Manuscript Review

Manuscript Review Skill

Pipeline position: Phase 1a (content audit). Runs in parallel with figure-rhetoric. No prior dependencies. Outputs consumed by manuscript-provenance (macro manifest feedback) and figure-rhetoric (claims map for visual argument assessment). See companion skills for full pre-publication coverage: manuscript-typography (typographic conventions), citation-audit (citation truth), arxiv-preflight (submission compliance).

Purpose

Execute a comprehensive, multi-pass diagnostic audit of an academic or technical manuscript, producing a structured improvement report that identifies issues across 24 audit dimensions — from macro-coherence and argumentative architecture through claims-evidence calibration, narrative flow, prose microstructure, rendered visual inspection, and cross-element coherence, down to citation hygiene and reproducibility.

The output is a prioritized, actionable improvement plan — not a line edit. The goal is to surface structural, logical, and clarity issues that authors systematically miss because they're too close to the text.

Optimized for arXiv/preprint submissions with flexible compliance standards.

Companion skill: manuscript-provenance audits whether manuscript content (numbers, tables, figures, ordering, terminology) is computationally derived from code and scripts. This skill audits the document as prose; that skill audits computational grounding. Run both for complete pre-publication coverage.

Boundary Agreement with manuscript-provenance

Rule: This skill never opens the codebase. manuscript-provenance never judges prose quality. Each reads the other's report when available.

Integration point — Macro Manifest: manuscript-provenance produces a macro manifest as part of its §4 audit: a structured list of every macro-injected value, its resolved numeric value, its source (script + output file), and its location(s) in the manuscript text. This skill's Pass 13 (Cross-Element Coherence) consumes that manifest to check whether the prose surrounding each injected value is appropriate for the actual value. If no provenance report exists, this skill extracts macro values directly from .tex source (less precise — no source tracing, but coherence check still runs).

Workflow

1. Ingest

Read the uploaded manuscript. Accept PDF, DOCX, LaTeX source, or Markdown. If multiple files are uploaded (e.g., main text + supplementary), process all of them.

Identify:

• Target venue (defaults to arXiv/preprint; adjust if conference/journal submission)
• Submission type (full paper, technical report, thesis chapter, etc.)
• Any specific concerns the user raised — these get priority in the report

For arXiv submissions, compliance checks are advisory. Focus on technical quality, reproducibility, and clarity rather than strict formatting rules.

2. Load the Checklist

Read references/checklist.md — the comprehensive 24-section, ~175-checkpoint refactoring checklist. Every audit pass is structured against this checklist.

Read references/checklist.md

3. Multi-Pass Audit

Execute the following passes sequentially. Each pass maps to one or more checklist sections. Work systematically — for each checkpoint:

• PASS: Note briefly, move on
• FAIL: Document with exact location (section, paragraph, line), specific defect, concrete fix required
• N/A: Mark if not applicable to this manuscript type

Pass 1 — Structural Integrity (Checklist §1, §4, §5, §10)

• Trace the thesis-thread from abstract through conclusion
• Verify section-level necessity and logical dependency ordering
• Check introduction funnel structure and contribution enumeration
• Verify conclusion contains no new information and maps 1:1 to stated contributions
• Assess related work organization (taxonomic vs. annotated) and differentiation

Pass 2 — Abstract & Title Calibration (Checklist §2, §3)

• Abstract functional completeness (context → gap → approach → results → implication)
• Quantitative specificity in abstract
• Title precision-scope alignment
• Keyword-abstract coherence

Pass 3 — Technical Rigor (Checklist §6, §7)

• Reproducibility sufficiency of methodology (document-level: does the paper describe enough? Code-level verification deferred to manuscript-provenance)
• Assumption explicitness and notation consistency
• Baseline adequacy, dataset characterization, statistical rigor
• Effect size reporting, evaluation metric justification
• Computational cost reporting (checks paper reports it; value tracing to benchmarking scripts deferred to manuscript-provenance)

Pass 4 — Argumentation Quality (Checklist §8, §9)

• Discussion introduces no new results
• Alternative explanations considered
• Generalizability boundaries stated
• Limitations genuine (not performative), preemptively addressing reviewer objections
• Threat-to-validity taxonomy coverage

Pass 5 — Citation & Reference Hygiene (Checklist §11)

• Citation-reference bijection (no orphans in either direction)
• Style conformance to target venue
• Primary source preference over secondary citations
• Preprint-to-publication status check
• Citation placement (claim-level, not paragraph-level)
• Retraction check advisory

Pass 6 — Visual & Tabular Quality (Checklist §12)

• Sequential callout ordering
• Resolution and legibility assessment
• Colorblind accessibility
• Axis labels with units, consistent visual language
• Table alignment and significant figure consistency

Pass 7 — Prose Mechanics (Checklist §13, §14, §15)

• Tense consistency (recommendations, not strict requirements)
• Hedging calibration (neither overclaiming nor vacuous)
• Passive voice patterns (advisory)
• Nominalization reduction opportunities
• Clarity and precision (marketing language advisory for arXiv)
• Abbreviation hygiene (first-use expansion, consistency)
• Mathematical typesetting consistency

Pass 7b — AI-Pattern Detection (advisory)

Scan prose sections for residual AI-writing patterns using detection rules from references/detection-patterns.md. Academic manuscripts drafted or polished with AI assistants often retain detectable tells.

