Sync Submission logo

Sync Submission

Organization
Aperivue
sync-submission

Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.

Overview

PublisherAperivue
Repositorymedsci-skills
Skill namesync-submission
Stars
349
Forks
82
Bundled files
52
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 52 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Aperivue on GitHub. Read the source before you install it.

Installation

Install the Sync Submission AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Aperivue/medsci-skills/tree/main/skills/sync-submission
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Aperivue/medsci-skills.git /tmp/medsci-skills
mkdir -p .claude/skills
cp -r /tmp/medsci-skills/skills/sync-submission .claude/skills/sync-submission
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sync Submission in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sync Submission on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sync Submission is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sync Submission

Keep the canonical manuscript and journal-specific submission packages from drifting apart. Treat submission/{journal}/ as derived output and record whether it is current, stale, or frozen.

Inputs

  1. Project root containing project.yaml, or a direct canonical manuscript path.
  2. Journal short name, e.g. chest, ryai, academic_radiology.
  3. Optional mode:
    • audit: compare existing submission against canonical source.
    • build: copy canonical source and optional declared final artifacts, preserving file bytes, and write metadata.
    • freeze: freeze the chosen byte snapshot with its available check context (not submission approval).

Deterministic Script

bash
python "${CLAUDE_SKILL_DIR}/scripts/sync_submission.py" audit --project-root . --journal chest
python "${CLAUDE_SKILL_DIR}/scripts/sync_submission.py" build --project-root . --journal chest
python "${CLAUDE_SKILL_DIR}/scripts/sync_submission.py" freeze --project-root . --journal chest --status submitted

For double-blind journals, sweep author identifiers across all upload artifacts:

bash
python "${CLAUDE_SKILL_DIR}/scripts/blind_sweep.py" \
  --registry _shared/authors/author_registry.yaml \
  --files submission/{journal}/supplementary/*.md submission/{journal}/cover_letter.md \
  --backup-dir .cache/blind_sweep_backup

The registry is a project-local YAML mapping author identifiers (full names, native scripts, initials with/without periods, email, ORCID) to role labels (e.g., "Reviewer 1"); schema in scripts/author_registry_example.yaml. Never commit a populated registry to a public repository, because it lists the authors' identities — keep it next to the manuscript.

Output Contract

ArtifactPathPurpose
Submission metadatasubmission/{journal}/.journal_meta.jsonSource hash, status, canonical path
Sync auditqc/submission_sync_{journal}.jsonDrift result consumed by orchestrator
Manifest updateartifact_manifest.jsonSubmission package registry
Pre-flight gateqc/preflight_gate_report.jsonAggregated halt-on-failure manifest (see "Pre-flight gate" below)
Supplement structureqc/supplement_structure.jsonGate 14: index↔file 1:1, sub-section gaps, callout coverage

For a complete bundle, run the existing renderers first, then build --bundle-spec bundle.json. The declaration adds final Word/PDF, supplement, cover-letter, table/figure and notice files with pinned render-input hashes and reuse-rights records. Copies preserve file bytes; content and visual fidelity stay not_assessed until separately reviewed. Build refuses edited or frozen outputs and destructive path collisions. Read bundle workflow for the schema, a runnable synthetic example and the limits of each recorded check.

Pre-flight gate (single command — last step before freeze)

Run this once, right before freeze/submission:

bash
python "${CLAUDE_SKILL_DIR}/scripts/preflight_gate.py" --project-root . --journal chest
# add --strict to also halt on the heuristic/conditional (P1) checks
# add --online to make fabricated / author-mismatched references halt (PubMed/CrossRef)
# add --double-blind to make the asset-anonymization scan halt

It shells out to the per-check scripts (reimplementing none), writes qc/preflight_gate_report.json, and exits 0 clean, 1 halt (≥1 blocker), 2 gate config error (e.g. a --require'd check could not run). A non-zero exit blocks the freeze.

