• Communicate with the user in their preferred language.
• All output documents, code, and checklists in English.
• Medical terminology always in English.

• PROSPERO template:

  ${CLAUDE_SKILL_DIR}/references/PROSPERO_template.md

  -- field-by-field guide with word limits, pitfalls checklist
• ICMJE COI guide:

  ${CLAUDE_SKILL_DIR}/references/icmje_coi_guide.md

  -- batch generation, python-docx pitfalls, form structure
• R templates:

  ${CLAUDE_SKILL_DIR}/references/r_templates.md

• Checklists:

  ${CLAUDE_SKILL_DIR}/references/checklists/

  • PRISMA_DTA.md

    -- 27-item checklist

  • QUADAS3.md

    -- current recommended DTA tool: 6 phases, 4 domains, 20 signalling questions, assessed per accuracy estimate

  • QUADAS2.md

    -- the 2011 tool: 4 domains + 10 signalling questions (use when appraising or reproducing a review that used it)

  • ROBINS_I.md

    -- 7 domains + pre-assessment + synthesis recommendation

  • RoB2.md

    -- 5 domains + signalling questions + overall judgment

  • PROBAST.md

    -- 4 domains + AI extension + validation studies

  • NOS.md

    -- Cohort (8 items) + Case-control (8 items) + star interpretation

  • JBI_Case_Series.md

    -- 10-item critical appraisal checklist for case series
• Phase 9 Co-author Circulation:

  ${CLAUDE_SKILL_DIR}/references/phase9_circulation.md

  -- thread continuity, attachment scope, recipient structure, 7-day window
• Phase 10 Self-Audit Recovery:

  ${CLAUDE_SKILL_DIR}/references/phase10_recovery.md

  -- trigger conditions, 12-step rebuild sprint, PROSPERO amendment, re-circulation framing
• Data integrity checklist:

  ${CLAUDE_SKILL_DIR}/references/data_integrity_checklist.md

  -- DI-1~DI-9 extraction/synthesis guardrails (prior anonymized MA projects)
• Review orchestration:

  ${CLAUDE_SKILL_DIR}/references/review_orchestration.md

  -- RO-1~RO-5 circulation discipline (extends phase9_circulation.md)
• Submission package drift:

  ${CLAUDE_SKILL_DIR}/references/submission_package_drift.md

  -- multi-journal folder hygiene,

  DO_NOT_EDIT_HERE

  gate,

  _build.sh

  pattern
• Post-submission release ops:

  ${CLAUDE_SKILL_DIR}/references/post_submission_release_ops.md

  -- Zenodo DOI gating, tag-cleanup gates, reject-retarget versioning
• Empirical peer-review lessons:

  ${CLAUDE_SKILL_DIR}/references/empirical_lessons.md

  -- 16 accumulated SR-MA peer-review / submission lessons (2026-05/06) that drive the Phase 4 extraction-form schema, Phase 4c QC, and Phase 8 submission gates. Load before designing the extraction form and before submission.

• Extraction Form v2 (

  templates/extraction_form_v2.md

  ) -- dual-extractor schema with

  source_page_ref

  ,

  source_verbatim_quote

  ,

  cohort_source

  ,

  overlap_flag_reviewer1/2

  ,

  sample_n_dta_pool

  vs

  sample_n_prognostic_pool

  columns. Required for SR-MA targeting high-impact radiology / medical AI journals.
• Supplementary 8-file Checklist (

  templates/supplementary_8file_checklist.md

  ) -- S1-S8 mandatory package (PRISMA, PROSPERO, search strategy, exclusion list, extraction table, per-study x per-domain RoB, subgroup forests, sensitivity / publication bias) with a submission-gate bash check.

• screening_reconcile.py

  -- Phase 3f ID-set screening reconciliation.

• check_pool_consistency.py

  -- pool-composition / PRISMA count consistency.

