OneCite

Citation & Academic Reference Toolkit

Features • Quick Start • 📖 Advanced Usage • 🗺️ Roadmap • 🤝 Contributing

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OneCite is a command-line tool and Python library for citation management. It resolves strong identifiers such as DOIs, PMIDs, arXiv IDs, ISBNs, GitHub URLs, and data DOIs into formatted bibliographic entries, while plain-text title searches are handled by the separate candidate-only suggest command.

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Researchers frequently accumulate reference lists in ad-hoc formats—DOIs copied from browser tabs, arXiv IDs from paper PDFs, PMIDs, ISBNs, software URLs, data DOIs, and BibTeX fragments from various sources. Cleaning these into consistent BibTeX output is tedious and error-prone. OneCite parses raw reference text and resolves strong identifiers against configured sources such as CrossRef, PubMed, arXiv, DataCite, GitHub, and Google Books. Plain-text title searches are exposed through onecite suggest so candidates can be reviewed without being mistaken for verified BibTeX. The result is a reproducible processing layer that reports unresolved entries and produces auditable BibTeX where metadata can be found.

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Features

Feature	Description
Candidate Suggestions	Search incomplete plain-text references with onecite suggest without resolving them to BibTeX.
Multiple Formats	Input .txt/.bib → Output BibTeX.
4-stage Pipeline	A 4-stage process (clean → query → validate → format) to produce consistent output.
Field Completion	Fill available fields returned by metadata sources, such as journal, volume, pages, authors, and abstract.
🎓 7+ Citation Types	Handles journal articles, conference papers, books, software, datasets, theses, and preprints.
Multi-Source Lookup	Uses source-specific routes for CrossRef, arXiv, PubMed, Semantic Scholar, Google Books, and others.
Many Identifier Types	Resolves DOI, PMID, arXiv ID, ISBN, GitHub URL, Zenodo DOI, and DataCite DOI inputs.
Custom Templates	YAML-based presets that provide a fallback BibTeX entry type when auto-detection is inconclusive.

🌐 Data Sources

Quick Start

Install and try OneCite in a few steps.

1. Installation

# Recommended: Install from PyPI
pip install onecite

2. Create an Input File

Create a file named references.txt with your mixed-format references:

# references.txt
# Add blank lines between entries to avoid misidentification

10.1038/nature14539

arXiv:1706.03762

ISBN:9780262035613

https://github.com/tensorflow/tensorflow

10.5281/zenodo.3233118

arXiv:2103.00020

Smith, J. (2020). Neural Architecture Search. PhD Thesis. Stanford University.

3. Run OneCite

Execute the command to process your file and generate a clean .bib output.

onecite process references.txt -o results.bib --quiet

4. View Output

Your results.bib file now contains entries of different types.

View Complete Output (results.bib)

@article{LeCun2015Deep,
  doi = "10.1038/nature14539",
  title = "Deep learning",
  author = "LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey",
  journal = "Nature",
  year = 2015,
  volume = 521,
  number = 7553,
  pages = "436-444",
  publisher = "Springer Science and Business Media LLC",
  url = "https://doi.org/10.1038/nature14539",
  type = "journal-article",
  abstract = "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction...",
}
@inproceedings{Vaswani2017Attention,
  arxiv = "1706.03762",
  title = "Attention Is All You Need",
  author = "Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia",
  year = 2017,
  booktitle = "Advances in Neural Information Processing Systems (NeurIPS)",
  url = "https://arxiv.org/abs/1706.03762",
}
# ... and 5 more entries ...

📖 Advanced Usage

Direct String and Stdin Input

onecite process "10.1038/nature14539"
onecite suggest "Attention is all you need, Vaswani et al., NIPS 2017"
echo "10.1038/nature14539" | onecite process -

🐍 Use as a Python Library

Use OneCite directly in your Python scripts.

from onecite import process_references

result = process_references(
    input_content="10.1038/nature14539",
    input_type="txt",
    template_name="journal_article_full",
    output_format="bibtex",
    interactive_callback=lambda candidates: -1
)

print('\n\n'.join(result['results']))

💻 CLI Commands & Options

OneCite provides a command-line interface with the following commands and options:

onecite process

The main command for processing references through the OneCite pipeline.

