paper-review-lite

name	paper-review-lite
description	Pre-submission audit. Argument, numerics, refs, writing, figures, replication.
argument-hint	[path to paper or describe manuscript to review]
context	fork

Paper Pre-Submission Review (Lite)

Heritage and scope

An 11-sub-agent pre-submission review that runs inside a Claude Code session — no extra install, billable against your Claude Code plan. Nine parallel dimension agents find issues, two cross-checkers verify each finding against the manuscript before it reaches the report, and the orchestrator synthesizes. This is the Claude-Code-native counterpart to presubmit, our port of the reviewer2 adversarial peer-review pipeline, and it inherits that lineage's two commitments: a critical-reviewer posture, and a verification cascade that drops any finding not pinned to a quoted passage. It is the fast in-flow check, not the full pipeline — see the last section for when to reach for presubmit instead.

Instructions

Nine agents each audit a different dimension; two cross-check agents then audit those Phase 2 findings for hallucinations, false positives, and missed issues. The deliverable is a severity-ranked Pre-Submit Report with a journal-readiness checklist.

Critical-Reviewer posture (required for every sub-agent). Brutally honest on the work, fair to the people. Attack the argument or the data, never the authors — "the claim on line X is not supported by the evidence on line Y" is in scope; framing like "fraudulent" or "incompetent" is not. Prestige carries no weight: reputation, journal status, citation counts, and prior peer review are not evidence.

Orchestration lead. Whatever model you are running — Claude Opus 5 or Fable 5 — orchestrates: orientation, launching the dimension sub-agents, the cross-check cascade, the synthesis. The sub-agents do the reading. If you are on Opus, run at medium reasoning effort by default (raise to high for the Phase 4 adjudication, deduplication, and Editor's Note — a bounded judgment call, not the sustained orchestration role), and fan Phase 2 (nine parallel dimensions) and Phase 3 (cross-check) out. If the Workflow tool is listed among your tools this session, express those two phases as a Workflow; otherwise — the common case, since dynamic Workflows are gated per session by org policy, the launch gate, or the "Dynamic workflows" setting in /config, and invoking a skill does not grant them — launch each phase's agents with parallel Agent calls in a single message and start the next phase once their outputs land. The fallback is the default, not a degraded mode; branch on tool availability, not on which model is leading. On the Workflow path, use a pipeline() so each dimension's findings go to cross-check as soon as that dimension completes; on the Agent path, launch all nine Phase 2 agents in one message and start Phase 3 once the nine files exist.

Sub-agent model and effort assignment. Route by task type, not by agent number — argument-level dimensions (evaluating logical structure, cross-referencing claims, detecting silent discrepancies) go to Opus at high, pinned; mechanical dimensions (matching, existence checks, checklist audits) go to Sonnet at medium, pinned. The one exception is the argument-level cross-checker (Agent 10). It verifies Opus's own findings, so it runs on Fable rather than Opus — a same-model checker grading its own kind of work is the weakest link in this design, the same failure mode presubmit's Red Team/Blue Team split and journal-review's Chief Reviewer synthesis are built to avoid. This is a bounded single-shot call per sub-agent, not a sustained role, so set the effort explicitly rather than letting it inherit the orchestrator's own (lower, sustained-role) effort — noting that the standalone Agent tool has no effort field, so on the Agent path the effort instruction goes into the prompt text itself; only agent() inside a Workflow accepts opts.effort — see opus-orchestrate / fable-orchestrate for the mechanical-vs-argument-level routing pattern and the same effort reasoning. The Phase 3 cross-checker groupings track this split exactly:

Sub-agent	Model	Effort	Why
Agent 1 — Content & Argument	Opus	high, pinned	evaluates logical structure and argument quality
Agent 2 — Numbers & Internal Consistency	Opus	high, pinned	cross-references quantitative claims across the paper and audits forking-paths risk
Agent 3 — References & Citations	Sonnet	medium, pinned	matches bibliography entries against in-text citations — mechanical
Agent 4 — DOI Audit	Sonnet	medium, pinned	lookup and verification against bibliography entries — mechanical
Agent 5 — Writing Quality & Journal Compliance	Sonnet	medium, pinned	style/craft review plus a completeness checklist — no argument evaluation
Agent 6 — CONSORT / Randomization & Flow	Opus	high, pinned	detects silent discrepancies across N's, arms, and stages
Agent 7 — Pre-Registration Verification	Opus	high, pinned	PAP-to-paper hypothesis mapping and forking-paths audit
Agent 8 — Figures, Tables & Formatting	Sonnet	medium, pinned	existence, numbering, and formatting checks — mechanical
Agent 9 — Replication Archive	Sonnet	medium, pinned	documentation-completeness audit against a checklist — mechanical
Agent 10 — Content/Numbers/Design Cross-Checker	Fable	high, pinned	re-verifies Agents 1, 2, 6, 7's argument-level findings and steel-mans the paper — deliberately off Opus so the checker isn't the same model as what it's checking
Agent 11 — Technical Cross-Checker	Sonnet	medium, pinned	re-verifies Agents 3, 4, 5, 8, 9's mechanical findings — same tier as its source agents