  • P0 — halts by default (unambiguous, deterministic errors): leftover placeholder/markers (check_placeholders.py), undefined [@key] citations (check_citation_keys.py), duplicate references (verify_refs.py, offline-deterministic), a canonical-vs-submission hash mismatch (sync_submission.py audit), and an internal-audit dump in a reviewer-facing file (check_checklist_dump_leak.py).
  • P1 — runs and reports warn, halts only with --strict or --require ID: check_xref, detect_copy_divergence, scope_drift_check, cover_letter_drift_check, cross_document_n_check, check_cross_artifact_stale; check_asset_anonymization is P1 unless --double-blind.
  • A check whose inputs are absent (no rendered docx, no cover letter, no copies, no journal) is recorded skipped, never a blocker. A check that ran and failed — a traceback, an unexpected exit code, or a "missing input" exit while its inputs are present (malformed .journal_meta.json, an undecodable cover letter, a named --copy that does not exist, an unreadable file in the N scan) — is recorded error: submission_safe is false and the gate exits 2, with or without --strict.

Do not read the report as approval. Its legacy submission_safe field means only "no configured blocker/error"; readiness stays not_assessed; the optional bundle hash binding identifies the package present during the run, not per-file visual inspection or every external check input. Run audit again after preflight to expose current, stale or unbound evidence. Freeze records a byte snapshot and its available check context; it does not run preflight or approve source fidelity or reuse permissions.

Audit-dump leak check (P0). A /check-reporting or /self-review report is an internal working audit — auto-fix annotations, a raw JSON block (compliance_pct, fixable_by_ai, check_reporting_version), pipeline-log paths, "Action Items". It is not the official reporting checklist a journal expects and must never reach a reviewer, even when its filename looks official (e.g. STROBE_checklist_v4.pdf reused into a later package). scripts/check_checklist_dump_leak.py --dir submission/ scans every .md/.docx/.pdf in the package for these tokens; any hit is a P0 leak. The pre-flight runs it over the journal asset directory; confirm submission_safe: true before freeze. Writes qc/checklist_dump_leak.json.

Disclosure & availability check (standalone). Top medical-AI journals require, before review, an AI-use disclosure carrying four tokens (version + access channel + date/date-range + responsible party — the tool name only triggers the check) and Data/Code Availability statements. Run python3 ${CLAUDE_SKILL_DIR}/scripts/check_disclosure_availability.py --manuscript <file> --journal <stem> [--ai-study] [--require data_availability ...] [--strict] (reads references/journal_availability_policy.json). It blocks on a missing required statement or an AI disclosure that is present but missing a token / carrying a placeholder; "available on reasonable request" where the journal expects a repository is a P1 warning. Writes qc/disclosure_availability_report.json.

Workflow

  1. Resolve canonical manuscript from project.yaml or explicit input.
  2. Run the script in the requested mode.
  3. If audit reports DRIFT, do not retarget or freeze until the user either patches the canonical manuscript or records the difference as journal-only. Never silently merge submission edits back into the SSOT, and never hide a journal-only difference — record it as drift or an explicit exception.
  4. If build succeeds, run /verify-refs before final submission.
  5. Call a package current only when its source hashes match, and mark it submitted only through freeze, which writes .journal_meta.json.

Quality Gates

  • Gate 0 (pre-flight, last step before freeze): run scripts/preflight_gate.py as in "Pre-flight gate" above; non-zero exit blocks the freeze. It orchestrates Gates 1–3, 5b, 5c, 5d, 8, 9, 11, 11b, the Phase 3b/3c checks, and the placeholder and citation-key checks; each gate also runs on its own.

  • Gate 1: block freezing when canonical manuscript is missing.

  • Gate 2: block retargeting when the previous submission has unresolved drift.

  • Gate 3: require /verify-refs audit before marking a package submission-safe. The pre-flight's offline references pass covers only duplicates and pagination placeholders; an online /verify-refs --strict against PubMed/CrossRef is the authoritative fabrication and author-name check.

  • Gate 4 (recursive docx walk): every docx stale-string audit must walk paragraphs + tables + nested-table cells recursively. document.paragraphs skips table cells, document.tables does not recurse, and paragraph.runs hides runs inside <w:hyperlink> — and figures, captions and reporting checklists often sit in 1×1 or nested tables. For run-level edits near hyperlinks or fields, inspect the paragraph XML, not .runs: a hidden inline element can look like an empty () and be "fixed" into a real defect.

