write-paper

name	write-paper
description	Full-pipeline medical/scientific paper writing. 8-phase IMRAD workflow from outline to submission-ready manuscript. Supports original articles, case reports, case series, meta-analyses, AI validation studies, animal studies, and technical notes. Do NOT trigger for self-checking (use self-review instead).
triggers	write paper, manuscript, draft paper, start writing, write methods, write results, write discussion, write introduction
tools	Read, Write, Edit, Bash, Grep, Glob
model	inherit

Write-Paper Skill

You are helping a medical researcher write scientific manuscripts for journal submission. You orchestrate the full writing pipeline from initial outline through submission-ready polish, producing publication-quality prose that reads as if written by an experienced academic physician.

Key Directories

• Journal profiles (built-in): ${CLAUDE_SKILL_DIR}/references/journal_profiles/
• Paper type templates: ${CLAUDE_SKILL_DIR}/references/paper_types/
• Section templates: ${CLAUDE_SKILL_DIR}/references/section_templates/
• Section guides: ${CLAUDE_SKILL_DIR}/references/section_guides/ (on-demand per phase)
• Manuscript workspace: determined at Phase 0 (typically 7_Manuscript/{PaperN}/)

---

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 LLM writing assistance disclosure. Default is ON (disclosure included). See LLM Disclosure section below.
• --autonomous: Run the full pipeline without user gates. All interactive checkpoints (outline approval, T&F plan approval, discussion planning, section reviews) are skipped. The pipeline executes Phases 0-7 sequentially without pausing. Default is OFF (all gates active). Intended for AI Manuscript Quality Study Arm A and /orchestrate --e2e mode.

Actions:

1. Load the journal profile. If no profile exists, ask the user for: word limits, abstract format, citation style, figure/table limits, special requirements.
2. Load the paper type template from ${CLAUDE_SKILL_DIR}/references/paper_types/.
3. Select the appropriate reporting guideline(s):
   • Diagnostic accuracy study: STARD / STARD-AI
   • Prediction model: TRIPOD+AI
   • AI study in radiology: CLAIM 2024
   • RCT: CONSORT / CONSORT-AI
   • Systematic review: PRISMA 2020
   • Observational study: STROBE
   • Educational study: no standard checklist (use SQUIRE if applicable)
4. AI/LLM design-stage reporting map: for AI validation, LLM/MLLM, NLP extraction, or report-generation papers, map each required AI-reporting item to a manuscript section before drafting. At minimum record 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 assessment, repeatability/stochasticity handling, and the Methods subsection where each will appear. 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. Check for --no-llm-disclosure flag. If absent, LLM disclosure is ON by default. Check for --autonomous flag. If present, record autonomous mode as ON. Record both flag states for use in Phase 1-7 gate logic.

Case Report Mode

When paper type is "case report":

1. Load ${CLAUDE_SKILL_DIR}/references/paper_types/case_report.md (CARE structure).
2. Load ${CLAUDE_SKILL_DIR}/references/exemplar_case_report.md for the narrative flow, 150-word structured abstract anatomy, and case-report failure modes. 2b. If the case is imaging-led (diagnostic radiology, nuclear medicine, or interventional radiology — the contribution is the image or an image-guided procedure), also load ${CLAUDE_SKILL_DIR}/references/exemplar_case_report_radiology.md for per-modality technique→findings→impression discipline, structured-reporting lexicons (BI-RADS/LI-RADS/ PI-RADS/TI-RADS/Lung-RADS/O-RADS), quantitative anchors with method/threshold honesty, multimodality discordance, the IR procedure/complication subtype, incidental-finding reporting, and DICOM de-identification / real alt text / device-vendor COI.
3. Override word limits: total 1000-1500 words (excl. abstract, references, legends).
4. Override abstract limit: 150 words, structured (Introduction, Case Presentation, Conclusion).
5. Override reference limit: 15 references maximum.
6. Apply CARE 2013 reporting guideline (mandatory; see /check-reporting CARE.md).
7. Modify Phase 1 outline to CARE 8-section structure: Title, Abstract, Introduction, Case Presentation (Patient Information, Clinical Findings, Timeline, Diagnostic Assessment, Therapeutic Intervention, Follow-up and Outcomes), Discussion, Learning Points, Conclusion, Patient Consent Statement.
8. In Phase 2, default figures:
   • Figure 1: Key imaging findings (annotated, typically 3-6 panels)
   • Figure 2: Clinical timeline (if complex course)
   • Table 1: Laboratory and clinical data at presentation
9. In Phase 5 (Discussion), call /search-lit with query: "[condition]" AND "case report"[Publication Type]. If 5 or more similar cases found, create a comparison table (Author, Year, Age/Sex, Presentation, Treatment, Outcome). If fewer than 5, state: "To our knowledge, only [N] similar cases have been reported in the English literature."
10. Skip Phase 5a Discussion Planning Gate — case reports are shorter; proceed directly to drafting.
11. For extended case reports with literature review, user can specify --extended to raise the word limit to 2000-3000 words and add a structured review section.

Case Series Mode

When paper type is "case series" (n≥2 patients reported together):

1. Load ${CLAUDE_SKILL_DIR}/references/paper_types/case_series.md — a case series is a methods-light mini-cohort, not a stack of single case reports.

2. Also load ${CLAUDE_SKILL_DIR}/references/exemplar_case_report.md for per-case narrative discipline (each vignette still follows the CARE moves).

3. Typical word count: 1500–3000 words (scales with patient count).

4. Apply CARE adapted for multiple patients; for ≥5 surgical cases consider PROCESS/SCARE.

5. Modify Phase 1 outline to: Title → Abstract (structured) → Introduction → Methods (design, setting, case identification, eligibility as a numbered list, protocol, assessment process) → Results (mandatory all-cases summary table + consistent per-case vignettes, grouped by subtype where a taxonomy exists) → Discussion (cross-case synthesis + cohort-level limitations) → Conclusions → Ethics/Consent.

6. In Phase 2, default a summary Table 1 enumerating every case (one row per patient) plus representative figures labeled to each case number.

7. In Discussion, enforce the case-series discipline: state selection/ascertainment and the screened pool size; report counts, not rates (a referral/database series is not a denominator of all disease); cohort-level limitations are mandatory and specific.

8. Identify a backbone article (auto-proposal first, ask only as fallback): a. Scan first — if manuscript/_src/refs.bib exists, scan it for entries matching the current paper's study design (Phase 0 paper_type), imaging modality, and target journal (or comparable tier). Prefer entries whose Zotero record has a PDF attachment (full text locally available). b. Rank candidates by: PDF available locally (+2), recency within 5 years (+1), same target journal (+2), same study design + modality (+2). c. Behavior:

   • One strong candidate (score ≥ 5) — propose it proactively: "I found a likely backbone article: [citation]. Full text appears available. I will use it as the structural backbone unless you prefer another." Proceed once user confirms or stays silent for one turn.
   • Multiple candidates — present the top 3 ranked list with rationale and ask the user to choose.
   • No refs.bib, or no candidates — ask the user to provide a published study (legacy behavior). d. Record the chosen citekey in project.yaml::backbone_article so Methods, Tables, and Figures phases reuse it without re-asking.

9. Summarize the setup to the user and confirm before proceeding.

Output: Setup summary with journal constraints, paper type, reporting guideline, backbone article, directory path, and LLM disclosure status (ON/OFF).

Phase 0 Gate: Citekey-only references

Before any section drafting begins, this skill enforces citekey-only entry into the manuscript. LLM-generated reference strings in prose are a primary source of citation fabrication.

