Citation Formatter

Workflow

above for related details. Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable. Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script. Primary implementation surface: scripts/main.py . Reference guidance: references/ contains supporting rules, prompts, or checklists. Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints. Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects. Quick Check Use this command to verify that the packaged script entry point can be parsed before deeper execution. python -m py_compile scripts/main.py Audit-Ready Commands Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths. python -m py_compile scripts/main.py python scripts/main.py -- help python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json Workflow Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions. Use the packaged script path or the documented reasoning path with only the inputs that are actually available. Return a structured result that separates assumptions, deliverables, risks, and unresolved items. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion. When to Use This Skill formatting references for journal submission converting between citation styles (APA, MLA, Vancouver, Chicago) generating bibliographies for manuscripts ensuring consistent reference formatting checking reference completeness and accuracy preparing grant proposal reference sections Quick Start from scripts.main import CitationFormatter

Initialize the tool

tool = CitationFormatter() from scripts.citation_formatter import CitationFormatter

formatter = CitationFormatter()

Format references for specific journal

formatted_refs = formatter.format_references( references=raw_references, target_style= "Nature Medicine" , output_format= "docx" )

Convert between styles

converted = formatter.convert_style( bibliography=apa_bibliography, from_style= "APA 7th" , to_style= "Vancouver" , include_doi= True , include_pmids= True )

Validate reference completeness

validation = formatter.validate_references( references=reference_list, required_fields=[ "authors" , "title" , "journal" , "year" , "volume" , "pages" , "doi" ] ) print ( f"Validation results:" ) print ( f" Complete: {validation.complete_count} " ) print ( f" Missing fields: {validation.incomplete_count} " ) print ( f" Invalid DOIs: { len (validation.invalid_dois)} " )

Generate in-text citations

in_text = formatter.generate_in_text_citations( citations=[ { "author" : "Smith" , "year" : 2023 , "type" : "paren" }, { "author" : "Jones et al." , "year" : 2022 , "type" : "narrative" } ], style= "APA" )

Batch process multiple documents

batch_results = formatter.batch_format( files=[ "chapter1.docx" , "chapter2.docx" ], style= "AMA" , output_dir= "formatted/" ) Core Capabilities

1. Format citations in 1000+ academic styles

Format functionality

result = tool.execute(data) 2. Convert seamlessly between citation formats

Convert functionality

result = tool.execute(data) 3. Validate reference completeness and accuracy

Validate functionality

result = tool.execute(data) 4. Batch process large reference collections

Batch functionality

result = tool.execute(data) Command Line Usage python scripts/main.py --input references.bib --from-style APA --to-style Vancouver --output formatted.docx --validate Best Practices Always validate DOIs and URLs before submission Check journal-specific requirements beyond standard style Maintain original reference database for updates Review formatting of special cases (websites, preprints) Quality Checklist Before using this skill, ensure you have: Clear understanding of your objectives Necessary input data prepared and validated Output requirements defined Reviewed relevant documentation After using this skill, verify: Results meet your quality standards Outputs are properly formatted Any errors or warnings have been addressed Results are documented appropriately References references/guide.md

• Comprehensive user guide references/examples/
• Working code examples references/api-docs/
• Complete API documentation Skill ID : 625 | Version : 1.0 | License : MIT Output Requirements Every final response should make these items explicit when they are relevant: Objective or requested deliverable Inputs used and assumptions introduced Workflow or decision path Core result, recommendation, or artifact Constraints, risks, caveats, or validation needs Unresolved items and next-step checks Error Handling If required inputs are missing, state exactly which fields are missing and request only the minimum additional information. If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment. If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback. Do not fabricate files, citations, data, search results, or execution outcomes. Input Validation This skill accepts requests that match the documented purpose of citation-formatter and include enough context to complete the workflow safely. Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond: citation-formatter only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill. Response Template Use the following fixed structure for non-trivial requests: Objective Inputs Received Assumptions Workflow Deliverable Risks and Limits Next Checks If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.