• cohort_overlap_check.py

  -- shared-database cohort-overlap detection.

• extract_assist.py

  -- Phase 4 AI-assisted extraction suggestions (page ref + verbatim quote,

  AI_SUGGESTED

  /

  needs_review

  ); human-confirm then

  dta_extraction_qc.py

  . Challenge card:

  scripts/extract_assist_challenge/

  .

• dta_extraction_qc.py

  -- 2x2 cell ↔ source sens/spec QC on the confirmed extraction CSV.

1. Structure the research question:
   • DTA: PIRD (Population, Index test, Reference standard, Diagnosis)
   • Intervention: PICO (Population, Intervention, Comparator, Outcome)
2. DTA only — do QUADAS-3 phases 1 and 2 now, not at risk-of-bias time: QUADAS-3's first two phases are review-level and belong in the protocol: phase 1 states the synthesis question(s) (population, index test(s), target condition — a review may have more than one), and phase 2 defines the ideal test accuracy trial for each: objective, participants, index test(s), definition of the target condition, analysis. Every later risk-of-bias and applicability judgement is made against that trial. Write the review-specific guidance for answering each signalling question here too, with clinical and methodological input, and publish it as a web appendix. Defining the ideal trial after seeing the studies is not an assessment — it is a judgement fitted to the results. See

   references/checklists/QUADAS3.md

   .
3. Define eligibility criteria:
   • Study design (cross-sectional DTA, cohort, RCT, etc.)
   • Population characteristics
   • Index test / intervention specifics
   • Comparator / reference standard
   • Outcome measures (Se/Sp for DTA; effect size for intervention)
   • Exclusion criteria with justification
4. Plan the search:
   • Minimum 3 databases: PubMed, Embase, and Cochrane CENTRAL (add Scopus, Web of Science as needed)
   • Draft Boolean search strategy using PIRD/PICO components
   • Grey literature plan (conference abstracts, trial registries)
   • Language restrictions (state explicitly)
   • Date range with justification
5. Plan RoB assessment:
   • Select tool based on type (see table above)
   • State number of independent assessors (minimum 2)
   • Plan for disagreement resolution (consensus, third reviewer)
6. Plan synthesis:
   • DTA: bivariate random-effects model (Reitsma) or HSROC (Rutter & Gatsonis)
   • Intervention: random-effects (DerSimonian-Laird or REML)
   • Heterogeneity assessment plan
   • Subgroup / sensitivity analysis plan
   • Publication bias assessment plan
7. Generate PROSPERO registration document:
   • Read

     ${CLAUDE_SKILL_DIR}/references/PROSPERO_template.md

     for field-by-field guidance
   • Generate all fields with word counts (stay within limits per field)
   • Structure: title, review question, PICO, searches, data collection, outcomes, synthesis, subgroups, stage, affiliation
   • Registration-ID format gate. A PROSPERO ID is

     CRD42

     + 9 digits (14 characters total), e.g.

     CRD42024500001

     . Validate any ID that appears in the manuscript or registration doc with

     grep -oE 'CRD42[0-9]+'

     and assert a 14-character length /

     ^CRD42\d{9}$

     — a 15-character ID (a stray digit) is a transcription error a reviewer will check against the live record.
   • Review-type selection. Pick the least-wrong portal review type for the actual design and state any portal constraint in the protocol. A descriptive single-arm proportion synthesis is not an "Intervention review"; choosing "Intervention review" only to satisfy a portal field contradicts a later GRADE / effect-certainty statement. Whatever certainty language the protocol commits to (GRADE vs "evidence statements only") must match the manuscript verbatim — a guideline-style "we recommend" is not licensed by a descriptive review type.
   • For mixed designs (comparative + single-arm): explicitly address comparator for both arms
   • For RoB: map tool to study design (NOS for comparative, JBI for case series → select "Other" in form)
   • Output: Markdown + DOCX (via pandoc) for copy-paste into PROSPERO web form
   • Append Common Pitfalls Checklist (HTML entities, word limits, stage constraint)
   • Save to project

