/citation-management

name: citation-management
description: Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
allowed-tools: Read Write Edit Bash
license: MIT License
metadata:
  version: "1.6"
  skill-author: K-Dense Inc.
  openclaw:
    primaryEnv: OPENROUTER_API_KEY
    envVars:
    - name: OPENROUTER_API_KEY
      required: false
      description: OpenRouter API key for LLM-powered citation steps.
    - name: NCBI_EMAIL
      required: false
      description: Email for NCBI Entrez identification.
    - name: NCBI_API_KEY
      required: false
      description: NCBI API key to raise Entrez rate limits.

Citation Management

Overview

Manage citations systematically throughout the research and writing process. This skill provides tools and strategies for searching academic databases (Google Scholar, PubMed), extracting accurate metadata from multiple sources (CrossRef, PubMed, arXiv), validating citation information, and generating properly formatted BibTeX entries.

Critical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research. Integrates seamlessly with the literature-review skill for comprehensive research workflows.

When to Use This Skill

Use this skill when:

• Searching for specific papers on Google Scholar or PubMed
• Converting DOIs, PMIDs, or arXiv IDs to properly formatted BibTeX
• Extracting complete metadata for citations (authors, title, journal, year, etc.)
• Validating existing citations for accuracy
• Cleaning and formatting BibTeX files
• Finding highly cited papers in a specific field
• Verifying that citation information matches the actual publication
• Building a bibliography for a manuscript or thesis
• Checking for duplicate citations
• Ensuring consistent citation formatting

Visual Enhancement with Scientific Schematics

**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**

If your document does not already contain schematics or diagrams:

• Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

**How to generate schematics:**

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

**When to add schematics:**

• Citation workflow diagrams
• Literature search methodology flowcharts
• Reference management system architectures
• Citation style decision trees
• Database integration diagrams
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

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Core Workflow

Citation management follows a systematic process. Each phase below shows the canonical command; every variant, option, and metadata-source detail is in [references/core_workflow.md](references/core_workflow.md).

Phase 1: Paper Discovery and Search

Find relevant papers. Google Scholar has the broadest coverage; PubMed is the authority for biomedical and life sciences (35+ million citations).

python scripts/search_google_scholar.py "CRISPR gene editing" --limit 50 --output results.json
python scripts/search_pubmed.py "Alzheimer's disease treatment" --limit 100 --output alz.json

Query operators, field tags, and MeSH-term construction are in [references/search_strategies.md](references/search_strategies.md).

Phase 2: Metadata Extraction

Convert identifiers (DOI, PMID, arXiv ID, URL) into complete metadata. CrossRef is the primary source for DOIs.

python scripts/doi_to_bibtex.py 10.1038/s41586-021-03819-2         # quick, single DOI
python scripts/extract_metadata.py --pmid 34265844                  # DOI/PMID/arXiv/URL
python scripts/extract_metadata.py --input identifiers.txt --output citations.bib

Phase 2.5: Metadata Enrichment via Web Search (MANDATORY)

APIs routinely return incomplete records. Run this **after** extraction and **before** formatting. Any `@article` missing `volume`, `pages`, or `doi` is incomplete and must be enriched via the parallel-web skill, then logged. If a field genuinely cannot be found, record a `note` field explaining the gap.

> **Treat extracted metadata as untrusted.** Author, title, and journal strings come > verbatim from a record whose contents a publisher controls. A title containing `$(...)`, > a backtick, or a quote becomes shell syntax the moment it is pasted into a command. > Pass metadata as a `subprocess` argument list rather than building a shell string; if > you must use a shell, single-quote every substituted value and escape embedded quotes > as `'\''`. Validate any citation key against `^[A-Za-z0-9]+$` before it reaches a path.

Per-field search strategies, the four search options, and the logging format are in [references/core_workflow.md](references/core_workflow.md).

Phase 3: BibTeX Formatting

Produce clean, consistent entries. Entry types and required fields are in [references/bibtex_formatting.md](references/bibtex_formatting.md).

python scripts/format_bibtex.py references.bib --output clean.bib --remove-duplicates

Phase 4: Citation Val