Focus on patterns relevant to academic writing:

• Significance inflation — "pivotal", "groundbreaking", "paradigm shift"
• AI-frequency vocabulary — "delve", "landscape", "tapestry", "underscore"
• Copula avoidance — "serves as" instead of "is"
• Vague attributions — "experts argue", "studies have shown" without citations
• Superficial -ing riders — "..., highlighting the importance of..."
• Excessive hedging — beyond what epistemically appropriate hedging requires

Skip patterns that are acceptable in academic prose:

• Passive voice — standard in methods sections
• Formal transitions — "Furthermore", "Moreover" are conventional in academic writing
• Title case headings — journal style may require it

This pass is MEDIUM priority. Flag findings but do not over-correct — academic conventions overlap with some AI patterns. Severity: report individual instances as LOW, flag clusters of 3+ patterns in a single paragraph as MEDIUM.

Pass 8 — Best Practices & Reproducibility (Checklist §16, §17, §18, §19)

• Supplementary material cross-reference integrity
• Code/data availability statements exist in the paper (verification that claimed repos are valid and pipelines work deferred to manuscript-provenance)
• License compatibility for third-party assets
• Hyperlink verification and reference integrity
• Overall clarity and accessibility assessment

Pass 9 — Claims-Evidence Calibration (Checklist §20)

This is a dedicated pass through every assertion in the manuscript.

For each claim:

1. Grade claim strength: strong/definitive ("X causes Y"), moderate/qualified ("X improves Y under conditions Z"), or hedged/tentative ("X may contribute to Y")
2. Grade evidence strength: direct experimental, indirect/correlational, citation-only, analogical, or no evidence
3. Flag mismatches:
   • Overclaim: Strong claim + weak evidence → soften the claim or add evidence
   • Underclaim: Hedged language + strong evidence → sharpen the language
   • Orphaned claim: Any strength + no evidence → add evidence or remove claim
4. Audit causal vs. correlational language against study design
5. Check generalization scope against actual experimental conditions
6. Verify comparative claims ("outperforms", "better than") against head-to-head evaluations actually present in the paper
7. Flag implicit claims (e.g., "Unlike prior work, our approach handles X" implies prior work cannot — verify this)
8. Check negation claims for evidence of absence vs. absence of evidence

This pass is HIGH priority. Claims-evidence mismatch is the single most common reason reviewers reject papers. An overclaim in the abstract poisons the entire reading.

Pass 10 — Narrative Flow & Coherence (Checklist §21)

Read the manuscript linearly, tracking the reader's cognitive state. At each sentence and paragraph boundary, check:

• Does this sentence follow from the previous one, or does the reader need to make an inferential leap?
• Does this paragraph's opening sentence state its point, or is the point buried?
• Does each sentence start with known information and end with new information (given-new contract)?
• Are cross-references between sentences ordered so the reader moves forward through the text, not zigzagging back?
• Does the last sentence of each paragraph connect to the first sentence of the next paragraph?
• Are there logic gaps where a premise is skipped because the author knows it implicitly?
• Does every setup/promise within a section get its payoff within that section?
• Does each section have a discernible arc (setup → content → landing)?

Flag any location where a domain-expert reader would need to re-read, scroll back, or pause to reconstruct the logical connection. These are flow breaks.

This pass is HIGH priority. Papers with strong results but poor narrative flow exhaust reviewers. A reader who has to fight the text stops trusting the author.

Pass 11 — Prose Microstructure (Checklist §22)

Sentence-level and paragraph-level patterns that compound into readability problems:

• Ambiguous referents: "this", "it", "they" without clear antecedents
• Information density spikes: paragraphs introducing too many new concepts at once
• Sentences requiring multiple re-reads: excessive clause nesting, misplaced modifiers, garden-path constructions
• Broken parallel structure in lists, comparisons, sequences
• Semantic redundancy: same point restated in nearby paragraphs without purpose
• Long-distance references: concepts introduced and referenced many paragraphs later without re-anchoring
• Dangling modifiers: "Using gradient descent, the loss function converged"

This pass is MEDIUM priority on individual items but compounds — a manuscript with 20 ambiguous pronouns, 10 density spikes, and 5 dangling modifiers is materially harder to read even though no single instance is fatal.