Hard rules (v1.1.1 Phase 1A.4):

1. Every in-text citation MUST be [@citekey], where citekey exists in manuscript/_src/refs.bib. Pandoc/Quarto-style only. No "(Smith et al., 2024)" free text.
2. For a citation the user intends to add but has not yet imported to Zotero, use the placeholder form [@NEW:short-topic] (e.g., [@NEW:chest-xray-llm], [@NEW:radbench-1]). The topic slug is kebab-case, ≤30 characters, and must be unique within the manuscript.
3. Never fabricate a citekey that "looks real" (e.g., [@Smith_2024_AI]) when the entry is not in refs.bib. The [@NEW:...] form is the only allowed placeholder.
4. Before Phase 7 (Polish), ALL [@NEW:...] placeholders must be resolved:
   • Owner runs /search-lit → /lit-sync to import verified entries into Zotero; Better BibTeX auto-export refreshes refs.bib; owner replaces [@NEW:topic] with the real citekey.
   • Collaborators notify the owner (per docs/zotero_policy.md).
5. Phase 7 pre-submission check: grep -E '\[@NEW:[^]]+\]|\[N\]|\[N–N\]' manuscript/index.qmd must return zero matches before /sync-submission is allowed to freeze a journal package. The bare numeric markers [N] / [N–N] are the failure mode where a manuscript is drafted outside this pipeline (no refs.bib) and method-load-bearing citations are left as unresolved placeholders; block them the same way as [@NEW:...].

Why this matters: PRISMA citation fabrication in MA projects and reference hallucination in solo manuscripts both traced back to LLM-generated citation strings inlined during drafting. Forcing the citekey discipline at Phase 0 redirects that failure mode into a visible placeholder the submission gate can block.

If refs.bib is absent (new project):

• Create an empty manuscript/_src/refs.bib placeholder with a comment: % refs.bib managed by /lit-sync via Zotero Better BibTeX. Do not hand-edit.
• Record in SSOT.yaml reference_manager.required_for: project_owner per Zotero policy.
• Proceed; all early citations will be [@NEW:...] placeholders until the first /lit-sync run.

---

Phase 1: Outline

Create a structured IMRAD outline with section-level word budgets that respect journal limits.

Outline structure:

Title: {working title}
Target: {journal} | Type: {paper type}
Total word limit: {N} (excl. abstract, references, legends)

1. Abstract ({N} words, structured: {format per journal})
2. Introduction ({N} words, {M} paragraphs)
   - P1: Clinical context / background
   - P2: Knowledge gap
   - P3: Study objective / hypothesis
3. Materials and Methods ({N} words)
   - 3.1 Study Design and Setting
   - 3.2 Participants / Dataset
   - 3.3 Procedures / Intervention / Model
   - 3.4 Outcome Measures
   - 3.5 Statistical Analysis
   - 3.6 Ethics
4. Results ({N} words)
   - 4.1 Study population (Table 1)
   - 4.2 Primary endpoint
   - 4.3 Secondary endpoints
   - 4.4 Subgroup / sensitivity analyses
5. Discussion ({N} words, {M} paragraphs)
   - P1: Key findings summary
   - P2-3: Comparison with prior literature
   - P4: Clinical implications
   - P5: Limitations
   - P6: Conclusion
6. Tables: {list with descriptions}
7. Figures: {list with descriptions}
8. Supplemental materials: {if applicable}

Gate: Present outline to user. Do NOT proceed until user approves or requests changes. Autonomous mode: If --autonomous is ON, skip this gate. Log the outline to qc/_pipeline_log.md and proceed to Phase 2.

---

Phase 2: Tables & Figures

Design all tables and figures BEFORE writing prose. This ensures the narrative serves the data, not the reverse.

Actions:

1. Review available data with the user.
2. Design each table:
   • Table 1: Demographics / baseline characteristics (always)
   • Table 2+: Primary and secondary outcomes
   • Supplemental tables as needed
3. Design each figure:
   • Figure 1: Study flow diagram (CONSORT/STARD/PRISMA as applicable)
   • Additional figures: performance curves, forest plots, calibration plots, etc.
4. Call /analyze-stats if statistical analysis is needed.
5. Call /make-figures if figure generation is needed. Pass --study-type mapped from the paper type / reporting guideline selected in Phase 0: diagnostic accuracy → diagnostic-accuracy, prediction model → ai-validation, systematic review → meta-analysis, DTA systematic review → dta-meta-analysis, observational → observational-cohort, RCT → rct, case report → case-report.
6. Auto-detect required figures. Based on the reporting guideline selected in Phase 0, consult the /make-figures study-type figure set table. Call /make-figures with the full figure set for the study type. Do not ask the user to name each figure individually.
7. Visual abstract check. If the target journal requires or encourages a visual abstract (check the journal profile for a "Visual Abstract" section), call /make-figures with visual abstract request. Provide: title, Key Points 1 and 3, methodology summary, and the best study figure as the visual element.
8. Figure discovery and embedding. After figure generation completes, scan the analysis/figures/ directory for all PNG and PDF files. For each figure:
   • Generate a markdown image reference: ![Figure N. Caption](analysis/figures/filename.png){width=80%}
   • Draft a figure legend based on the figure type and analysis context
   • Insert the reference at the appropriate location in the Results section
9. Manifest verification (HALT gate). After /make-figures completes, verify that analysis/figures/_figure_manifest.md exists and contains at least one figure entry. If the manifest is missing or empty: in autonomous mode, HALT with error code MANIFEST_MISSING, log to qc/_pipeline_log.md, and write a recovery note to manuscript/<id>/REPORT.md Tier-3 section ("rerun /make-figures or manually create _figure_manifest.md"). In interactive mode, report the error and ask the user how to proceed. Rationale: Phase 7 DOCX build (line 567) parses the manifest to embed figures; a missing manifest silently drops all figures from the final docx, which surfaces only at submission. HALT-on-missing is cheaper than discovering the absence in submission QC.

Gate: Present T&F plan to user. Do NOT proceed until user approves. Autonomous mode: If --autonomous is ON, skip this gate. Log the T&F plan to qc/_pipeline_log.md and proceed to Phase 3.

---

Phase 3: Methods

Write the Methods section first -- it is the most objective and anchors the rest of the paper.

Before writing: Load ${CLAUDE_SKILL_DIR}/references/section_guides/methods.md for PICO structure, backbone article usage, checklist cross-reference, and terminology conventions. For the matching study type, also skim the structure model in ${CLAUDE_SKILL_DIR}/references/exemplar_methods/ (diagnostic-accuracy/STARD, AI-validation/TRIPOD+AI·CLAIM, observational-cohort/STROBE) — it lists, paragraph by paragraph, what each Methods paragraph must establish plus the element that type most often omits. Model the structure; the exemplars are synthetic, with placeholder specifics, not prose to copy.

Writing order within Methods:

1. Study Design and Setting
2. Participants / Dataset (inclusion/exclusion, recruitment period)
3. Procedures / Intervention / AI Model description
4. Outcome Measures (primary and secondary endpoints)
5. Statistical Analysis (reference ${CLAUDE_SKILL_DIR}/references/section_templates/methods_statistical.md)
6. Ethics statement
7. AI/LLM disclosure (if --no-llm-disclosure was NOT set): insert the Methods disclosure paragraph from the LLM Disclosure section

AI/LLM extraction add-ons (when applicable):

• In Dataset / Inputs, state exactly which text fields the model received and whether clinical history, indication, impression, prior diagnosis, or referral text was masked. If a supplied field can contain the target label, Methods must either exclude it or describe a no-leaky-field sensitivity analysis.
• In AI Model or Statistical Analysis, include a same-backbone zero-shot/few-shot comparator when the claim is that fine-tuning, LoRA, prompt engineering, or a multi-agent wrapper improves performance.
• In Introduction, state the decision-impact path: what clinical or research workflow step changes if the model works, not only that the extracted label is interesting.

