zxyasfas/paper_format_agent/paper-format-agent

Claude Code and agent skill by zxyasfas/paper_format_agent

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Use this skill when a user needs to inspect, score, repair, or regression-test academic paper formatting in DOCX files, especially thesis, journal, or conference documents with a separate format specification. The skill guides Codex through local-first document handling, rule extraction, formatting repair, report review, and contribution-safe validation.

Install

npx vskill@latest install zxyasfas/paper_format_agent/paper-format-agent

SKILL.md

Paper Format Agent

Use this skill for local-first academic document formatting work. It is designed for .docx papers and a separate format specification in .docx, .doc, or .txt.

Safety Defaults

  • Never upload private papers or format guides to external services unless the user explicitly asks for it.
  • Treat student papers, manuscripts, reviewer comments, and school templates as sensitive data.
  • Do not change academic content unless the user explicitly opts in. Default to formatting-only changes.
  • Keep reports and generated documents in a user-provided output directory.

Standard Workflow

  1. Confirm the input files:
  • --format-file: format guide or template text.
  • --paper-file: source paper, currently .docx.
  • --out-dir: output directory for reports and generated documents.
  1. Run the CLI in formatting-only mode:
bash
python -m paper_format_agent.cli \
--format-file "format_guide.docx" \
--paper-file "paper.docx" \
--out-dir "./output" \
--engine auto \
--strict-required-sections
  1. Inspect generated artifacts:
  • formatted_paper_v3.docx: repaired document.
  • format_rules.json: extracted rules.
  • format_report.json: machine-readable score and checks.
  • format_report.html: human-readable report.
  • modify_log.json: formatting operations.
  • engine_report.json: post-processing engine result.
  1. Check the content guard fields in format_report.json:
  • content_changed should normally be false.
  • content_guard_enforced should normally be true.
  1. If a rule is wrong, update rule extraction or scoring logic and add a minimal test case.

Validation

Run these before handing work back:

bash
python tools/validate_skill.py
python -m unittest discover -s tests -p "test_*.py"
python tools/compile_check.py
python tools/release_audit.py

When Adding Template Support

  • Add the smallest representative rule text needed to reproduce the behavior.
  • Prefer deterministic rule extraction over LLM-only interpretation.
  • Add tests for extracted margins, font size, line spacing, required sections, headings, captions, or references.
  • Do not commit real student documents. Use synthetic text or anonymized fixtures.

When Reviewing Output

Read format_report.json first, then inspect format_report.html if the user needs a human-readable summary. Prioritize issues in this order:

  1. Content changed unexpectedly.
  2. Required sections or headings were misclassified.
  3. Page setup, margins, fonts, or line spacing are wrong.
  4. Captions, references, tables, or numbering look wrong.
  5. Report wording or score explanation is unclear.

Contribution-Friendly Tasks

Good first PRs usually fit one of these buckets:

  • Add a synthetic test for a school, journal, or conference formatting rule.
  • Improve a rule extractor with a narrowly scoped regex or parser.
  • Improve report clarity without changing scoring semantics.
  • Add a regression case to docs/regression_manifest.sample.json.
  • Improve this skill workflow or agents/openai.yaml metadata.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

  • SKILL.md in the repositoryCheckingSKILL.md

Popularity

Repository stars
81
Repository forks
7
7-day trend score
22

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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