Usage:

onecite process <input_file> [OPTIONS]

Arguments:

• input_file - Input file path, - for stdin, or a strong identifier/reference string

Options:

Option	Short	Description	Default
--input-type		Input format: txt or bib	txt
--template		Fallback BibTeX entry-type preset when auto-detection is inconclusive	journal_article_full
--output-format		Output format (currently only bibtex supported)	bibtex
--output	-o	Output file path (default: stdout)	-
--quiet	-q	Suppress verbose logging output	False
--json		Print a stable JSON envelope instead of BibTeX text	False
--ndjson		Print newline-delimited JSON events for streaming automation workflows	False
--fail-on-unresolved		Return exit code 2 when any entry cannot be resolved	False

Examples:

# Process a text file
onecite process references.txt -o results.bib

# Process a BibTeX file with auto-detection
onecite process references.bib

# Use stdin
echo "10.1038/nature14539" | onecite process -

# Process a direct string (DOI)
onecite process "10.1038/nature14539"

# Process with custom template
onecite process references.txt --template conference_paper

# Quiet mode for scripts
onecite process references.txt -o results.bib --quiet

# Automation-friendly JSON with unresolved-entry exit-code handling
onecite process references.txt --json --fail-on-unresolved

# Streaming NDJSON for automation
onecite process references.txt --ndjson

onecite suggest

Search for candidate matches without producing BibTeX or returning a validation passed status.

onecite suggest "Attention is all you need, Vaswani et al., NIPS 2017" --json

Optional Google Scholar fallback. suggest accepts --google-scholar (requires the optional scholarly package: pip install onecite[scholar]). It is consulted only as a best-effort fallback when CrossRef and Semantic Scholar return nothing. Because it scrapes a service with no public API, it is off by default, may be rate-limited or blocked by a CAPTCHA, and is not guaranteed to be reproducible — it is exposed only on suggest (candidates for human review), never on process (authoritative output).

pip install onecite[scholar]
onecite suggest "some obscure title" --google-scholar

onecite --version

Display the installed OneCite version.

Usage:

onecite --version

onecite version

Alternative command to display version information.

Usage:

onecite version

onecite templates

List the bundled fallback BibTeX templates and the fields they request.

Usage:

onecite templates
onecite templates --json

onecite benchmark

Run a small deterministic regression suite for covered DOI lookup, arXiv lookup, PMID/PubMed lookup, GitHub software URLs, Zenodo/DataCite dataset DOIs, and mixed valid/invalid batches. The command is designed for CI and automation workflows that need a machine-readable pass/fail check; it is not a comprehensive citation-accuracy benchmark.

Usage:

onecite benchmark [OPTIONS]

Options:

Option	Description	Default
--cases	Path to a custom benchmark suite JSON file	bundled golden cases
--min-success-rate	Minimum covered-case pass rate required for exit code 0	1.0
--json	Print the benchmark report as JSON	False
--live	Use live external APIs instead of bundled offline fixtures	False

Examples:

onecite benchmark
onecite benchmark --json
onecite benchmark --live --json
onecite benchmark --cases my_cases.json --min-success-rate 1.0 --json

The repository baseline record is stored at benchmarks/leaderboard.json, with reproduction instructions in benchmarks/README.md.

onecite doctor

Check the local installation health for automation and CI. The doctor command checks package importability, bundled templates, packaged benchmark resources, the repository-contained OneCite Skill, and the offline benchmark regression check.

Usage:

onecite doctor
onecite doctor --json

The JSON output is a stable envelope with schema_version, tool, command, status, environment, summary, and checks fields.

OneCite Skill for Automated Workflows

The repository includes a local skill package at skills/onecite/SKILL.md. It gives automation and contributor workflows a repeatable procedure for reference cleanup, benchmark and doctor checks, and explicit reporting of unresolved entries. The skill is repository-contained and does not install itself into any local tool memory.

Input Type Auto-Detection

When --input-type is not specified, OneCite automatically detects the input type:

• Files ending with .bib are treated as BibTeX format
• All other files and strings are treated as plain text

Available Templates

OneCite supports several template presets for different entry types:

• journal_article_full - Full journal article entry (default)
• conference_paper - Conference proceedings paper
• book - Book entry
• thesis - Thesis/dissertation entry
• dataset - Dataset entry
• software - Software/code entry

Exit Codes

• 0 - Success
• 1 - Error occurred (invalid input, processing failure, etc.)
• 2 - One or more entries were unresolved when --fail-on-unresolved was used

For onecite benchmark and onecite doctor, exit code 0 means the configured checks passed and exit code 1 means at least one check failed.

🗺️ Roadmap

• OneCite Skill — Repository-contained operating guide for local citation-cleanup workflows
• Benchmarking — Small deterministic regression suite, configurable pass-rate gate, and baseline record
• Enhanced CLI — Automation-friendly JSON, NDJSON, summaries, and exit codes for reference processing

🤝 Contributing

Contributions are always welcome! Please see CONTRIBUTING.md for development guidelines and instructions on how to submit a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

OneCite

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