     7_Submission/

     or equivalent directory

1. Build search blocks from PIRD/PICO:
   • Population block (MeSH + free text)
   • Index test / Intervention block
   • Comparator / Reference standard block (optional)
   • Study design filter (if applicable)
2. Combine with Boolean operators:
   • Within blocks: OR
   • Between blocks: AND
3. Execute search per database using

   /search-lit

   :
   • PubMed: MeSH + free text
   • Embase: Emtree + free text
   • Additional databases as specified in protocol
4. Report search per PRISMA-S (Rethlefsen et al. 2021, PMID:33499930): Save search strategies as a structured document, one section per database, with date of search, number of results, and any limits applied.
5. Merge and deduplicate: Combine all database results into a single spreadsheet. Deduplicate by DOI first, then PMID. Save raw counts for PRISMA flow.

round1_{date}.tsv

round2_tag

round2_tag

round2_reason

round3_decision

round3_reason

round3_decision = INCLUDE

/fulltext-retrieval

/make-figures

python "${CLAUDE_SKILL_DIR}/scripts/screening_reconcile.py" \
  --screening 2_Screening/fulltext_screening.tsv \
  --consensus 2_Screening/consensus_decisions.tsv \
  --table1 6_Tables/table1_studies.csv \
  --output 2_Screening/screening_consensus.json

screening_consensus.json

1. List the narrative-only IDs explicitly. The highest-yield red flag is a numeric claim ("10 narrative-only studies") that does not match the enumerable set

   (A ∪ C) \ B \ T

   .
2. No "N → M" transition without ID receipts. "k rose from 30 to 32 after FLAG consensus" must cite the added/removed IDs. A transition claim with no enumerable ID set is a P0 and blocks the Phase 5 hand-off.

3. STAGE_TRANSFER_LOSS

   is a P0. Exit 1 when a record is included at screening but absent from the consensus artifact altogether — no adjudication was ever recorded. An exclusion is a decision; silence is a gap. Never let it settle into narrative-only (why: reference file).

cp "${CLAUDE_SKILL_DIR}/templates/FINAL_POOL_LOCK.yaml.template" 2_Data/FINAL_POOL_LOCK.yaml

FINAL_POOL_LOCK.yaml

python "${CLAUDE_SKILL_DIR}/scripts/check_pool_consistency.py" \
    --lock 2_Data/FINAL_POOL_LOCK.yaml \
    --adjudication-tsv 2_Screening/round3_adjudication.tsv \
    --decision-col round3_decision --uid-col uid \
    --include-labels "INCLUDE,INCLUDE_MIXED" \
    --out qc/pool_consistency.json

Failure-mode cross-ref →

references/data_integrity_checklist.md

DI-1~DI-5 are mandatory during extraction (2x2 arm-swap, KM audit trail, methodology mismatch, PRISMA 5-way drift, single-source k).

${CLAUDE_SKILL_DIR}/templates/extraction_form_v2.md

_DO_NOT_USE_VERBATIM

${CLAUDE_SKILL_DIR}/references/phase4_km_composite.md

IPDfromKM

event ÷ denominator

{project}/consensus_log.md

undefined

Failure-mode cross-ref →

references/data_integrity_checklist.md

DI-6/DI-7/DI-9 are the consistency gate (CSV ↔ script ↔ prose; single-source k; 3-way numeric reconciliation before Stage 4).