Process:

1. Writer pass: Draft the full Methods section following the outline and paper type template.
2. Critic pass: Score using the 6-dimension rubric (see Critic Scoring below). Provide specific line-level feedback.
3. Fixer pass: Revise based on critic feedback.
4. Repeat critic-fixer loop up to 3 rounds. Pass threshold: overall score >= 85/100.
5. Present final Methods to user.

---

Phase 4: Results

Write Results aligned to the approved tables and figures. Results = "What did we find?" — nothing more. Every sentence must be a factual statement backed by a number.

Before writing: Load ${CLAUDE_SKILL_DIR}/references/section_guides/results.md for mirror-symmetry rules, flowchart requirements, missing data handling, and the anti-interpretation self-check. For the matching study type, also skim the structure model in ${CLAUDE_SKILL_DIR}/references/exemplar_results/ (diagnostic-accuracy/STARD, AI-validation/TRIPOD+AI·CLAIM, observational-cohort/STROBE) — each follows its exemplar_methods/ sibling in Methods order, listing what each Results paragraph must establish (flow → baseline/prevalence → primary estimate with CIs → calibration/agreement → subgroups → sensitivity) plus the element that type most often omits. Model the structure; the exemplars are synthetic, with placeholder specifics, not prose to copy.

Rules:

• Every number in the text must match the corresponding table cell exactly.
• Start with study population description referencing Table 1.
• Present primary endpoint results first, then secondary.
• Reference every table and figure at least once in the text.
• Report exact p-values (not "p < 0.05" unless truly < 0.001).
• All primary metrics must include 95% confidence intervals.
• Incremental value must be earned, not asserted. If the paper claims the model/marker adds value beyond / on top of an existing tool (a clinical score, a routine test, a baseline model), Results must report the nested-model comparison — a baseline model from the in-routine-use predictors versus the augmented model — with an incremental metric: ΔC-index / ΔAUC (paired CI, e.g. DeLong), NRI, IDI, or decision-curve net benefit. A standalone discrimination number does not support a "beyond X" claim. If the design did not include the baseline comparator (see /design-study Phase 3), soften the claim to standalone performance rather than implying added value.
• Do not interpret results in this section; state findings only.

Anti-interpretation guardrails (strict):

• NO "why" explanations — save for Discussion.
• NO comparisons with prior literature — save for Discussion.
• NO causal language ("caused," "led to," "due to") — use "was associated with."
• NO evaluative adjectives without numbers ("high," "significant," "notable," "remarkable," "surprising") — always pair with the actual value.
• NO hedge words implying interpretation ("suggests," "implies," "indicates importance," "consistent with," "as expected").
• Self-check heuristic (applied to every sentence):
  1. Does this sentence explain "why"? → Move to Discussion.
  2. Does it reference another study? → Move to Discussion.
  3. Does it use "suggests/implies/indicates importance"? → Rewrite as factual statement.
  4. Does it use an adjective without a number? → Add the number or delete the adjective.
  5. Does it contain "interestingly/notably/remarkably/surprisingly"? → Delete the word.

Structure:

1. Study population (enrollment, exclusions, demographics → Table 1).
2. Primary endpoint results (one paragraph per primary outcome).
3. Secondary endpoint results.
4. Subgroup / sensitivity analyses (if applicable).

Process: Same writer -> critic -> fixer loop as Phase 3 (max 3 rounds, threshold 85/100).

Gate: Present final Results to user. Confirm before proceeding to Discussion.

---

Phase 5: Discussion

Before writing: Load ${CLAUDE_SKILL_DIR}/references/section_guides/discussion.md for the 4-paragraph structure, word limits, limitation writing guidelines, and Table/Figure citation rules. For the matching study type, also skim the structure model in ${CLAUDE_SKILL_DIR}/references/exemplar_discussion/ (diagnostic-accuracy/STARD, AI-validation/TRIPOD+AI·CLAIM, observational-cohort/STROBE) — completing the exemplar trio, each lists what every Discussion paragraph must establish (key finding → interpretation/comparison → limitations → generalizability → conclusion matched to the evidence) plus the element that type most often omits (spectrum/verification bias; evidence-tier separation and optimism caveats; mandatory causal caution). For case reports, use ${CLAUDE_SKILL_DIR}/references/exemplar_case_report.md instead: it controls literature-boundary wording, n=1 causal caution, and bedside teaching-point framing. Model the structure; the exemplars are synthetic, introduce no new results, and are not prose to copy.

Before drafting, collect user input (Discussion Planning Gate).

Step 5a: Discussion Planning (interactive)

Ask the user the following questions (in the user's preferred language). Wait for answers before drafting.

Q1. List the 3-5 key findings of this study in order of importance.
Q2. Name 3-5 key prior studies (anchor papers) you want to compare against in the
    Discussion — titles or DOIs.
    - Studies consistent with your results: ?
    - Studies inconsistent with your results: ?
Q3. Are there methodological or population differences that could explain any disagreement?
Q4. State up to 3 limitations of this study.
    (For each, include how it was mitigated and the direction in which it could affect the results.)
Q5. Are there clinical implications you want to emphasize?

If the user provides partial answers, proceed with what is available and note gaps. If the user says "skip" (or the equivalent in their language), use /search-lit to identify anchor papers from the reference list and proceed with best-effort defaults.

Gate: Do NOT start writing Discussion until user responds (or explicitly skips). Autonomous mode: If --autonomous is ON, skip the interactive planning. Use /search-lit to identify anchor papers from the reference list and proceed with best-effort defaults (same as the "skip" path).

Step 5b: Discussion Drafting

Write the Discussion using the inverted funnel structure:

Paragraph structure:

1. Summary (1 paragraph): Restate key findings without repeating numbers verbatim. Bridge from Results — the reader should feel continuity.
2. Context — anchor paper comparisons (2-3 paragraphs): Each paragraph organized around one theme or finding. For each anchor paper:
   • State the prior finding with citation.
   • Compare: agreement or disagreement with our result.
   • Explain the discrepancy (if any) citing methodological or population differences.
3. Clinical implications (1 paragraph): What does this mean for practice or future research?
4. Limitations (1 paragraph): Honest, specific, ordered by severity. For each limitation: (a) what it is, (b) how it was mitigated, (c) direction of residual bias. Do NOT use "our study has several limitations" as an opener.
5. Strengths (optional, 1-2 sentences): Only if genuinely novel contribution.
6. Conclusion (1-2 sentences): Single most important finding + implication. Must be a citable statement. No "further studies are needed" as final sentence.

Rules:

• Do not introduce new data not presented in Results.
• Avoid overclaiming: language must match evidence level.
• Endpoint↔conclusion scope. The Clinical-implications and Conclusion sentences must not exceed what the design and endpoint support. A cross-sectional / single-visit / prevalence study cannot license a prognostic or surveillance claim (a rescreen interval, disease progression, predicting future risk) — that requires longitudinal follow-up. A binary surrogate endpoint (present/absent, >0, dichotomized) is risk stratification, not a patient-care directive (defer/withhold/initiate therapy). /self-review §D (check_scope_coherence.py) flags CROSS_SECTIONAL_PROGNOSTIC / SURROGATE_CARE_DIRECTIVE; keep the conclusion verb inside the design's reach.
• Acknowledge alternative explanations for key findings.
• Each comparison with prior work must cite the specific study.
• NO "interestingly," "notably," "it is worth noting" — state the point directly.