1. Orientation (do this yourself before launching agents)

Read the paper yourself to understand its structure before writing agent prompts. Determine:

• Where the paper source lives (LaTeX .tex vs Pandoc .md vs Word) and what the build command is
• Whether a Supplementary Information file exists and where it lives
• Where figures are stored and how they are referenced (relative paths, figure directories)
• Whether a replication archive exists (look for replication/, archive/, data/, README files)
• The paper's rough structure: section names, approximate page count, key claims in the abstract
• The bibliography format and location (.bib, inline, etc.)
• Design family. Is this a conjoint/factorial-vignette paper, a list experiment, a topic-modeling or LLM-classification study, or a VLM-OCR corpus paper? If so, also invoke the relevant sibling skill (conjoint-diagnostics, list-experiment, topic-modeling, text-classification, vlm-ocr-pipeline) and fold its domain-specific checklist into Agent 9's deliverable. For any experimental manuscript, also run methods-reporting in audit mode so its 45-item checklist becomes the baseline for Agents 1, 2, 6, 7, and 8.

Use this knowledge to write specific agent prompts that reference actual file paths, section names, and relevant files. Generic prompts produce shallow results.

Orchestration contract. Before Phase 2, create a scratch directory .review-tmp/ in the paper's working directory. Each Phase 2 agent writes its structured findings to a dedicated file — agent-1-content.md, agent-2-numbers.md, agent-3-references.md, agent-4-dois.md, agent-5-writing.md, agent-6-consort.md, agent-7-prereg.md, agent-8-figures.md, agent-9-archive.md — using the output format specified below. Use these exact filenames; Phase 3 cross-checkers read them directly rather than having findings pasted into their prompts. Run the nine Phase 2 agents concurrently: under a Workflow pipeline each cross-check fires as its inputs land; with parallel Agent calls, launch all nine in a single message and start Phase 3 once the nine files exist. For experimental manuscripts, Agents 6 (CONSORT/randomization-and-flow) and 7 (pre-registration verification) are mandatory; for non-experimental manuscripts they can be skipped and their checklist rows marked NA. After the Pre-Submit Report has been delivered, delete .review-tmp/ — it is workflow scratch, not a deliverable — unless the user asks to keep it.

2. Parallel Deep Review (nine agents, concurrent)

Agent 1 — Content & Argument (Red Team primary): Read the full paper. Your posture is adversarial but fair: find every place the argument is weaker than the paper presents it to be. Check logical flow from introduction through conclusion. Identify unsupported claims, logical gaps, missing caveats, and places where the argument is unclear or circular. Flag any claims in the abstract not backed up in the body. Note missing discussion of limitations. Check whether the framing accurately positions the contribution relative to cited prior work.

Agent 2 — Numbers & Internal Consistency: Check every quantitative claim against JARS-Quant reporting expectations (Appelbaum et al. 2018). Do numbers in the abstract match the body? Do table values match in-text references? Do SI cross-references point to the right appendices/tables? Are confidence intervals, p-values, N counts, and effect sizes reported consistently throughout? Flag multiple-comparisons issues (many tests without correction or discussion). Verify that significance thresholds are defined and used consistently. For experimental papers, verify denominator consistency across ITT and any complier/compliance-adjusted analyses and flag any manipulation-check that is present in the design but missing from the results. Flag forking-paths risks explicitly: DV switching between primary and secondary outcomes, covariate-set changes across models, transformation or subsetting decisions not traceable to a pre-registration, and any analysis whose choice was visibly made after seeing outcome data (Wicherts et al. 2016; Gelman & Loken 2014; Simmons et al. 2011). Do NOT audit the CONSORT flow, baseline balance, attrition-by-arm, or PAP-to-paper mapping — those are Agents 6 and 7.

Agent 3 — References & Citations: Audit the bibliography file. Are all cited works present? Are there uncited entries? Check for stale working papers (2025+) that may now be published — flag entries that need author verification. Check formatting consistency (journal names, author encoding, entry types). Do NOT check DOIs — that is Agent 4's job.

