp list-tools                    # List all MCP tools (323)

```



> **[Full CLI reference](./docs/01_CLI_COMMANDS.md)**

</details>



<details>

<summary><strong>MCP Server (323 tools across 23 modules)</strong></summary>



Turn AI agents into autonomous researchers via [MCP](https://modelcontextprotocol.io/).



| Category | Tools | | Category | Tools | | Category | Tools |

|----------|-------|-|----------|-------|-|----------|-------|

| plt | 73 | | crossref | 15 | | io | 5 |

| cloud | 50 | | dev | 13 | | template | 4 |

| writer | 38 | | introspect | 12 | | openalex | 4 |

| scholar | 22 | | stats | 10 | | linter | 3 |

| clew | 9 | | dataset | 8 | | social | 3 |

| project | 6 | | notify | 5 | | tunnel | 3 |

| docs | 4 | | ui | 2 | | usage | 2 |



```json

{"mcpServers": {"scitex": {"command": "scitex", "args": ["mcp", "start"],

  "env": {"SCITEX_ENV_SRC": "${SCITEX_ENV_SRC}"}}}}

```



> **[Full MCP reference](./docs/02_MCP_TOOLS.md)**

</details>



## Configuration



```bash

cp -r .env.d.examples .env.d   # 1. Copy examples

$EDITOR .env.d/                # 2. Edit credentials

source .env.d/entry.src        # 3. Source in shell

```



> **[Full configuration reference](./.env.d.examples/README.md)**



## SciTeX Ecosystem



[`scitex-cloud`](https://github.com/ywatanabe1989/scitex-cloud) is a self-hosted web application that serves as a collaborative research workspace — with a built-in Writer, Scholar, and App Store where researchers build custom tools using [`scitex-app`](https://github.com/ywatanabe1989/scitex-app) SDK and [`scitex-ui`](https://github.com/ywatanabe1989/scitex-ui) components, then share them with the community. A live instance is hosted at [scitex.ai](https://scitex.ai).



<!-- hook-bypass: line-limit -->

<details>

<summary><strong>Full Ecosystem (37 packages, grouped by primary interface)</strong></summary>



Each package exposes the ecosystem via up to six interfaces: Python library, CLI, MCP tools, Claude Code skills, hooks, and HTTP. Ratings: ⭐⭐⭐ = primary / canonical surface, ⭐⭐ = strong secondary, ⭐ = thin, — = not provided. Packages are grouped by their *primary* interface — the one users should reach for first.



### Python-first (library API is primary)



| Package | Module | Interfaces | Description |

|---------|--------|-----------|-------------|

| [crossref-local](https://github.com/ywatanabe1989/crossref-local) | `stx.scholar` | Py ⭐⭐⭐ · CLI ⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Offline, zero-API-key DOI lookup + full-text search over the CrossRef corpus |

| [openalex-local](https://github.com/ywatanabe1989/openalex-local) | `stx.scholar` | Py ⭐⭐⭐ · CLI ⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Offline, zero-API-key search over the full OpenAlex academic corpus |

| [scitex-browser](https://github.com/ywatanabe1989/scitex-browser) | `stx.browser` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Playwright wrappers for scientific web scraping + AI-agent browsing |

| [scitex-compat](https://github.com/ywatanabe1989/scitex-compat) | `stx.compat` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐ · Hook — · HTTP — | Backward-compatibility shims for deprecated SciTeX APIs |

| [scitex-core](https://github.com/ywatanabe1989/scitex-core) | `stx.core` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Foundation layer for the SciTeX ecosystem |

| [scitex-dataset](https://github.com/ywatanabe1989/scitex-dataset) | `stx.dataset` | Py ⭐⭐⭐ · CLI ⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Unified dataset-discovery API across 7 scientific repositories |

| [scitex-db](https://github.com/ywatanabe1989/scitex-db) | `stx.db` | Py ⭐⭐⭐ · CLI ⭐ · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Relational-DB wrapper for scientific Python |