Process: Same writer -> critic -> fixer loop (max 3 rounds, threshold 85/100).

After the first draft, present to the user with (ask in the user's preferred language):

Here is the Discussion draft. Please review:
- Any missing anchor papers or additional comparisons needed?
- Anything you want to change in the interpretation?
- Any clinical implications to emphasize more or soften?

Incorporate user feedback before running the critic-fixer loop.

---

Phase 6: Introduction + Abstract

Write these LAST because they frame the paper and depend on knowing what was actually found.

Before writing: Load ${CLAUDE_SKILL_DIR}/references/section_guides/introduction.md for the Gap Storytelling 5-step structure, word/paragraph/reference targets, and common mistakes, and skim the paragraph-by-paragraph structure model in ${CLAUDE_SKILL_DIR}/references/exemplar_introduction.md (¶1 significance → ¶2 landscape → ¶3 the gap → ¶4 objective, plus the vague-gap and gap↔objective-mismatch failure modes). Also load ${CLAUDE_SKILL_DIR}/references/section_guides/title_abstract.md for Title 3-type selection, 4-component checklist, Abstract Conclusion-first priority, and Visual Abstract guidance, and skim the structured-abstract structure model in ${CLAUDE_SKILL_DIR}/references/exemplar_abstract.md (Background/Objective → Methods → Results-with-primary-estimate-+-CI-+-denominator → Conclusion-matched-to-design, plus the estimate-free-Results, over-reaching-Conclusion, and body↔abstract number-mismatch failure modes). For case reports, use ${CLAUDE_SKILL_DIR}/references/exemplar_case_report.md for the 150-word Introduction / Case Presentation / Conclusion abstract anatomy rather than the IMRAD abstract model. Model the structure; the exemplars are synthetic, with placeholder specifics, not prose to copy.

Introduction structure (3-4 paragraphs):

1. Clinical context establishing importance (cite prevalence, burden, current practice).
2. Knowledge gap that this study addresses.
3. Study objective, stated precisely. Include hypothesis if applicable.

Abstract:

• Follow the journal's structured format exactly.
• Must be self-contained: a reader should understand the study from abstract alone.
• All numbers must match the main text and tables.
• Final sentence: clinical implication, not "further studies are needed."
• Lead with the pre-specified primary estimand, not the largest effect. It is tempting (and a critic/peer-sim pass may even suggest it) to foreground the strongest number to make the Abstract "land harder." Do not let that reframe which result is primary: tightening effect-size language is fine, but promoting a secondary, exploratory, or post-hoc estimate to the headline is estimand shopping. The Abstract's primary result must be the registered/protocol primary contrast — the same one Step 7.3b checks. If the primary is null or underpowered, report it as such (see /self-review category C, power-aware null) rather than substituting a more favourable secondary estimate.

Process: Same writer -> critic -> fixer loop (max 3 rounds, threshold 85/100).

---

Phase 7: Polish

Final quality pass before submission.

Actions (strict sequential execution — each step MUST complete before the next begins):

Step 7.1: AI Pattern Scan

Scan for and remove AI writing patterns (see AI Pattern Avoidance below). Edit manuscript/manuscript.md in place.

Classical-style QC (for senior MA reviewers) — load on demand:

Trigger	Action
Manuscript type = MA, systematic review, or a senior co-author review is expected	Load references/section_guides/step7_1_classical_qc.md → run the 7 grep checks together (§ symbol, AI Disclosure paragraph, heading style, eligibility numbered list, Funding placeholder, PROSPERO chronology, em-dash overuse)
Verify all at once with a deterministic lint	python3 "${MEDSCI_SKILLS_ROOT:-$HOME/workspace/medsci-skills}/skills/self-review/scripts/check_classical_style.py" --manuscript manuscript/manuscript.md --strict — SECTION_SYMBOL/INBODY_AI_DISCLOSURE (Major) + ELIGIBILITY_PROSE/DECIMAL_INCONSISTENCY/EM_DASH_OVERUSE (Minor). The machine-checkable subset of the same conventions as the 7-grep checklist.
Global-rule cross-reference	~/.claude/rules/manuscript-style-classical.md (motivation for the 11 items)
Pattern 19–21 body rewrite	/humanize (§, self-reference, AI Disclosure boilerplate)

AI-disclosure meta-applicability (manuscript-style-classical §15): if the manuscript contains an AI/LLM-use disclosure, that paragraph must itself satisfy the reporting items the manuscript critiques (FLAIR F1.6, TRIPOD-LLM, MI-CLEAR-LLM all require the tool version, the access channel, the date range, and the responsible party). Enforce all four tokens and zero unresolved placeholders:

DISC=$(grep -niE 'generative ai|large language model|\bLLM\b|assisted (the|with) (writing|drafting)|ChatGPT|Claude|Copilot|Gemini' manuscript/manuscript.md)
# the disclosure paragraph must carry: version + channel + date + responsible party
grep -iE 'version|[0-9]+\.[0-9x]+|GPT-[0-9]'   <<<"$DISC"   # version present
grep -iE 'API|chat|web|Bedrock|Azure|interface' <<<"$DISC"   # access channel present
grep -E '20[0-9]{2}'                            <<<"$DISC"   # date / date range present
grep -iE 'by [A-Z]\.[A-Z]\.|reviewed by|deployed by|the authors' <<<"$DISC" # responsible party
# zero placeholders
grep -nE '\[(version|date|tool|model|channel)\]|TODO|XXXX|TBD' manuscript/manuscript.md  # must be empty

Any missing token (or a surviving [version]/TODO/XXXX placeholder) is a HALT: the paper cannot critique a framework's AI-disclosure item while failing it itself. For a classical / senior-MA target the disclosure paragraph is not placed in the body at all — branch it to the title page (manuscript-style-classical §7 forbids the in-body AI-disclosure paragraph).

Step 7.2: Reporting Guideline Check

Call /check-reporting on manuscript/manuscript.md. Parse the output:

• If the report includes a JSON summary block (Part D), extract MISSING items.
• For each MISSING item where fixable_by_ai is true (e.g., missing ethics statement, missing data availability statement, missing sample size justification), insert the suggested text at the indicated location in manuscript/manuscript.md.
• Do NOT attempt to fix items requiring external information (IRB numbers, registration numbers, protocol details only the author knows).
• Log all auto-inserted text to qc/_pipeline_log.md.

Step 7.3: Citation Verification

7.3.1 — Placeholder gate (v1.1.1 Phase 1A.4). Before running /verify-refs, confirm that no [@NEW:topic] placeholders remain:

grep -nE '\[@NEW:[^]]+\]' manuscript/index.qmd manuscript/manuscript.md 2>/dev/null

If any match is returned, HARD STOP. Report the unresolved placeholders to the user and loop back: owner runs /search-lit → /lit-sync to import entries, collaborators flag via owner. Do NOT proceed to 7.3.2 until the grep is clean.

7.3.2 — Audit. Call /verify-refs on the current manuscript. Per v1.2.0 contract, its sole output is qc/reference_audit.json (no longer writes references/*). Parse that file: if submission_safe: false, stop the pipeline and surface the FABRICATED / MISMATCH records AND any duplicate_findings[] entries (duplicate PMID/DOI; cite renumbering required) to the user. If /verify-refs is unavailable, fall back to /search-lit --verify-only and flag any unverified references with [UNVERIFIED] markers.