Agent 4 — DOI Audit: Check every bibliography entry for a DOI. For entries missing a DOI, attempt to locate one via web search (title + author + "doi"). Report which entries are missing DOIs and, where found, provide the correct DOI. Verify that existing DOIs resolve to the correct paper — wrong-paper DOIs are a common copy-paste error.

Agent 5 — Writing Quality & Journal Compliance: Check for redundancy, passive voice overuse, unclear antecedents, jargon without definition on first use, overly long sentences (60+ words), and inconsistent terminology for the same concept. Audit journal-level transparency compliance against the TOP Guidelines (Nosek et al. 2015): data citation, data/code/materials transparency, design and analysis transparency, preregistration of studies and analysis plans, replication standards. Check reporting conformance against JARS-Quant (Appelbaum et al. 2018) and the reproducibility manifesto (Munafò et al. 2017). Explicitly check these pre-submission completeness items:

• Anonymization / double-blind compliance: no self-identifying information, self-citations in third person, no "unpublished manuscript by [author]" references, no author names in file metadata or acknowledgments
• Data availability statement (present and accurate per TOP Level 2+ and DA-RT; Lupia & Elman 2014)
• Ethics/IRB statement (present and sufficient for human subjects research; judge against APSA 2020 Principles and Guidance for Human Subjects Research — protocol number, exempt vs. expedited vs. full review, consent language, and any minor/vulnerable-population flags)
• AI use disclosure (required by some journals — flag if absent and suggest adding)
• Conflict of interest declaration
• Funding/acknowledgment statement
• Author ORCID (if journal requires)
• Abstract word count (most journals cap at 150 words)
• Keywords (some journals require 4-5 keywords)
• Word/page count against journal limits (if known)

Flag each completeness item as present, missing, or insufficient. Do NOT audit the preregistration itself — that is Agent 7.

Agent 6 — CONSORT / Randomization & Flow (experimental papers only): Audit randomization and participant flow against the CONSORT 2010 Statement (Schulz, Altman & Moher 2010) and Gerber et al. (2014) Appendix 1. Verify a participant-flow diagram is present and documents, at every stage: number assessed for eligibility; number excluded pre-randomization and reasons; number randomized per arm; number receiving the intended condition per arm; number lost to follow-up or excluded post-randomization (by arm, with reasons); number analyzed per arm for the primary outcome. Verify that: (a) a baseline-balance table across treatment arms is reported with standardized mean differences or equivalent, and imbalances are discussed; (b) intention-to-treat (ITT) is the primary analysis — per-protocol or complier-average analyses, if reported, are clearly labeled as secondary; (c) attrition rates are reported by arm and differential attrition (magnitude and composition) is explicitly addressed, including any sensitivity analysis (e.g., Lee bounds, Manski bounds); (d) the randomization procedure (unit, block structure, stratification, allocation concealment, mechanism) is described; (e) blinding / masking status is stated for participants, experimenters, and analysts, or its absence is justified. Flag any discrepancy between the CONSORT diagram numbers and the N's reported in the main tables. For non-experimental manuscripts, write NA — non-experimental to the output file and exit.

Agent 7 — Pre-Registration Verification (experimental / pre-registered papers only): Audit the manuscript against its pre-analysis plan (PAP) end-to-end. Steps: (1) Registry verification — locate the registration on OSF, AsPredicted, EGAP, or the journal registry; record the registry ID, registration timestamp, and whether the PAP is embargoed or public; flag any registration that post-dates data collection or data access. (2) PAP-to-paper hypothesis mapping — produce a table with columns PAP ID | Registered hypothesis | Registered estimator | Registered outcome | Reported in paper? | Location | Deviation?. Every registered hypothesis must appear; every reported confirmatory analysis must trace back to a registered hypothesis. (3) Confirmatory vs. exploratory classification — every reported analysis must be explicitly labeled confirmatory (registered) or exploratory (not registered); flag analyses presented as confirmatory that are not in the PAP. (4) Silent-deviation audit — flag registered analyses not reported; reported analyses not registered; changes in DV, covariate set, subgroup, estimator, sample filter, or pre-processing step that are not disclosed in a deviations section. Judge the PAP against Waldron & Allen (2022): is it specific, precise, and exhaustive? (5) Forking-paths check — Gelman & Loken (2014) and Simmons et al. (2011): list every branch point (DV choice, covariate set, exclusion rule, transformation, subgroup definition) and confirm each is resolved by the PAP or acknowledged as exploratory. Nosek et al. (2018) "Preregistration Revolution" is the benchmark for what counts as a credible pre-registration. For non-preregistered manuscripts, write NA — no preregistration to the output file, note whether the manuscript justifies the absence, and exit.

