@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 ...

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

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']))

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:

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:

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.