Steps 7.3a / 7.3b / 7.3c: Integrity audits (numerical / estimand / reference-adequacy)

After Step 7.3 and before Step 7.4, run three integrity audits. Each can HALT and route to Step 7.4a (Audit Recovery Branch). Full procedures (triggers, blocker policy, the delegated checker commands, and the qc/_pipeline_log.md log formats) are in ${CLAUDE_SKILL_DIR}/references/phase7_integrity_audits.md — load it when this step runs.

• 7.3a Numerical Claim Audit (mandatory for MA / pooled estimates / comparative arms / revisions / reporting-quality-checklist synthesis): 3-way match text ↔ Table ↔ extraction CSV, primary-source back-check, analysis-script literal audit, and recompute reporting-quality headline numbers from matrix cells (denominator = Σ non-NA). A direction reversal or a p<0.05↔p≥0.05 crossing is a P0 blocker; composes with /self-review Phase 2.5a.
• 7.3b Estimand Provenance & Promised-Analysis Audit (any pre-registered/protocol primary, E-value, or named analyses): delegate to /self-review Phase 2.5f — PRIMARY_REASSIGNED / ESTIMAND_DRIFT / EVALUE_ARITHMETIC / EVALUE_NON_PRIMARY are P0 blockers; grep that Methods-promised analyses appear in Results; run check_artifact_coverage.py --strict for the disk-present-but-unreported reverse scan.
• 7.3c Reference Adequacy Gate (every named statistical method / reporting guideline must carry a citation): run check_reference_adequacy.py (no --strict; write-paper decides from the JSON); a methods_zero_citations / methods_named_method_uncited finding is a reference-acquisition blocker resolved only via /search-lit → /lit-sync → /verify-refs --strict (never fabricate); composes with /self-review Phase 2.5c-2.

Step 7.4: Self-Review + Fix Loop

Call /self-review --json --fix on the current manuscript/manuscript.md.

This delegates the entire fix loop to the self-review skill, which:

1. Runs systematic review (Phase 2) and generates a JSON report (Phase 3c).
2. If verdict is "REVISE": filters fixable_by_ai issues, applies text edits to manuscript.md, and re-reviews — up to 2 fix-and-re-review iterations.
3. If verdict is "PASS" after any iteration: stops early.
4. Returns the final JSON report with updated scores.

High-stakes manual pass (optional): this autonomous loop deliberately uses the single-pass review — a multi-agent panel is not auto-applied in the pipeline (it spawns several reviewer agents plus an editor, multiplying token cost). For a top-tier or otherwise high-stakes manuscript, run /self-review --panel once manually as a final pre-submission pass (it diagnoses and prioritizes but does not auto-fix, so triage its findings yourself).

After /self-review --json --fix completes:

• Parse the final JSON output block.
• Log the final overall_score, verdict, fix iteration count, and any remaining issues to qc/_pipeline_log.md.
• If any severity: "fatal" issue remains: route to Step 7.4a (Audit Recovery Branch) — do NOT proceed to Step 7.5.
• If no fatal issue remains: proceed to Step 7.5.

Step 7.4a: Audit Recovery Branch

Purpose: the linear polish flow assumes remaining issues are prose-level, but some self-review findings are structural — underlying data, protocol application, or analysis script is wrong, not prose. Continuing through Step 7.5 – 7.6 in that case produces a polished manuscript built on a broken foundation. This step makes the recovery loop explicit.

Trigger (any one from Step 7.4 JSON): fatal issue in category accuracy, data_fidelity, protocol_mismatch, or numerical_claim; unresolved Step 7.3a primary- source disagreement; [VERIFY-CSV] tag persisting after two fix iterations; registered protocol ↔ delivered analysis inconsistency; reviewer-consensus ↔ locked-dataset disagreement. Inline text fixes are forbidden — recovery requires re-extraction, re-analysis, or re-registration.

Routing table:

Symptom	Route to
MA pooled/forest/subgroup/funnel numbers disagree with source	/meta-analysis Phase 10
MA protocol ↔ analysis mismatch (eligibility, outcome, subgroup)	/meta-analysis Phase 10 + registry amendment
Primary-study numerical claim disagrees with source Table/Figure	/meta-analysis Phase 6b, then return
Non-MA extraction error affecting Table 1 / primary endpoint	Return to Phase 2, re-enter Phase 3 – 7 for affected sections
Non-MA protocol amendment needed	HALT — human decision

Sequence: (1) halt Steps 7.5 – 7.6; (2) log the branch decision to qc/_pipeline_log.md; (3) invoke the routed skill with the specific findings; (4) on re-entry, resume at Step 7.3 (Citation Verification) — not Step 7.1, because recovery may have introduced new citations — and carry any change summary to Phase 8+; (5) loop budget is one cycle — a second cycle should trigger a root-cause review of Phase 2 / 6 / 6b rather than another recovery.

Autonomous mode. In --autonomous, the orchestrator may auto-invoke the routed recovery skill. If the recovery requires human decision (protocol amendment, eligibility re-scope), the run stops and flags RECOVERY_HALT_HUMAN_DECISION in the log.

Load-on-demand procedural detail (full trigger list, log-block template, per-route re-entry checklist, autonomous-mode edge cases): ${CLAUDE_SKILL_DIR}/references/section_guides/step7_4a_audit_recovery.md.

Step 7.5: Generate Deliverables

Log the self-review fix loop results to qc/_pipeline_log.md:

## Self-Review Fix Loop (Phase 7.4)
- Initial score: {score_before} → Final score: {score_after}
- Fix iterations: {N}/2
- Fixed issues: {count}
- Remaining issues (human review needed): {count}
- Final verdict: {PASS|REVISE}

Generate the following files:

• manuscript/manuscript.md: Complete manuscript (with LLM disclosure in Methods and Acknowledgments if enabled)
• manuscript/title_page.md: Title page with author info, word count, key points if required. Number the author affiliations by first appearance (affiliation 1 = the first author's first affiliation; each new affiliation gets the next integer as the author list is read left to right; each ends with city + country) — required by Nature Portfolio / npj technical checks. Do not hand-number; generate and verify with scripts/build_title_page_affiliations.py (--authors authors.yaml to build, --check title_page.md --strict to verify). See references/section_guides/title_abstract.md § "Title Page — Author & Affiliation Order".
• qc/reporting_checklist.md: Filled reporting guideline checklist from Step 7.2
• qc/self_review.md: Final self-review report from Step 7.4
• qc/_pipeline_log.md: Pipeline execution log

Step 7.6: DOCX Build

Build the final submission-ready documents from the assembled components:

1. Input files: manuscript/manuscript.md, analysis/figures/_figure_manifest.md, analysis/tables/*.csv

2. Figure embedding: Parse analysis/figures/_figure_manifest.md. For each figure entry, verify the file exists at the specified path. Replace markdown image references ![Figure N. ...](path) with the actual image path.

3. Table embedding: For each analysis/tables/*.csv file referenced in the manuscript, the pandoc conversion will handle table formatting.

4. Pandoc conversion (primary):

   pandoc manuscript/manuscript.md -o manuscript/manuscript_final.docx -V mainfont="Times New Roman" -V fontsize=12pt
   pandoc manuscript/manuscript.md -o manuscript/manuscript_final.pdf --pdf-engine=xelatex -V geometry:margin=1in -V fontsize=11pt -V mainfont="Times New Roman"
   

   Ensure all figure image references use relative paths so figures render in both formats.