  • Gate 5 (portal free-text fields): cover letter, data availability, acknowledgements, abstract and author contributions are often typed into the portal, outside any docx this skill audits. Before freeze, diff the portal's final review page against the manuscript body 1:1 and treat each field as its own drift target.

  • Gate 5c (portal-field markdown residue): portal paste-verbatim .txt fields (abstract.txt, keywords.txt, …) are cut from the markdown but never stripped of it, so a trailing ---, a **bold**, or a cm^2^ superscript publishes literally. The pre-flight runs scripts/check_portal_field_residue.py --dir portal_fields/ (P1, --strict-promotable); only .txt is scanned (a .md is meant to carry markdown). Its Minor char_expansion advisory flags ≥/≤, which ScholarOne expands to "{greater than or equal to}" (five words), inflating the word count — pre-substitute >=/<= (only ≥/≤; × and the en-dash paste cleanly).

  • Gate 5d (figure portal readiness): a figure bounces at the upload button for reasons decidable from the file on disk — byte size (JACC: Asia caps a figure at 25 MB) and extension (SNAPP accepts only .tiff/.jpeg/.eps, rejecting .png). The pre-flight runs scripts/figure_portal_readiness_check.py --figures-dir <dir> (P1) over submission/<journal>/figures (or ./figures), emitting FIGURE_OVERSIZE and — when the portal's formats are supplied, e.g. --figure-accept tiff --figure-accept jpeg --figure-accept eps — FIGURE_FORMAT_REJECTED. Fix by regenerating with /make-figures export_portal_tiff.py (LZW + RGBA→RGB flatten). No figures directory → skipped, never an error.

  • Gate 6 (double-blind journals): a clean manuscript blind does not imply a clean portal blind. Before freeze, export the portal's blinded review PDF — the authoritative drift detector — and grep for all author identifiers across the entire upload set: manuscript; supplementary materials (especially methodology logs, agreement metrics, amendment logs); cover letter (a separately uploaded file is reviewer-visible unless toggled "Don't show in review PDF"); registry/approval PDFs (PROSPERO, ClinicalTrials.gov, IRB); portal Letter-field text if a signature was pasted; response-to-reviewers in revision rounds. Cover both period and no-period initials (G.S. and GS), full names in roman + native scripts, institution names, ORCID IDs and submission email domains.

  • Gate 7 (text-only docx rebuilds): never use pandoc --reference-doc=manuscript.docx for response/cover/supplementary text-only docx, because the reference docx ships its embedded media (figure files) into the new docx, bloating it 50–100×. Use plain pandoc input.md -o output.docx. If such a file grows past 100 KB, unzip -l output.docx | grep word/media/ should come back empty.

  • Gate 5b (cover-letter free-text drift): before freeze — see Phase 4.

  • Gate 8 (cross-document N consistency): before freeze — see Phase 5.

  • Gate 9 (intra-manuscript scope drift): see Phase 6.

  • Gate 10 (v_(N+1) docx regeneration): when building from a frozen prior version — see Phase 7.

  • Gate 11 (multi-copy divergence): before freeze or circulation — see Phase 8.

  • Gate 11b (reframe / headline-change survivor scan): after a revision that reframes a claim class (e.g. retires "location-stratified benchmark" for "overall pooled") or changes a headline number, the old term/value often survives in an untouched paragraph, legend, the supplement or the response letter — even when the letter claims the change was applied "throughout". Pass the retired vocabulary and superseded values from the reframe diff to the cross-artifact gate, which scans the body and every aux artifact:

    bash
    python3 "${CLAUDE_SKILL_DIR}/scripts/check_cross_artifact_stale.py" \
        --manuscript manuscript.md --aux supplement/ --aux figures/legends.md --aux revision/response_to_reviewers.md \
        --retired-term "location-stratified benchmark" --old-value 1.72

    A retired_framing_survivor / stale_old_value finding is a P1 stale claim-site. --aux takes files or folders: .docx sidecars are read, and figure scripts (.py/.R) are swept for stale literals only. An --aux that holds no readable file exits 2 instead of passing, and unreadable documents (.pdf, .pptx, …) are listed as not checked. For any wording or number change, also grep the OLD string across the entire SSOT tree, never a subset, and watch for substring near-misses — an exact grep for expertise-dependent patterns passes while expertise-dependent evaluation patterns stays stale.