| [scitex-dict](https://github.com/ywatanabe1989/scitex-dict) | `stx.dict` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐ · Hook — · HTTP — | Dictionary utilities for scientific Python |

| [scitex-etc](https://github.com/ywatanabe1989/scitex-etc) | `stx.etc` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐ · Hook — · HTTP — | Miscellaneous SciTeX utilities |

| [scitex-gists](https://github.com/ywatanabe1989/scitex-gists) | `stx.gists` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐ · Hook — · HTTP — | SigmaPlot v12 macro snippets as printable Python functions |

| [scitex-logging](https://github.com/ywatanabe1989/scitex-logging) | `stx.logging` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Enhanced Python logging + warnings + exceptions for SciTeX |

| [scitex-parallel](https://github.com/ywatanabe1989/scitex-parallel) | `stx.parallel` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐ · Hook — · HTTP — | Minimal thread-pool parallel execution for scientific Python |

| [scitex-path](https://github.com/ywatanabe1989/scitex-path) | `stx.path` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Project-aware path utilities for scientific Python |

| [scitex-plt](https://github.com/ywatanabe1989/scitex-plt) | `stx.plt` | Py ⭐⭐⭐ · CLI — · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Publication-ready plotting (thin wrapper around figrecipe) |

| [scitex-repro](https://github.com/ywatanabe1989/scitex-repro) | `stx.repro` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Reproducibility helpers for scientific Python experiments |

| [scitex-stats](https://github.com/ywatanabe1989/scitex-stats) | `stx.stats` | Py ⭐⭐⭐ · CLI ⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Publication-ready statistical testing for 23 tests |

| [scitex-str](https://github.com/ywatanabe1989/scitex-str) | `stx.str` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Text-processing utilities for scientific Python |

| [scitex-types](https://github.com/ywatanabe1989/scitex-types) | `stx.types` | Py ⭐⭐⭐ · CLI — · MCP — · Skills ⭐ · Hook — · HTTP — | Type aliases and runtime type guards for scientific Python |



### CLI-first



| Package | Module | Interfaces | Description |

|---------|--------|-----------|-------------|

| [scitex-agent-container](https://github.com/ywatanabe1989/scitex-agent-container) | `stx.agent_container` | Py ⭐⭐ · CLI ⭐⭐⭐ · MCP — · Skills ⭐⭐ · Hook — · HTTP — | Declarative YAML-based AI agent lifecycle management (tmux/screen/SSH) |

| [scitex-app](https://github.com/ywatanabe1989/scitex-app) | `stx.app` | Py ⭐⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | App-developer SDK for SciTeX workspace apps |

| [scitex-audit](https://github.com/ywatanabe1989/scitex-audit) | `stx.audit` | Py ⭐ · CLI ⭐⭐⭐ · MCP ⭐ · Skills ⭐ · Hook — · HTTP — | Unified repo security scanner for scientific Python projects |

| [scitex-clew](https://github.com/ywatanabe1989/scitex-clew) | `stx.clew` | Py ⭐⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Hash-based reproducibility verification for scientific pipelines |

| [scitex-container](https://github.com/ywatanabe1989/scitex-container) | `stx.container` | Py ⭐⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Unified container management for Apptainer/Singularity + Docker |

| [scitex-dev](https://github.com/ywatanabe1989/scitex-dev) | `stx.dev` | Py ⭐⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Developer utilities for maintaining the whole SciTeX ecosystem |

| [scitex-notebook](https://github.com/ywatanabe1989/scitex-notebook) | `stx.notebook` | Py ⭐⭐ · CLI ⭐⭐⭐ · MCP ⭐ · Skills ⭐⭐ · Hook — · HTTP — | Jupyter notebook reproducibility — verify, compile to DAG, convert to script |

