<h1>OneCite</h1> <h3>Citation & Academic Reference Toolkit</h3> </div>

![Downloads](https://pepy.tech/project/onecite) ![Awesome CLI Apps](https://github.com/agarrharr/awesome-cli-apps?tab=readme-ov-file#academia)

![Tests](https://github.com/HzaCode/OneCite/actions) ![codecov](https://codecov.io/gh/HzaCode/OneCite) ![PyPI](https://pypi.org/project/onecite/) ![Python](https://www.python.org) ![MIT](LICENSE) ![Docs](https://hzacode.github.io/OneCite/) ![Awesome LaTeX](https://github.com/egeerardyn/awesome-LaTeX?tab=readme-ov-file#bibliography-tools)

</div>

<p align="center"> <a href="#-features">Features</a> • <a href="#-quick-start">Quick Start</a> • <a href="#-advanced-usage">📖 Advanced Usage</a> • <a href="#-roadmap">🗺️ Roadmap</a> • <a href="#-contributing">🤝 Contributing</a> </p>

---

<p align="center"> 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. </p>

---

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.

---

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

![CrossRef](https://www.crossref.org/) ![Semantic Scholar](https://www.semanticscholar.org/) ![PubMed](https://pubmed.ncbi.nlm.nih.gov/) ![arXiv](https://arxiv.org/) ![DataCite](https://datacite.org/) ![Zenodo](https://zenodo.org/) ![Google Books](https://books.google.com/) </div>

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: ```text

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

4. View Output

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

<details> <summary><strong>View Complete Output (results.bib)</strong></summary>

@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 ...

</details>

📖 Advanced Usage

<details> <summary><strong>Direct String and Stdin Input</strong></summary>

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

</details>

<details> <summary><strong>🐍 Use as a Python Library</strong></summary>

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']))

</details>

<details> <summary><strong>💻 CLI Commands & Options</strong></summary>

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: ``bash 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: ```bash

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: ``bash onecite --version ``

`onecite version`

Alternative command to display version information.

Usage: ``bash onecite version ``

`onecite templates`

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

Usage: ``bash 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: ``bash 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: ``bash 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: ``bash 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.

</details>

🗺️ Roadmap

[x] OneCite Skill — Repository-contained operating guide for local citation-cleanup workflows
[x] Benchmarking — Small deterministic regression suite, configurable pass-rate gate, and baseline record
[x] 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

<p> <a href="https://github.com/HzaCode/OneCite">Star on GitHub</a> • <a href="http://hezhiang.com/onecite">Web App</a> • <a href="https://github.com/HzaCode/OneCite/issues">🐛 Report an Issue</a> • <a href="https://github.com/HzaCode/OneCite/discussions">Discussions</a> </p>

</div>

OneCite

Features

🌐 Data Sources

Quick Start

1. Installation

2. Create an Input File

references.txt

Add blank lines between entries to avoid misidentification

3. Run OneCite

4. View Output

📖 Advanced Usage

`onecite process`

Process a text file

Process a BibTeX file with auto-detection

Use stdin

Process a direct string (DOI)

Process with custom template

Quiet mode for scripts

Automation-friendly JSON with unresolved-entry exit-code handling

Streaming NDJSON for automation

`onecite suggest`

`onecite --version`

`onecite version`

`onecite templates`

`onecite benchmark`

`onecite doctor`

OneCite Skill for Automated Workflows

Input Type Auto-Detection

Available Templates

Exit Codes

🗺️ Roadmap

🤝 Contributing

📄 License

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