  • Gate 12 (target-journal metadata drift): on build / retarget, compare the target the manuscript is written for — project.yaml target (and any in-manuscript header/footer "for submission to X" string) — against the journal the package is built for, and check the structural metadata the target dictates — abstract heading structure (4- vs 5-heading), body word limit, citation style (Vancouver / AMA), required elements (Highlights / Central Illustration / Key Points). A mismatch (e.g., a header still reading the previous journal after a cascade retarget, or a 4-heading abstract for a 5-heading target) is a target-restructure trigger — branch to v_(N+1) and sync every sidecar (cover letter, title page, ICMJE COI list) — not a silent build.

    bash
    # header target vs project.yaml target
    TGT=$(python3 -c "import yaml;print(yaml.safe_load(open('project.yaml')).get('target',''))" 2>/dev/null)
    grep -niE 'for submission to|submitted to|prepared for' manuscript/manuscript.md   # compare against "$TGT"
  • Gate 13 (body word count vs journal cap — the revision-inflation trap): resolving reviewer majors adds words, so a revised body silently breaches the journal's limit. Before freeze and after every /revise pass, run scripts/check_wordcount_cap.py against the target journal profile's body cap. WORDCOUNT_OVER_CAP is a P0 (relocate methods/sensitivity detail to the Supplement); WORDCOUNT_NEAR_CAP (>0.95×) warns that the next pass will breach. The binding number is the rendered count (citeproc expands [@key] → "(Author Year)"), so prefer the built DOCX count with --rendered-words N; otherwise the script estimates it from the markdown body + inline-citation expansion.

    bash
    python3 "${CLAUDE_SKILL_DIR}/scripts/check_wordcount_cap.py" \
      --manuscript manuscript/manuscript.md \
      --journal-profile "${CLAUDE_SKILL_DIR}/../find-journal/references/journal_profiles/<Journal>.md" \
      --article-type "Original Article" --out qc/wordcount_cap.json --strict
    # or, deterministic: --limit <the journal's body cap>   (and --rendered-words N from the built DOCX when available)

    The profile cap is read only from a structured field: the body-limit column of an article-type table (| Type | Body Word Limit | Abstract | ... |), or, when no table carries the type, a list item that starts with it (- Original Article (4,000 words, ...)). Prose that merely mentions the type (often an abstract limit) is never read; when no single number results, or the cell holds more than one number, the script exits 2 and asks for --limit.

  • Gate 14 (supplement structure — the numbering lock): a supplement of S{N}_*.md sections plus an index, hand-concatenated into _combined.md, desynchronizes across revision rounds — an index row with no file, a file the index never lists, two files claiming the same S{N}, a sub-section gap after an insert (S6.3 then S6.5) — and "Supplementary Table S9" opens the wrong content. Before freeze, run scripts/assemble_supplement.py to validate index↔file 1:1, rebuild _combined.md in index order (reproducible rather than hand-maintained), and — with --manuscript — report body callouts with no section file (CALLOUT_WITHOUT_SECTION) and section files the body never cites (SECTION_UNCITED). The four structural kinds are P0 under --strict; coverage findings are advisory.

    bash
    python3 "${CLAUDE_SKILL_DIR}/scripts/assemble_supplement.py" \
      --dir submission/{journal}/supplementary --index 00_index.md \
      --manuscript manuscript/manuscript.md \
      --out submission/{journal}/supplementary/_combined.md \
      --json qc/supplement_structure.json --strict

Phase 3b — Portal fields that REPLACE the manuscript

On some portals the box, not the manuscript, is what gets published. SNAPP says so at Author Contributions, Competing Interests, Data Availability and Acknowledgements: "This replaces any statement written within the manuscript and is the one that we will publish." A declaration that lives only in the manuscript then vanishes from the published record, and nothing warns you. Two that nearly did:

  • Co-first authorship. A † title-page footnote. There is no equal-contribution checkbox — unless "X and Y contributed equally to this work" is typed into the Author Contributions box, the published paper has no co-first authors.
  • "The funder had no role in study design…" The structured Research funding field takes a funder and a grant ID and has nowhere to put a role disclaimer, so pasting only an AI-use note into the Acknowledgements box drops it.