meta

metafor

mada

${CLAUDE_SKILL_DIR}/references/r_templates.md

mada::reitsma()

meta::metagen()

meta::metabin()

metabin

metaprop

${CLAUDE_SKILL_DIR}/references/phase6_statistical_synthesis.md

1. Is the event rare? A pooled event rate < 1%, or any zero-event arm, moves the analysis off the inverse-variance default onto Peto / Mantel-Haenszel without a zero-cell correction / GLMM. Inverse-variance methods including DerSimonian-Laird are to be avoided for rare events, and so are 0.5 continuity corrections with them.
2. Why this model? Fixed vs random is a judgment about whether one common true effect exists — never derived from Cochran's Q or I². "A random-effects model was used because I² was 65%" is a reviewer catch, not a rationale.
3. Does one study contribute several correlated effect sizes? Multiple outcomes, readers, thresholds, or time points from the same participants need one pre-specified estimate per study, a multivariate model, or robust variance estimation — not independent pooling.

A safety outcome is reported with its arm-level events, and therefore its p-value, direction-reversed relative to what the primary-source Table actually recorded. The extraction CSV is correct; the R script's Fisher exact

matrix()

was hand-typed after a column in the source Table was misread. Internal consistency checks passed because every downstream artifact (Abstract, Discussion, Table, forest caption) echoed the same wrong number. The reversal was caught only on a second-pass audit with random extraction sampling against the primary paper.

1. No hand-typed numerical matrices when a CSV exists.
   • Use

     read.csv(...)

     + subset / filter. Never copy a 2x2 table from a paper's Table into

     matrix(c(...), ...)

     by eye.
   • If hand entry is truly unavoidable (e.g., text-only extraction), the

     matrix

     ,

     c()

     , or

     data.frame

     line MUST carry a comment citing the exact CSV row + column OR the exact primary-source Table/Page coordinate. Example:
     r

     # source: data_extraction_final.csv row <N> (<first-author> <year>), cols <event_arm1>=0, <event_arm2>=1
     # verified against primary source Table <X>, page <P>
     fisher.test(matrix(c(0, 45, 1, 55), nrow = 2, byrow = FALSE))

2. Comparative-arm subsets are a separate consensus-log row.
   • When one study's arm-specific values (e.g., one arm of a multi-arm study) are used in a comparative analysis while the full cohort of that study appears elsewhere,

     extraction_consensus_log.md

     must carry an explicit row for the arm-specific values. Pooled totals and arm-specific values MUST NOT share a row.
3. Random 3-claim back-check before closing Phase 6.
   • After the forest/funnel/subgroup outputs stabilize, randomly sample 3 numerical claims from the Results section of the draft manuscript and trace each back to (a) the R output log and (b) the original paper's Table/Figure.
   • Record the back-check as a small table in

     peer_review_<vN>_internal.md

     :

     Claim (manuscript line)	R output file:line	Primary source (paper, Table/Fig, page)	Match?

   • A single mismatch is a P0 blocker — do not advance to Phase 7 until resolved.
4. Revision-introduced numbers must be tagged.
   • Any new number added after v1 — including numbers produced by a new comparative / subgroup / sensitivity script — MUST be wrapped inline as

     [VERIFY-CSV]

     in the manuscript until the Phase 2.5a audit in

     /self-review

     clears it.
5. Sensitivity analyses must be recomputed on the modified data, not copied.
   • When you add a sensitivity / leave-one-out / erosion / alternative-model analysis, every reported effect size (Cohen's dz/f, AUC, OR, HR, β, sens/spec, ICC) MUST be re-derived from the modified dataset. If a sensitivity-table effect size is identical to the primary analysis to two decimals across ≥4 values, the recomputation almost certainly did not run and the primary values were transcribed — re-run the script on the modified data.
   • The underlying means/SDs/counts will change even when the effect size looks similar; if the effect sizes are byte-identical while the inputs differ, that is the tell. Probability of ≥4 independent values coinciding to 2 decimals by chance is ≈ (0.01)^4 — essentially zero.
   • The failure it catches: a sensitivity analysis reports a block of effect-size values byte-identical to the primary tables while the underlying means/SDs differ — the sensitivity analysis was never actually recomputed. Internal consistency cannot see it.
6. A "fixed" / "resolved" audit note requires re-run evidence, not a claim.
   • When a prior audit note records a number as

     fixed

     ,

     resolved

     , or

     corrected

     , that status is only valid if it carries the re-run evidence: a timestamp and the relevant stdout / output-file line showing the corrected value, or the commit that changed it. A bare "fixed in v10" with no re-run artifact does NOT clear the finding — re-run the script and attach the output.
   • The forward pipeline can echo a stale value through every artifact while an audit note claims it was fixed (e.g., a major-comparison N still reading the old total after a "fixed" note). The outcome-denominator cross-check (