Agent 8 — Figures, Tables & Formatting: Verify all figures referenced in text exist on disk. Check captions for self-containedness (a reader should understand the figure from the caption alone). Verify figure and table numbering for gaps or duplicates. Check for LaTeX/build warnings (undefined references, overfull/underfull boxes). Verify SI is internally consistent and all SI cross-references in the main text point to the correct appendix/table/figure numbers. Check for formatting inconsistencies (e.g., mixed \hline and booktabs, inconsistent float placement). Flag accessibility issues in figures (alt text, color-blind-safe palettes, contrast). Do NOT audit the CONSORT participant-flow diagram itself — that is Agent 6; but if Agent 6 reports a CONSORT diagram is missing, still verify that a placeholder or substitute figure is not mis-numbered.

Agent 9 — Replication Archive: Review the replication archive independently against TOP Level 2+ (Nosek et al. 2015) and DA-RT (Lupia & Elman 2014). Does the README document the full pipeline? Are all data files present (or documented as embargoed/restricted)? Do script paths in the README match what actually exists? Are software dependencies and versions documented? Is there a codebook for each dataset (variable definitions, coding)? Does a table/figure-to-script mapping exist (which script produces which output)? Are PRNG seeds documented for any simulation or bootstrap procedures? Is data provenance documented (source, license, redistribution rights)? Could a competent researcher reproduce the main results from the archive alone? Flag missing files, undocumented steps, or broken path references.

Output format for each agent: Write findings to the agent's assigned file under .review-tmp/ (see Orchestration contract above). Use structured lists, not prose. For each issue:

• Severity: [CRITICAL], [RECOMMENDED], or [MINOR]
• Location: file path and line number or section name
• Issue: one sentence describing the problem
• Fix: one sentence describing what to do
• Quote: a verbatim span (≤ 2 sentences) from the manuscript that supports the finding — required for every [CRITICAL] and [RECOMMENDED] item, optional for [MINOR]. Findings without a quote will be dropped by the Phase 3 cross-checker as likely hallucinations.

Severity rubric (apply consistently across all agents):

• [CRITICAL] — blocks submission: wrong numbers, broken references, missing ethics/data-availability statement when required, silent deviation from a pre-registration, anonymization failure, unreproducible main result.
• [RECOMMENDED] — will draw reviewer complaint: unsupported claim, missing robustness check, undefined threshold, missing limitation, insufficient figure caption, undocumented exclusion.
• [MINOR] — polish: style, typography, citation format, wording consistency.

3. Cross-Check

Tell each cross-checker which .review-tmp/ files to read, and where to write its own validated output — .review-tmp/agent-10-content-numbers.md and .review-tmp/agent-11-technical.md.

Agent 10 — Content, Numbers & Design Cross-Checker (Blue Team / verification, Fable): Read .review-tmp/agent-1-content.md, .review-tmp/agent-2-numbers.md, .review-tmp/agent-6-consort.md, and .review-tmp/agent-7-prereg.md. For each [CRITICAL] or [RECOMMENDED] item: (a) verify the cited quote appears verbatim at the cited location in the manuscript; drop items whose quote is missing, paraphrased, or does not support the claim made — these are hallucinations; (b) verify the issue itself against the actual paper text and flag false positives (issue doesn't exist or is already addressed); (c) steel-man the paper — for each retained finding, briefly note whether the paper anticipates or partially addresses the concern, since a partial response may downgrade severity. Add any issues missed — pay particular attention to: abstract vs. conclusion claims that drift from body evidence, significance thresholds, multiple comparisons, denominator consistency (e.g., percentages computed with vs. without a residual category), ITT vs. per-protocol mismatches, CONSORT-number vs. table-N mismatches, and silent pre-registration deviations (registered analyses not reported, reported analyses not registered).

Agent 11 — Technical Cross-Checker (Blue Team / verification): Read .review-tmp/agent-3-references.md, agent-4-dois.md, agent-5-writing.md, agent-8-figures.md, and agent-9-archive.md. For each flagged item: (a) verify the cited location actually contains the described issue (drop hallucinations); (b) verify the finding itself against the actual files — check the bibliography, figure files, figure/table numbering, and archive directory; (c) confirm or refute each finding. Add any issues missed.

4. Synthesis

Before listing anything, consolidate. If two or more Phase 2 agents independently flagged the same underlying issue, present it once with the strongest supporting quote — the same complaint restated five times under five headings is an artifact of the parallel-agent structure and it erodes trust in the report. If a finder's own description includes language conceding the point ("does not in itself indicate a deviation from common practice," "while a real concern, this is standard in the literature," "the paper acknowledges this on p. X"), demote the issue one severity tier — or drop it if the concession negates the critique.