Do not hand-compose the boxes. Generate them from the manuscript, then check:

bash
SS="${CLAUDE_SKILL_DIR}/scripts"
# scaffold every replacing field straight from the manuscript (lifts the equal-contribution
# sentence in from the title page, which is the one place --emit cannot copy it from)
python3 "$SS/check_portal_mirror.py" --manuscript manuscript/manuscript.md \
  --profile "<...>/journal_profiles/npj_Digital_Medicine.md" --emit portal_fields/

# then verify nothing was lost on the way to the box
python3 "$SS/check_portal_mirror.py" --manuscript manuscript/manuscript.md \
  --portal-dir portal_fields/ --profile "<...>/npj_Digital_Medicine.md" \
  --out qc/portal_mirror.json
VerdictFires when
PORTAL_FIELD_NOT_MIRROREDA sentence in a replacing manuscript section has no home in that field's paste artifact.
PORTAL_FIELD_MISSINGThe manuscript has the section, the journal replaces it, and no artifact exists — the field publishes empty or as the portal's auto-extraction guessed it.
EQUAL_CONTRIBUTION_NOT_IN_PORTALThe manuscript asserts equal / co-first contribution and the Author Contributions text does not.

All three are major and exit 1; the pre-flight runs this as P1 (--strict-promotable).

Which fields replace is a journal fact, not a guess. It is read from the journal profile's ## Portal Mechanics block (Fields that REPLACE the manuscript: …). For a journal whose portal contract was never recorded the check exits 2 and asserts nothing — record the block at first submission rather than inventing a contract. Matching is graded through _quote_match.py, so a sentence re-flowed while pasting is not reported as lost.

Phase 3c — CRediT integrity (not author order)

A contribution taxonomy is a published factual claim, but the work behind a term often leaves no repository artifact (a legitimate Conceptualization may live only in email). So the taxonomy is gated and corroboration is only a prompt.

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/check_credit_integrity.py" \
  --manuscript manuscript/manuscript.md --out qc/credit_integrity.json
VerdictSeverityFires when
CREDIT_TERM_INVALIDmajorA term outside the official fourteen in a section that says CRediT — "Statistical analysis", "Manuscript writing", "Study design" read as CRediT and are not. The message names the intended term.
CREDIT_INITIALS_UNRESOLVEDmajorInitials matching no author, or two — the residue a byline edit leaves.
CREDIT_AUTHOR_UNLISTEDmajorA byline author with no contribution attributed (under ICMJE, an authorship question or a dropped clause).
CREDIT_UNCORROBORATEDpromptA term whose footprint is absent — Visualization on a paper with no figures, Software with no Code Availability statement, or (only with --contribution-record) a contributor absent from the record.

Never gate author order or equal-contribution designation — they are negotiated, and negotiation is legitimate. Answer a corroboration prompt with an attestation; do not fail the build on an off-repo contribution. With fewer than two resolvable byline names the author/initials cross-check is skipped and says so; with no contributions section the script exits 2 and asserts nothing.

Phase 4 — Cover-letter free-text drift

The cover letter's ## Article details block — body word count, abstract word count, reference count, table/figure count — is a sidecar that goes stale when a manuscript branches v_N → v_(N+1) (word-limit retarget, abstract restructure, late reference batch), and no docx-level audit covers it. scripts/cover_letter_drift_check.py measures the manuscript and compares it to the letter's numeric claims:

bash
python "${CLAUDE_SKILL_DIR}/scripts/cover_letter_drift_check.py" \
    --manuscript manuscript.md \
    --cover-letter cover_letter.md \
    --refs refs.bib \
    --out qc/cover_letter_drift.json

Reference / table / figure counts must match exactly; abstract words tolerate ±5; body words tolerate 5%, narrowed to the headroom when the letter states the count against the cap ("3,998/4,000 words"). Resolve drift by regenerating the cover letter from the manuscript at v_(N+1) build time. The script never edits the cover letter, which stays a deliberate authored artifact.