     /self-review

     Phase 2.5b, the cohort-arithmetic / pool-lock assertions) must pass against the current outputs before any "fixed" status is accepted.

1. Risk of bias (from QUADAS-3, or QUADAS-2 for a legacy review)
2. Indirectness (applicability concerns)
3. Inconsistency (heterogeneity)
4. Imprecision (wide CIs, small sample)
5. Publication bias

Failure-mode cross-ref →

references/submission_package_drift.md

— apply the

_build.sh

pattern +

DO_NOT_EDIT_HERE

gate when staging multi-journal submission folders.

1. Check reporting compliance: Use

   /check-reporting

   with PRISMA-DTA or PRISMA 2020, then run it a second time over the abstract with PRISMA 2020 for Abstracts — 12 items, its own denominator. One run does not cover both.
2. Write manuscript: Use

   /write-paper

   with meta-analysis type selected
3. Figures: Use

   /make-figures

   for:
   • PRISMA flow diagram
   • Forest plots (paired for DTA)
   • SROC curve (DTA)
   • Funnel plot
   • RoB summary (traffic light plot)
4. Tables:
   • Characteristics of included studies
   • 2x2 data per study (DTA)
   • RoB assessment results
   • Summary of findings / GRADE table (one row per outcome — Phase 7)
5. The items published radiology SR/MA most often drop. Park 2022 (Korean J Radiol; PMID:35213097) scored 24 SR/MAs against PRISMA 2020 and found 24 of 42 items reported by fewer than 80%. The checklist itself lives in

   /check-reporting

   ; what follows is where drafts actually fail, so check these by hand before the compliance run rather than after it:

   PRISMA item	What is missing	Observed
   20a	For each synthesis, a brief summary of the contributing studies' characteristics and risk of bias — not one global paragraph covering all pools	0/24
   27	Data availability: which of the extraction forms, extracted data, analysis dataset, and analytic code are public, and where	0/24
   24a–c	Registration number, where the protocol can be read, and any amendment — an explicit "not registered" satisfies 24a	0/24
   22 / 15	Certainty of evidence per outcome, and the method used to assess it	9%
   13f / 20d	Sensitivity analysis: method and result	28%
   18	Risk of bias per study, shown study-by-study rather than as a pooled proportion	32%
   13d	Rationale for the synthesis model (see Phase 6 check 2)	35%
   16b	Studies that look eligible but were excluded, cited individually with the reason	25%
   Abstract #3, #12	Eligibility criteria and registration inside the structured abstract	0/24 each

   The abstract items are the cheapest of these and the most reliably forgotten. PRISMA 2020 devotes a separate 12-item instrument to the abstract — item 2 of the main checklist does nothing but defer to it — so a manuscript can satisfy all 42 main-text items and still fail most of the twelve.