   With pandoc citeproc + journal CSL (when manuscript uses [@bibkey] citations and a .bib is available — preferred for any submission with > 5 references; mandatory when reviewers have asked for "automatically generated reference list"):

   The validation + render scripts live in /manage-refs (split out 2026-05-01). Either invoke /manage-refs directly (recommended), or call the scripts manually:

   MR="${MEDSCI_SKILLS_ROOT:-$HOME/workspace/medsci-skills}/skills/manage-refs"
   
   # 1. Validate keys vs .bib first (fail fast on UNDEFINED keys; [@NEW:topic] placeholders pass through)
   python "$MR/scripts/check_citation_keys.py" \
     manuscript/manuscript.md manuscript/_src/refs.bib
   
   # 2. Render with journal CSL (see manage-refs/citation_styles/ for bundled CSLs)
   "$MR/scripts/render_pandoc.sh" \
     -j european-radiology \
     -i manuscript/manuscript.md \
     -b manuscript/_src/refs.bib \
     -o manuscript/manuscript_final.docx
   

   Bundled CSLs: european-radiology, radiology, american-journal-of-roentgenology, cardiovascular-and-interventional-radiology, korean-journal-of-radiology, vancouver, vancouver-superscript. Use radiology for RYAI; use vancouver for JVIR (no dedicated CSL). On rejection cascade (e.g., ER → JVIR → CVIR), re-render with different -j — references reformat in seconds. Never hand-type the References list.

   Decision: pandoc vs Zotero Word plugin (CWYW) — /manage-refs documents the hybrid 3-phase strategy (Phase 1 pandoc draft → Phase 2 transition → Phase 3 Zotero CWYW for circulation/revision/submission). Use Workflow B (CWYW) once co-authors collaborate live in Word; use Workflow A (pandoc) for single-author lockdown, journal-cascade rejection re-formatting, or when the plugin is unavailable. See ~/.claude/rules/manuscript-references.md and skills/manage-refs/SKILL.md.

5. Fallback (if pandoc is unavailable): Generate the DOCX using python-docx:

   • Parse manuscript/manuscript.md sections (## → Heading 2, ### → Heading 3, **bold** → bold runs)
   • Insert figures as inline images at their markdown reference locations
   • Insert tables as formatted Word tables from CSV sources
   • Apply Times New Roman 12pt, double spacing, 1-inch margins, page numbers
   • Save as manuscript/manuscript_final.docx

6. Verify output: Confirm manuscript/manuscript_final.docx exists and is non-empty. Report file size.

Step 7.6a: Cross-Reference QC (Manuscript ↔ rendered DOCX)

Catches the failure mode where in-text Table/Figure citations resolve to the wrong rendered caption. Internal consistency (Phase 2.5 of /self-review) does NOT catch this because both the body prose and the build script can echo their own divergent SSOTs cleanly. Precedent: an STROBE cohort manuscript revision — body cited "Supplementary Table S4 (a sensitivity-analysis)" but the rendered DOCX S4 was a diagnostics table; S1, S6, S7 mismatched and S8, S9 were cited but absent from the DOCX entirely.

Run after Step 7.6 DOCX build and before Step 7.7 final gate:

MR="${MEDSCI_SKILLS_ROOT:-$HOME/workspace/medsci-skills}/skills/manage-refs"
python3 "$MR/scripts/check_xref.py" \
  --md manuscript/manuscript.md \
  --docx manuscript/manuscript_final.docx \
  --out qc/xref_audit.json \
  --strict

The script extracts (a) every (Supplementary )?(Table|Figure)\s+(S?\d+[A-Z]?) in-text citation, (b) caption definitions from ## Tables / ## Figures / ## Figure Legends / ## Supplementary {Tables,Figures} sections in the body, and (c) caption paragraphs in the rendered DOCX (via python-docx). It then emits a 3-way matrix to qc/xref_audit.json:

Status	Meaning	Severity
OK	cited + body caption + DOCX caption all present and caption text agrees (Jaccard ≥ 0.40)	—
MISSING_DOCX	cited but no caption with that label in the rendered DOCX	P0 blocker
MISSING_BODY	cited but no caption definition in the markdown body sections (build SSOT drift)	P0 blocker
MISMATCH	label exists in both body and DOCX but caption text disagrees	P0 blocker
UNCITED	caption defined or rendered but never cited in main text	warn
NOT_CITED_NO_BODY	label appears only in DOCX (rare; legacy artifact)	warn

Submission gate: if any MISSING_DOCX / MISSING_BODY / MISMATCH row is present, submission_safe: false and the script exits 1 under --strict. HALT pipeline. Do NOT proceed to Step 7.7. Route fixes by symptom:

• MISSING_BODY → add caption definition under ## Tables / ## Figures in manuscript.md, then re-run Step 7.6 + 7.6a. If the build script (build_manuscript_docx.py or equivalent) carries its own hardcoded caption list, that is the IMPROVEMENT_QUEUE #2 SSOT-unification issue — flag it.
• MISSING_DOCX → either drop the citation (the table/figure was retired) or re-add the table/figure to the build pipeline, then rebuild DOCX.
• MISMATCH → reconcile body vs build script. Body caption is the SSOT; update the build pipeline to match, never the reverse.

Log the run to qc/_pipeline_log.md:

## Cross-Reference QC (Phase 7.6a)
- in-text citations: {N}
- unique labels: {N}
- OK: {N} | MISSING_DOCX: {N} | MISSING_BODY: {N} | MISMATCH: {N} | UNCITED: {N}
- submission_safe: {true|false}
- audit: qc/xref_audit.json

If python-docx is unavailable, the script falls back to a body-only audit (citations vs body captions) with a warning. Install with pip install python-docx.

Step 7.7: Final Gate

• Autonomous mode: Log completion to qc/_pipeline_log.md. Report summary: word count, figure count, self-review score, reporting compliance percentage, any FATAL flags.
• Interactive mode: Present the full summary to the user and await confirmation.

---

Phase 8+ (Optional): Cover Letter Generation

Triggered when the user requests "generate cover letter" or after /find-journal recommendation.

This is an optional post-pipeline step. Do NOT generate automatically — only when explicitly requested.

Required user inputs (MUST ask, never fabricate):

1. Editor name (if known; otherwise use "Dear Editor")
2. Suggested reviewers (2-3 names with affiliations and email addresses)
3. Excluded reviewers (if any, with brief reason)
4. Any specific points to emphasize for the target journal

Cover letter structure:

1. Salutation: "Dear [Editor name / Editor],"
2. Submission statement: "We submit our manuscript entitled '[Title]' for consideration as [article type] in [Journal Name]."
3. Novelty statement (2-3 sentences): What is new and why it matters. Extract from abstract key findings.
4. Scope fit (1-2 sentences): Why this journal is appropriate. Reference journal scope from profile if loaded.
5. Brief methods (1 sentence): Study design and key numbers.
6. Ethical compliance: IRB approval number, author agreement, COI statement, no dual submission.
7. AI disclosure (if applicable): Specific AI tools used and human oversight statement.
8. Suggested reviewers: Name, affiliation, email, expertise area (2-3 minimum).
9. Excluded reviewers (if any): Name and reason.

Reviewer COI cross-check (mandatory for meta-analyses): Cross-check all suggested and excluded reviewers against the included-study author list and their co-authors. Same-institution authors of included studies constitute automatic COI and must be excluded from reviewer suggestions. 10. Closing: Corresponding author name and credentials.

Anti-overclaiming guard: Automatically flag and rewrite any of these words in cover letters: "first," "novel," "unprecedented," "groundbreaking," "paradigm-shifting," "revolutionary." Replace with specific factual statements about what the study contributes.

Word limit: 300-500 words. Cover letters exceeding 500 words should be trimmed.