Phase 5 — Cross-document N consistency

Abstract, body prose, PROSPERO record, cover letter, supplementary extraction sheets, INDEX and PRISMA flow caption all repeat the same k included / k excluded / N patients totals, and any disagreement reads to reviewers as a data-integrity or late-edit failure. scripts/cross_document_n_check.py extracts every "N " claim by category (patients, cases, included, excluded, nodules, tumors, studies_total); a category with more than one distinct integer value is a P0 drift. With --root it scans the manuscript, abstract, PROSPERO, root and per-journal (submission/<journal>/) cover letters, supplement/ and supplementary/. A matched file that cannot be read as UTF-8 is listed under unreadable_files (never files_scanned) and the script exits 2, so incomplete coverage cannot read as a pass.

bash
python "${CLAUDE_SKILL_DIR}/scripts/cross_document_n_check.py" \
    --root . \
    --out qc/cross_document_n.json

When the project has frozen a 2_Data/FINAL_POOL_LOCK.yaml from /meta-analysis Phase 3f.5, pass it as the authoritative anchor:

bash
python "${CLAUDE_SKILL_DIR}/scripts/cross_document_n_check.py" \
    --root . \
    --pool-lock 2_Data/FINAL_POOL_LOCK.yaml \
    --out qc/cross_document_n.json

Treat submission_safe: false in qc/cross_document_n.json as a halt. Resolve drift by tracing each location to its data artifact (extraction sheet, PRISMA cascade TSVs) and correcting the document(s) that disagree with the locked count.

Phase 6 — Intra-manuscript scope drift

scripts/scope_drift_check.py detects two P0 patterns:

  • SCOPE_DRIFT — a numeric anchor (AUC, OR/HR/RR, sensitivity/specificity) in Limitations / Discussion but absent from Methods + Results, typically a late sensitivity analysis whose primary report never exists.
  • PROSPERO_DRIFT — the PROSPERO record commits to one synthesis method (Freeman-Tukey, random-effects DerSimonian-Laird, bivariate, HSROC, Bayesian, etc.) and Methods uses another, or the record was updated and Methods stayed behind. With a Methods line saying "no amendment lodged", this is a documented silent protocol deviation.
bash
python "${CLAUDE_SKILL_DIR}/scripts/scope_drift_check.py" \
    --manuscript manuscript.md \
    --prospero prospero/prospero_v2.md \
    --out qc/scope_drift.json

Resolution: either (a) propagate the anchor into Methods + Results as a primary report or (b) remove it from Limitations / Discussion. For synthesis-method drift, file a PROSPERO amendment and update Methods to match — both must agree before submission.

PROSPERO's public-record "Print/PDF" export renders only the current amendment; older versions are reachable only through the version-history dropdown. When citing PROSPERO version state, never rely on a single PDF export — save each published version's PDF independently and state in the cover letter/supplement which version anchors the methodology and which reflects a documentation-only erratum. For such an erratum (a narrative fact, no change to methods/eligibility/synthesis), prefer a single Revision-Note append over a new structured amendment.

Phase 7 — v_(N+1) docx regeneration gate

When a v_(N+1) is built from a frozen v_N package (after a markdown body edit, reviewer round, or cascade-rejection re-target), the v_(N+1) docx MUST differ from the v_N docx. The common silent revert is a cp v_N/manuscript.docx v_(N+1)/manuscript.docx step that skips the pandoc / Zotero CWYW regeneration: the markdown is edited, but the portal receives the frozen v_N docx. Run the byte-identity assertion at the top of the v_(N+1) submission gate — even when the upstream pipeline appears to have regenerated the docx:

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/verify_package_integrity.py" \
    --assert-vN-docx-changed \
    --vN-docx SUBMISSION/<journal>/v<N>/manuscript.docx \
    --new-docx SUBMISSION/<journal>/v<N+1>/manuscript.docx

Identical MD5 → exit 1. Block submission until the regeneration step is fixed.