   /check-reporting

   carries it as

   PRISMA_2020_Abstracts.md

   ; run it as its own pass and report its score separately, because folding twelve items into a 42-item total is how they stay invisible.
6. Data availability statement: name what is being shared (extraction template, locked dataset, analysis code, RoB judgments) and where — repository, DOI, or supplementary file. "Available from the corresponding author on reasonable request" satisfies few journals now and no longer satisfies item 27. If a Zenodo DOI is minted post-acceptance,

   references/post_submission_release_ops.md

   covers propagating it back into this statement.
7. Supplementary & analysis-code pre-submission gate (run before Phase 9 circulation and before portal upload). Presence of the 8-file package (Empirical Lesson 5) is necessary but not sufficient — each item must also be reviewer-ready:
   • De-scaffold: strip internal-QC / tool artifacts before bundling — raw

     /check-reporting

     output ("Assessed by: <tool>", JSON blocks, "READY FOR SUBMISSION" verdicts, action-item lists), search-development planning docs (decision logs, expected-yield estimates,

     [Check on execution]

     placeholders, version-history dev notes), and stale version stamps. Ship a clean PRISMA 2020 checklist (27-item / 42-subitem table only) and an executed-method search-strategy doc, not the working drafts.
   • Blind: supplementary goes to reviewers — remove author names/initials and sibling-project cross-references ("Designed by: <name>", "identical to a sibling review"). Same standard as the blinded manuscript.
   • Cross-consistency with the manuscript: every supplementary number must match the main text — PRISMA counts, pool k/N, the Cochrane/CENTRAL search description, RoB counts. A supplement that says "Cochrane — NOT SEARCHED" while Methods report a confirmatory CENTRAL search is a contradiction reviewers catch.
   • Submitted analysis code must reproduce and be self-contained: run it from a clean copy of the bundle. It must (a) read the bundled locked dataset (not an out-of-bundle path) and write to the working directory, and (b) regenerate every pool reported in the results table. A hard-coded study-id subset that drifts from the manuscript (e.g., a pool computed over k=7 while the manuscript reports k=9) is a P0 — fix and re-run; never ship stale code or stale figures derived from it.
   • Run a supplementary-only review pass — the manuscript self-review/panel does not see the supplement; mirror

     /self-review

     Phase 2.5c–2.5d (reference + cross-reference QC) over the supplementary files.

In-Reply-To

${CLAUDE_SKILL_DIR}/references/phase9_circulation.md

Failure-mode cross-ref →

references/review_orchestration.md

RO-1~RO-5 (dual-rating completeness, defensive-tone bias audit, response-matrix numeric tracking, 2nd-reviewer availability blocking).

qc/audit_vN_to_vNplus1.md

[VERIFY-CSV]

/make-figures

${CLAUDE_SKILL_DIR}/references/phase10_recovery.md

Failure-mode cross-ref →

references/post_submission_release_ops.md

Gate 4 covers reject/revise Zenodo versioning, tag-cleanup gate, and re-target workflow (avoid "new version" misuse on re-target).

${CLAUDE_SKILL_DIR}/references/empirical_lessons.md

• software citations, wide-table PDF rendering, and submission-portal journal-identity checks.

• Bivariate/HSROC model may not converge -- consider univariate random-effects as fallback
• Publication bias tests are underpowered -- state this limitation
• Subgroup/meta-regression analysis not recommended
• Wide prediction regions expected -- emphasize uncertainty in conclusions
• Consider narrative synthesis as alternative/complement

• If study type is ambiguous (DTA vs intervention), ask user to clarify before proceeding.
• If fewer than 4 studies for DTA, warn that bivariate model may not converge.
• If data extraction is incomplete (missing 2x2 cells), suggest contacting authors or sensitivity analysis with imputed values.
• If PROSPERO ID is missing, flag as a limitation but continue.
• Always remind user: this is a methodological support tool; final decisions rest with the research team and ideally include a biostatistician/methodologist.

• Never fabricate variable names, dataset column names, or variable codings. If a variable mapping is uncertain, output

  [VERIFY: variable_name]

  and ask the user to confirm against the data dictionary.
• Never fabricate statistical results — no invented p-values, effect sizes, confidence intervals, or sample sizes. All numbers must come from executed code output.
• Never generate references from memory. Use

  /search-lit

  for all citations.
• If a function, package, or API does not exist or you are unsure, say so explicitly rather than guessing.