Compile validated, deduplicated findings into a single Pre-Submit Report:

## Pre-Submit Report: [Paper Title]
Date: [today]
Recommendation: [Submit as-is | Minor revisions before submit | Major revisions before submit | Hold for new analysis | Substantial restructuring needed]
Issues: [N critical, N recommended, N minor]

### Editor's Note (revision strategy — read first)

A 3–6 paragraph prose memo, in your own voice, summarizing the path to a revised version that defends the contribution. Not a punch list — the punch list is below. This section should:

- Open with the single most consequential addition that would *strengthen* rather than weaken the paper. (Often a small analytical addition, a missing robustness check, or a missing comparison whose result would convert the strongest critique into a supporting result.)
- Identify which Critical Issues require new analysis (cannot be addressed by rewriting alone) versus which are textual/framing fixes.
- Where the manuscript already concedes a point that a critique missed, name the concession and note that the fix may be a small expansion of the existing acknowledgement rather than a new section.
- Where two Critical Issues are coupled (fixing one resolves the other), say so.
- End with a short paragraph on what could be deferred to a future paper without weakening the current contribution.

### Critical Issues (must fix before submission)
### Recommended Changes (should fix, not blocking)
### Minor Issues (nice to have)
### Strengths (what is working well)
### Journal-Readiness Checklist

| Dimension                        | Status            | Notes |
|----------------------------------|-------------------|-------|
| Compiles cleanly                 | PASS/FAIL         |       |
| Anonymized for double-blind      | PASS/FAIL         |       |
| Argument & logic                 | PASS/FAIL/PARTIAL |       |
| Internal numerical consistency   | PASS/FAIL/PARTIAL |       |
| References complete              | PASS/FAIL/PARTIAL |       |
| DOIs present                     | PASS/FAIL/PARTIAL |       |
| Writing quality                  | PASS/FAIL/PARTIAL |       |
| Figures/tables correct           | PASS/FAIL/PARTIAL |       |
| Formatting consistent            | PASS/FAIL/PARTIAL |       |
| Abstract & keywords              | PASS/FAIL/PARTIAL |       |
| Word/page count compliant        | PASS/FAIL/NA      |       |
| Replication archive ready        | PASS/FAIL/PARTIAL |       |
| CONSORT participant flow         | PASS/FAIL/NA      |       |
| ITT & baseline balance reported  | PASS/FAIL/NA      |       |
| Attrition-by-arm reported        | PASS/FAIL/NA      |       |
| Pre-registration deviations disclosed | PASS/FAIL/NA |       |
| Data availability statement      | PASS/MISSING      |       |
| Ethics/IRB statement             | PASS/MISSING      |       |
| Preregistration disclosure       | PASS/MISSING/NA   |       |
| AI use disclosure                | PASS/MISSING      |       |
| COI declaration                  | PASS/MISSING      |       |
| Funding/acknowledgments          | PASS/MISSING      |       |

### What Still Needs Your Input

"What Still Needs Your Input" lists items the review cannot resolve because they require author knowledge: ethics approval numbers, funding grant details, journal-specific formatting requirements, embargo status of data, or factual claims only the author can verify.

Every issue in the report carries a file path and line number (or section name), and objective errors (wrong numbers, broken references) are distinguished from subjective suggestions (writing style, framing). reference/example-report.md is a filled example of the whole report.

When to reach for presubmit instead

This skill covers most in-flow review needs. The heavier presubmit Python CLI runs ~30 stages and is the right tool when:

• You want full adversarial pressure — a dedicated Red Team whose personas (Breaker, Butcher, Shredder, Collector, Void) each attack the paper from a different angle, a Blue Team defence, separate numbers/fact-check/citation-verification cascades, and a legal pass; this skill compresses that into Agent 1 plus the two cross-checkers.
• You want a standalone deliverable — a single report.txt and optional editor's note, produced outside your Claude Code session. Useful when the review is the product: feedback to a co-author, a pre-print critique, or a final pass before external peer review.
• You want resumability + cost tracking — presubmit checkpoints every stage to disk; deleting a stage file forces that stage to re-run. Tracks per-stage API cost.
• You want a math audit — presubmit has an optional Mathpix-backed equation audit (--math); this skill doesn't.
• You want a code-replication audit — presubmit can ingest a replication archive and compare claims against the code (--code); this skill stops at the "does the archive look complete" question in Agent 9.

Trade-off: presubmit bills per-token against an Anthropic API key (separate from your Claude Code subscription). See presubmit/README.md for install, cost considerations, and known trade-offs vs. the upstream Gemini implementation.