Phase 8 — Multi-copy manuscript divergence

When a project hand-maintains several manuscript copies — manuscript.md (the working SSOT), manuscript_circulation.md (co-author feedback), and submission/<journal>/manuscript.md (portal) — SSOT edits routinely land in only some copies. Before freezing a package or sending a circulation round, run the directional detector (SSOT → each copy):

bash
python3 ${CLAUDE_SKILL_DIR}/scripts/detect_copy_divergence.py \
  --ssot manuscript.md \
  --copy manuscript_circulation.md \
  --copy submission/<journal>/manuscript.md \
  --out qc/copy_divergence.json --strict

It reports, per copy, the SSOT claims (numeric assertions — n = N, percentages, p, OR/HR/RR, 95% CI — and section headings) that did not propagate. A STALE_COPY (DIVERGENT overall) is a P0 blocker: re-propagate the claims, or — better — stop hand-maintaining parallel copies and generate the circulation / submission variants from the single SSOT via a build step (pandoc transform). Only a changed or absent number/heading registers, not wording. A numeric claim present only in the copy (stale_in_copy, e.g. an old n = 118 left beside the propagated n = 120) also makes the copy STALE_COPY; a copy-only heading (a circulation cover note) is listed in copy_only but does not. A --copy path that does not exist exits 2.

Phase 9 — Springer Editorial Manager packaging (no title-page slot)

Read ${CLAUDE_SKILL_DIR}/references/springer_em_packaging.md when a Springer Editorial Manager journal offers only Manuscript / Figure / Table / Supplementary / LaTeX upload item types (no Title Page or Cover Letter slot).

Phase 10 — Marked (tracked-changes) manuscript for a revision round

Every revision round asks for a marked manuscript: the revised paper with tracked changes against the version the reviewers saw.

The baseline is R0, not the previous round. The base of the diff is always the originally reviewed submission; only the target advances each round. An editor wants every change made since the version under review, so do not diff v7 against v8.

Word's Compare is the only safe producer — but it is scriptable. pandiff and LibreOffice --compare corrupt OOXML on real manuscripts (tables collapse, affiliation superscripts are lost); do not use them. Word for Mac exposes compare through AppleScript with author name, so the build needs no GUI pass and no post-hoc rewriting of w:author:

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/build_marked_manuscript.py" \
  --original submission/{journal}/R0/manuscript.docx \
  --revised  submission/{journal}/R1/manuscript_clean.docx \
  --out      submission/{journal}/R1/manuscript_marked.docx \
  --author   "Submitting Author" --line-numbers

(macOS + Word only. On any other platform, produce the marked file in Word by hand — then still run the gate below.)

The gate: a round trip, not a grep

"The marked file contains sentence X" passes even when Compare has dropped a paragraph, duplicated one, or split the revisions between two authors. Verify by construction — accepting every revision must reproduce the revised manuscript exactly, and rejecting every revision must reproduce the original:

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/check_marked_manuscript.py" \
  --marked   submission/{journal}/R1/manuscript_marked.docx \
  --original submission/{journal}/R0/manuscript.docx \
  --revised  submission/{journal}/R1/manuscript_clean.docx \
  --author   "Submitting Author" --strict

The gate is move-aware: Word encodes relocated content as w:moveFrom / w:moveTo, not w:ins / w:del, and a verifier that knew only insert/delete would see a moved paragraph twice and call a good file corrupt. Read ${CLAUDE_SKILL_DIR}/references/marked_manuscript.md when the gate reports a verdict, before writing any other docx probe, or when the marked file is too large to upload.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sync Submission AI skill do?

Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.

Why use Sync Submission on TypingMind?

Because you install it once and use it with any model. Sync Submission is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sync Submission in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Aperivue/medsci-skills/tree/main/skills/sync-submission. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sync Submission?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sync Submission?

As many as you like. As long as a model supports skills, you can use Sync Submission with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sync Submission AI skill free?

Yes. It is published on GitHub by Aperivue under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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