Pre Submission ReviewerSAFE

将博导十年科研经验炼化为可直接调用的 AI 技能。从 Idea 构思到论文投稿,你的 AI 科研副导师。

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: pre-submission-reviewer
description: >-
  Runs a pre-submission review of a technical paper across five
  dimensions: macro logic, writing details, English grammar, LaTeX
  formatting, and figure quality. Uses a reviewer-style severity
  taxonomy (CRITICAL / MAJOR / MINOR) and flags banned AI-tone
  vocabulary and em-dash misuse. Use when the user asks to 'review
  this paper', 'audit before submission', 'check the draft', 'find
  issues', 'proofread', or within one week of a submission
  deadline.
license: CC-BY-4.0
---

# Pre-Submission Reviewer

## Overview

Three to five days before a submission deadline is the window where
a careful external review pays off most. This skill takes a full
paper or key sections and produces a structured review across five
dimensions, each with severity-tagged findings and concrete
rewrite suggestions. It enforces the mechanical rules from the
writing-checklist section (no em-dashes, no banned AI-tone
vocabulary, leading text per paragraph, topic-sentence discipline,
citation-format uniformity) and surfaces the patterns that
non-native English-speaking authors most commonly violate
(articles, subject-verb agreement, tense consistency, which versus
that, Chinglish phrasing).

The output is not a rewrite. It is a prioritised list of findings
with severity tags; the author decides which to fix. CRITICAL items
should block submission until addressed.

## When to use this skill

- Three to five days before a submission deadline.
- The user asks to 'review this paper', 'audit before submission',
  'check the draft', 'find issues', 'proofread'.
- After a camera-ready revision, before sending the final version.
- After any major rewrite (rebuttal responses, Section 3 overhaul).
- When the user suspects AI-tone contamination in a section.

## When NOT to use this skill

- The paper is still being structured. Use `tech-paper-template`,
  `intro-drafter`, or `benchmark-paper-template` (separate plugin) first.
- The user wants structural advice rather than review. Use the
  drafting skills instead.

## Core procedure

### Step 0: paradigm and venue fit

Before the dimensional review, settle two things.

**Paradigm**, judged by research method, because what counts as a
severe problem differs by paradigm:

- **STEM / technical** (CS, engineering, materials, chemistry): weight
  the Introduction chain, contribution-to-section mapping, baseline
  coverage, ablation-based attribution, figure quality. Benchmark
  papers additionally get coverage, reproducibility, and
  contamination checks; chemistry and materials get characterization
  completeness, purity, controls, replicates.
- **Humanities** (literature, history, philosophy, discourse
  analysis): weight whether the thesis is explicit and defensible,
  core concepts pinned down and stable across sections, the
  literature genuinely engaged, and the material (texts, archives,
  cases) able to carry the claims. Do **not** demand baselines or
  ablations here; check instead whether sub-arguments build on each
  other rather than sit in parallel, and whether conclusions outrun
  the material.
- **Empirical social science**: weight operationalized research
  questions, sampling and data-source justification, statistics
  matched to data types, conclusions bounded by the sample. For
  theory papers, "experiments" reads as "proofs": check the proofs,
  and never call the evidence thin merely because there is no results
  table.
- **Finance / economics**: weight identification credibility,
  endogeneity handling, robustness checks (their absence is a
  first-round flag), and economic versus merely statistical
  significance.
- **Law**: weight the accuracy of statutes, case numbers, and
  holdings (an invented or wrong citation is rejection-level), the
  clarity of the interpretive approach, and whether comparative
  arguments state their scope.

When the paradigm is unclear, ask the author before reviewing with
the wrong ruler.

**Venue fit**: if the stated target venue's scope visibly mismatches
the paper's topic or contribution type, that is a real rejection
risk, not a taste note. Flag it as a finding and suggest two or three
better-fitting venues.

### Step 1: Dimension 1 Macro logic review

See: references/logic-and-structure.md for the Logic First rule,
Self-contained rule, Leading Text rule, and Running Example rule.

Check:

- Introduction flowchart is intact (Background, Limitations, Goal
  or Key Idea, Challenges, Methodology, Contributions).
- Contributions map one-to-one with methodology modules and with
  section numbers.
- Experiments validate the paper's main claims, not tangential
  ones.
- Related Work covers the necessary prior art.
- Running example is consistent across Introduction, Methodology,
  Experiments.
- The headline result's attribution is isolated: an ablation
  separates the core mechanism from peripheral factors (a routing
  step, post-processing, a stronger base model, favorable samples).
  No such ablation: flag "attribution unverified" as MAJOR.
- Claims match their evidence: "solves" is stronger than most papers
  earn (usually "improves"); "state-of-the-art" needs the benchmark
  and conditions; "we are the first" gets checked or flagged.

Every break in the chain is CRITICAL.

**Retrieval-grounded checks** (when the environment has a
literature-search capability: a scholarly tool, web search over
scholarly indexes, or shell access to public APIs):

1. **Novelty verification**: extract two or three keyword groups from
   the paper's core method and problem setting, retrieve, and identify
   the three to five closest published works. If Related Work already
   covers them, the paper's positioning stands; a highly relevant
   uncovered work is a MAJOR finding ("missed X, Author et al.,
   Year"). Compare on difference axes; a similar title alone proves
   nothing.
2. **Citation completeness**: retrieve the field's recent
   representative works and its canonical ones, and compare

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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