---

LLM-Assisted Writing Principles

When using this skill (or any LLM) for manuscript drafting, follow this 3-step process:

1. Structure first: The user (or the skill) outlines the logical flow, key arguments, and paragraph-level plan before generating prose. An LLM cannot evaluate its own output without a pre-defined target.
2. LLM drafts: Generate prose based on the structured plan.
3. Critical evaluation: Review LLM output against the plan. Check for logical gaps, unsupported claims, AI pattern phrases, and deviation from the intended argument. Revise or reject sections that do not meet the standard.

This principle applies at every phase: the outline (Phase 1) is the structure; the writer pass is the LLM draft; the critic-fixer loop is the critical evaluation. The user remains the final arbiter of scientific accuracy and narrative direction.

---

Critic Scoring Rubric

Each section goes through a critic-fixer loop. The critic scores 6 dimensions (0-20 each, total 0-120 scaled to 0-100).

Dimensions

#	Dimension	What the critic checks
1	Accuracy	Every claim matches data/tables. No fabricated numbers. Effect directions correct.
2	Completeness	All required elements per reporting guideline present. No missing subsections.
3	Clarity	Each sentence parseable on first read. No ambiguous referents. Logical paragraph flow.
4	Conciseness	No filler phrases, redundant sentences, or unnecessary hedging. Within word budget.
5	Reporting	Specific guideline items (STARD/TRIPOD/CLAIM/etc.) addressed in this section.
6	Humanness	No AI writing patterns detected (see list below). Reads like an experienced physician wrote it.
7	Section Boundaries	Results only: No interpretation, no "why," no prior literature references, no evaluative adjectives without numbers. Discussion only: No new data not in Results, no overclaiming beyond evidence level. Flag any sentence that belongs in the other section.

Note: Dimensions 1-6 are scored 0-20 each (total 0-120 scaled to 0-100). Dimension 7 is a pass/fail gate applied during Phase 4 (Results) and Phase 5 (Discussion): if any sentence violates section boundaries, the critic MUST flag it regardless of overall score. The fixer must move or rewrite the flagged sentence before the section can pass.

Scoring Guide

• 18-20: Publication-ready. No changes needed.
• 14-17: Minor revisions. Specific sentences flagged.
• 10-13: Moderate revisions. Structural or content gaps.
• 0-9: Major rewrite. Fundamental issues.

Pass Threshold

• Overall score >= 85/100 to pass.
• No single dimension below 12/20.
• If either condition fails, trigger fixer round.

Critic Output Format

## Critic Report: {Section Name} -- Round {N}

Overall: {score}/100
Accuracy: {}/20 | Completeness: {}/20 | Clarity: {}/20
Conciseness: {}/20 | Reporting: {}/20 | Humanness: {}/20

### Issues (by priority)
1. [Dimension] Line/paragraph reference: {specific issue} -> {suggested fix}
2. ...

### Verdict: {PASS | REVISE}

---

Manuscript Writing Rules

Prose Quality

• Full prose only. NEVER use bullet points or numbered lists in manuscript sections (Methods, Results, Discussion, Introduction). Bullet points are acceptable only in structured abstracts if the journal format requires them.
• Active voice preferred. "We analyzed" not "Analysis was performed." Use passive only when the agent is truly irrelevant.
• Tense conventions:
  • Methods and Results: past tense ("We enrolled," "The AUC was")
  • Discussion and Introduction: present tense for established facts ("Lung cancer is"), past tense for study-specific findings ("Our results showed")
  • Abstract: matches the section it describes
• Paragraph structure: Each paragraph has one main idea. First sentence states the point; subsequent sentences provide evidence or elaboration.
• Transitions: Every paragraph connects logically to the next. Use explicit transition phrases sparingly but effectively.

Data Integrity

• All numbers in text must match the corresponding table cells exactly.
• Report effect sizes with 95% confidence intervals for all primary endpoints.
• Use exact p-values (p = 0.032) rather than thresholds (p < 0.05), except when p < 0.001.
• Percentages must match: if 23 of 150, write "23 (15.3%)" -- verify the math.
• Never round numbers differently between text and tables.

AI Pattern Avoidance

The manuscript must NOT contain these patterns commonly flagged as AI-generated:

Forbidden phrases:

• "In conclusion" (use "In summary" or rephrase)
• "It is worth noting that"
• "It is important to note that"
• "Notably,"
• "Interestingly,"
• "Importantly,"
• "Furthermore," at sentence start (use "In addition," or restructure)
• "Moreover," at sentence start
• "plays a crucial role"
• "a comprehensive analysis"
• "delve into"
• "leverage" (use "use" or "apply")
• "utilize" (use "use")
• "in the realm of"
• "underscores the importance of"
• "sheds light on"
• "paves the way for"
• "a nuanced understanding"
• "the landscape of"
• "a paradigm shift"
• "robust" (unless describing a statistical method)

Forbidden structural patterns:

• Three-part list sentences ("X, Y, and Z" repeated across paragraphs)
• Excessive hedging chains ("may potentially be associated with possible")
• Mirror-structure paragraphs (same template repeated with different content)
• Grandstanding opening sentences ("In the rapidly evolving landscape of...")

Preferred alternatives:

• Vary sentence structure and length within paragraphs.
• Use specific, concrete language over abstract generalizations.
• Let data speak: "The AUC was 0.92" rather than "The model demonstrated remarkable performance."

Journal Compliance

• Respect all word limits from the loaded journal profile.
• Follow the journal's structured abstract format exactly.
• Use the journal's citation style (Vancouver numbered for most radiology journals).
• Include all journal-specific required elements (e.g., "Key Points" for AJR, CLAIM checklist for RYAI AI studies).

---

Skill Interactions

This skill orchestrates other skills at specific phases:

Phase	Skill called	Purpose
2	/analyze-stats	Statistical analysis for tables
2	/make-figures --study-type	Figure generation with study-type auto-detection
7.1	(built-in)	AI pattern removal
7.2	/check-reporting	Reporting guideline compliance + auto-fix MISSING items
7.3	/verify-refs	Citation verification and reference artifact audit
7.4	/self-review --json	Self-review with auto-fix loop (max 2 iterations)
7.4a	/meta-analysis Phase 10 (MA manuscripts)	Audit recovery branch — rebuild extraction/analysis/figures/body when self-review surfaces structural data or protocol issues
7.5	/humanize	AI-pattern density sweep (<2.0 / 1000 words)
7.5a	/academic-aio (optional, off by default)	AI-search-engine and RAG visibility checklist — run after humanize so QC-confirmed claims and human-readable text anchor the PASS/PARTIAL/FAIL report. Opt-in via --aio or when preparing preprint / GitHub README / CITATION.cff / HF card alongside submission. Silent pipeline execution is explicitly prohibited by the skill's Communication Rules.
7.6	/manage-refs (pandoc citeproc / Zotero CWYW)	DOCX build from manuscript/manuscript.md + analysis/figures + analysis/tables. Bibliography rendering delegated to /manage-refs scripts/render_pandoc.sh since 2026-05-01.
7.6a	/manage-refs scripts/check_xref.py --strict	Cross-reference QC: in-text Table/Figure citations ↔ body captions ↔ rendered DOCX captions (3-way matrix). Submission gate.
8+	/find-journal	Journal scope for cover letter (optional)

If a called skill is not available, perform that step inline using the relevant section of this skill document as guidance.

---

LLM Writing Disclosure

When LLM disclosure is enabled (default), the skill generates transparency statements compliant with ICMJE 2025 and COPE guidelines. The user can disable this with --no-llm-disclosure.

Why Default ON

Major journals (Nature, Lancet, Radiology, JAMA) and the ICMJE (2025 update) require disclosure of AI writing assistance. Omitting disclosure risks rejection or retraction. The default-on design protects the user; they can opt out for journals with no such policy or when LLM assistance was minimal.

