Write-Paper Skill

Write-Paper Skill

8-Phase Pipeline

Phase 0: Init

Gather essential information from the user before any writing begins.

Required inputs:

1. Title (working title is fine)
2. Paper type: original article, AI validation, case report, case series, meta-analysis, technical note, animal study, NHIS cohort, cross-national
3. Target journal: load profile from ${CLAUDE_SKILL_DIR}/references/journal_profiles/
4. Research question / hypothesis
5. Available data: what datasets, tables, analyses already exist

Optional flags:

• --no-llm-disclosure: skip the LLM writing-assistance disclosure. Default is ON.
• --autonomous: run Phases 0–7 without user gates (outline approval, T&F plan, discussion planning, section reviews all skipped). Default OFF.

Actions:

1. Load the journal profile. If none exists, ask for word limits, abstract format, citation style, figure/table limits, and special requirements.

2. Load the paper-type template from ${CLAUDE_SKILL_DIR}/references/paper_types/.

3. Select the reporting guideline: diagnostic accuracy → STARD / STARD-AI · prediction model → TRIPOD+AI · radiology AI → CLAIM 2024 · RCT → CONSORT / CONSORT-AI · systematic review → PRISMA 2020 · observational → STROBE · educational → SQUIRE if applicable.

4. AI/LLM design-stage reporting map (AI validation, LLM/MLLM, NLP extraction, report generation): map every required AI-reporting item to a manuscript section before drafting — model/version/access date, input fields, prompt or fine-tuning protocol, same-backbone zero-shot/few-shot baseline if an adaptation claim is made, test-data independence/contamination, repeatability, and the Methods subsection each will land in. If any item cannot be placed, halt for design clarification rather than burying it as a Phase 7 limitation.

5. Create or confirm the project scaffold directory.

6. Record the --no-llm-disclosure and --autonomous flag states for Phase 1–7 gate logic.

7. Identify a backbone article — scan manuscript/_src/refs.bib first and propose proactively (ranking: Backbone ranking below); ask only as a fallback. Record the chosen citekey in project.yaml::backbone_article. Then gate on its full text — a backbone whose full text is not extracted is a backbone in name only; the draft would follow an abstract:

   python3 ${CLAUDE_SKILL_DIR}/scripts/gate_backbone_fulltext.py \
     --project project.yaml --refs manuscript/_src/refs.bib \
     --fulltext-dir pdfs/ --strict
   

   A file in --fulltext-dir counts as the backbone when it is named <citekey>.md or names the backbone DOI/citekey before its References heading; a paper that only cites the backbone in its reference list does not count. Known limit: another paper that names the DOI in its own body still resolves — name the file <citekey>.md or pass --fulltext to remove the doubt.

   BACKBONE_FULLTEXT_MISSING / BACKBONE_FULLTEXT_THIN → stop and retrieve it (/lit-sync Phase 2.7, then /fulltext-retrieval pdf_to_md.py). Do not begin Methods drafting until this passes. If the article is genuinely unavailable in full text, record that limitation and get user confirmation before proceeding on the abstract alone.

8. Summarize the setup — journal constraints, paper type, reporting guideline, backbone article, directory path, LLM-disclosure status — and confirm before proceeding.

Phase 0 Gate: Citekey-only references