| [scitex-ssh](https://github.com/ywatanabe1989/scitex-ssh) | `stx.tunnel` | Py ⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | SSH primitives (exec/copy/attach) plus gated, auto-reconnecting reverse tunnels for NAT traversal |



### MCP-first



| Package | Module | Interfaces | Description |

|---------|--------|-----------|-------------|

| [scitex-audio](https://github.com/ywatanabe1989/scitex-audio) | `stx.audio` | Py ⭐⭐ · CLI ⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Unified text-to-speech with automatic backend fallback |

| [socialia](https://github.com/ywatanabe1989/socialia) | `stx.social` | Py ⭐ · CLI ⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Unified posting + analytics client for 6 social platforms |



### Hook-first



| Package | Module | Interfaces | Description |

|---------|--------|-----------|-------------|

| [scitex-linter](https://github.com/ywatanabe1989/scitex-linter) | `stx.linter` | Py ⭐ · CLI ⭐⭐ · MCP ⭐ · Skills ⭐⭐ · Hook ⭐⭐⭐ · HTTP — | AST-based linter for reproducible-research Python (pre-commit hook) |



### Mixed (multiple equally-primary interfaces)



| Package | Module | Interfaces | Description |

|---------|--------|-----------|-------------|

| [figrecipe](https://github.com/ywatanabe1989/figrecipe) | `stx.plt` | Py ⭐⭐⭐ · CLI ⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | Publication-ready matplotlib figures with mm-precision layouts |

| [scitex-cloud](https://github.com/ywatanabe1989/scitex-cloud) | `stx.cloud` | Py ⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP ⭐⭐ | SciTeX Cloud operational surface (55 MCP tools) |

| [scitex-io](https://github.com/ywatanabe1989/scitex-io) | `stx.io` | Py ⭐⭐⭐ · CLI ⭐ · MCP ⭐⭐ · Skills ⭐⭐⭐ · Hook — · HTTP — | Universal one-call file I/O for 30+ scientific formats |

| [scitex-notification](https://github.com/ywatanabe1989/scitex-notification) | `stx.notification` | Py ⭐⭐ · CLI ⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | One-call alerting across 9 backends (audio/desktop/email/Telegram/...) |

| [scitex-orochi](https://github.com/ywatanabe1989/scitex-orochi) | `stx.orochi` | Py ⭐⭐ · CLI ⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP ⭐⭐ | Agent Communication Hub — real-time WebSocket messaging between agents |

| [scitex-scholar](https://github.com/ywatanabe1989/scitex-scholar) | `stx.scholar` | Py ⭐⭐⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | End-to-end scientific-literature toolkit |

| [scitex-ui](https://github.com/ywatanabe1989/scitex-ui) | `stx.ui` | Py ⭐⭐ · CLI ⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP ⭐⭐ | Shared frontend framework for SciTeX web apps |

| [scitex-writer](https://github.com/ywatanabe1989/scitex-writer) | `stx.writer` | Py ⭐ · CLI ⭐⭐⭐ · MCP ⭐⭐⭐ · Skills ⭐⭐ · Hook — · HTTP — | End-to-end LaTeX manuscript toolchain (45 MCP tools) |

</details>



>Four Freedoms for Research

>

>0. The freedom to **run** your research anywhere -- your machine, your terms.

>1. The freedom to **study** how every step works -- from raw data to final manuscript.

>2. The freedom to **redistribute** your workflows, not just your papers.

>3. The freedom to **modify** any module and share improvements with the community.

>

>AGPL-3.0 -- because research infrastructure deserves the same freedoms as the software it runs on.



---



<p align="center">

  <a href="https://star-history.com/#ywatanabe1989/scitex-python&Date">

    <img alt="Star History" src="https://api.star-history.com/svg?repos=ywatanabe1989/scitex-python&type=Date" />

  </a>

</p>



<p align="center">

  <a href="https://scitex.ai"><img src="docs/assets/images/scitex-icon-navy-inverted.png" alt="SciTeX" width="40"/></a>

</p>



<!-- EOF -->