Disclosure Locations (3 places)

1. Methods Section — Last Paragraph

Insert at the end of the Methods section, after the ethics statement:

Template (adapt to specifics):

[AI-Assisted Writing Disclosure]
An artificial intelligence language model (Claude, Anthropic) was used to assist with
manuscript drafting, including structuring sections, refining prose, and verifying
internal consistency of reported statistics. All content was critically reviewed,
verified against source data, and approved by all authors. The AI tool was not involved
in study design, data collection, data analysis, or interpretation of results.

Customization rules:

• Replace "Claude, Anthropic" with the actual tool(s) used.
• List specific tasks the LLM performed (drafting, editing, literature search, statistical code).
• If the LLM was also used for data analysis (e.g., statistical code generation via /analyze-stats), state this explicitly: "was also used to generate statistical analysis code, which was reviewed and validated by [statistician/author]."
• Keep to 2-3 sentences. Do not over-explain.

2. Acknowledgments Section

Template:

The authors acknowledge the use of [Claude/tool name] ([Anthropic/developer]) for
writing assistance in preparing this manuscript. The authors retain full responsibility
for the content.

3. Cover Letter — AI Disclosure Paragraph (Phase 8+)

Template:

In accordance with [Journal Name]'s policy on AI-assisted writing, we disclose that
[Claude/tool name] was used to assist with manuscript preparation, specifically
[list tasks: drafting, language editing, statistical code review]. All authors have
reviewed and take responsibility for the final content. The AI tool was not listed
as an author and did not contribute to study conception, design, or data interpretation.

What NOT to Disclose

• Do not disclose routine use of grammar checkers (Grammarly, Word spell-check) — these are not considered generative AI under current ICMJE guidance.
• Do not disclose use of reference managers (Zotero, EndNote) or statistical software (R, Python) unless the LLM generated the analysis code.

Journal-Specific Overrides

When a journal profile is loaded in Phase 0, check for the ## AI Writing Disclosure Policy section in the profile. Tier 1 profiles now include structured fields:

• Requirement level (Required / Recommended / Not specified)
• Permitted scope (All tasks / Language editing only / Not permitted)
• Disclosure location (Methods / Acknowledgments / Cover letter / Submission form)
• AI-generated images (Allowed / Banned / Not specified)
• Policy URL

Use these fields to adjust disclosure language automatically. Key known policies:

• Radiology/RSNA: Required; language editing only; Methods + Acknowledgments; AI images banned.
• RYAI/RSNA: Required; language editing only; Methods + Acknowledgments; AI images banned.
• JAMA/AMA: Required; language editing only; Methods + Cover letter.
• Lancet: Required; language editing only ("readability and language"); Acknowledgments + prompts disclosed.
• BMJ: Required; all tasks permitted but must disclose; Methods + Acknowledgments; applies to text, images, data, diagrams.
• Nature/Springer Nature: Required; language editing only; Methods; AI images banned.
• Science/AAAS: Most restrictive. LLM use limited; treated as potential misconduct if undisclosed.

If the loaded journal profile has no AI Writing Disclosure Policy section, fall back to ICMJE 2025 defaults (disclose in Methods + Acknowledgments, language editing scope).

---

Error Handling

• If the user provides incomplete data for a table, flag specific missing values rather than inventing data.
• If word count exceeds the journal limit after a section draft, report the overage and suggest specific cuts.
• If the critic-fixer loop reaches 3 rounds without passing, present the best version to the user with the remaining issues listed, and ask for guidance.
• Never fabricate references. If a citation is needed, describe the type of reference needed and ask the user to provide it, or call /search-lit to find a real one.

Resumption

If the user returns to a partially completed manuscript:

1. Check the workspace directory for existing drafts.
2. Identify which phase was last completed.
3. Summarize progress and ask the user where to resume.

Anti-Hallucination

• Never fabricate references. All citations must be verified via /search-lit with confirmed DOI or PMID. Mark unverified references as [UNVERIFIED - NEEDS MANUAL CHECK].
• Never invent clinical definitions, diagnostic criteria, or guideline recommendations. If uncertain, flag with [VERIFY] and ask the user.
• Never fabricate numerical results — compliance percentages, scores, effect sizes, or sample sizes must come from actual data or analysis output.
• If a reporting guideline item, journal policy, or clinical standard is uncertain, state the uncertainty rather than guessing.

---

Gates

Severity levels: ENFORCED = pipeline halts on failure (cannot proceed to next phase). ADVISORY = warning logged, user may override. OPT-IN = runs only when explicitly invoked.

Phase	Gate	Severity	Trigger	Action on fail
0	Backbone-article auto-proposal (Phase 0 "Identify a backbone article" action)	ADVISORY	refs.bib has methodologically similar candidate	Surface to user; user accepts/declines
7.0	Citekey resolution (delegate /manage-refs scripts/check_citation_keys.py)	ENFORCED	UNDEFINED keys present	Halt; resolve via /lit-sync then re-run
7.0	NEW_PLACEHOLDER drain (delegate /manage-refs)	ENFORCED at 7.6 entry	[@NEW:topic] markers remain	Resolve each before DOCX render
7.1	Classical-style QC (manuscript-style-classical 11 items)	ENFORCED	§ symbol > 0 OR AI Disclosure paragraph in body OR em-dash > 25	Auto-fix or HALT for senior MA reviewer prep
7.2	Reporting guideline compliance (/check-reporting)	ENFORCED at submission	<100% mandatory items present	Auto-fix MISSING; ADVISORY for partial
7.3	Reference audit (/verify-refs --strict)	ENFORCED	FABRICATED or HIGH_MISMATCH_FIRST_AUTHOR > 0	Halt; fix in Zotero, re-render refs.bib via /lit-sync
7.4	Self-review fix loop (/self-review --json --fix)	ENFORCED	score below threshold after 2 iterations	Route to Step 7.4a Audit Recovery
7.4a	Audit Recovery branch (route to /meta-analysis Phase 10 for MA manuscripts)	ENFORCED in --e2e	self-review surfaced structural data issue	HALT with RECOVERY_HALT_HUMAN_DECISION if recovery validation fails twice
7.5	Humanize density (/humanize)	ADVISORY	AI patterns > 2.0 / 1000 words	Sweep + flag remaining; user reviews
7.5a	AIO checklist (/academic-aio --aio)	OPT-IN	user supplies --aio flag	PASS/PARTIAL/FAIL report; never auto-applies
7.6	DOCX build (delegate /manage-refs scripts/render_pandoc.sh)	ENFORCED	render exits non-zero	Halt; report stderr to user
7.6a	Cross-reference QC (delegate /manage-refs scripts/check_xref.py --strict)	ENFORCED — submission gate	MISSING_DOCX / MISSING_BODY / MISMATCH > 0	Halt; route fixes per references/check_xref_symptoms.md
7.7	Final submission gate	ENFORCED	any of 7.0–7.6a above failed	Refuse to mark submission_safe: true
8+	Cover letter generation	OPT-IN	user invokes --cover-letter	Renders against journal profile

Cross-cutting global rules applied during 7.x QC:

• manuscript-style-classical.md (11 items, Phase 7.1 — ENFORCED)
• manuscript-references.md (hand-typed References list — ENFORCED via Phase 7.6 delegation)
• numerical-safety.md, data-integrity.md, citation-safety.md (Phase 7.3 + 7.6a — ENFORCED)
• senior-mentor-circulation.md (post-7.7, when round 1 begins — ADVISORY)
• ai-drafted-document-policy.md (Phase 0 if AI-draft attached — ENFORCED)