Agent skill

Pre Submission Reviewer

by HKUSTDial in HKUSTDial/Supervisor-Skills

Runs a pre-submission review of a technical paper across five dimensions: macro logic, writing details, English grammar, LaTeX formatting, and figure quality.

CC-BY-4.0Auto-check passedDocuments & Office

Install Pre Submission Reviewer

skills CLI
$ npx skills add HKUSTDial/Supervisor-Skills --skill pre-submission-reviewer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUSTDial/Supervisor-Skills pre-submission-reviewer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUSTDial/Supervisor-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pre-submission-reviewer .claude/skills/pre-submission-reviewer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pre-submission-reviewer
GitHub stars
8.8k
Token cost
~3.2k tokens
SKILL.md length
1,610 words
Files
6 (incl. references)
Skills in repo
12
Repo updated
First seen
Licence
CC-BY-4.0

At a glance

Runs a pre-submission review of a technical paper across five dimensions: macro logic, writing details, English grammar, LaTeX formatting, and figure quality.

  • Works in 10 steps: paradigm and venue fit → Dimension 1 Macro logic review → Dimension 2 Writing details review → …
  • The user asks to review this paper
  • SKILL.md covers Overview, When to use this skill, When NOT to use this skill and Core procedure, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Pre Submission Reviewer is an agent skill from HKUSTDial/Supervisor-Skills. 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.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/forbidden-patterns.md`, `references/grammar-rules.md` and `references/latex-rules.md`).

It sits in Documents & Office, covering LaTeX and Copy editing and proofreading. It works with LaTeX. The repository describes itself as: 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 licence is CC-BY-4.0.

When your agent uses it

  • The user asks to review this paper
  • Audit before submission
  • Check the draft
  • Within one week of a submission deadline

Example prompts

  • “review this paper”
  • “audit before submission”
  • “check the draft”
  • “/pre-submission-reviewer”

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. paradigm and venue fit
  2. Dimension 1 Macro logic review
  3. Dimension 2 Writing details review
  4. Dimension 3 English grammar review
  5. Dimension 4 LaTeX format review
  6. Dimension 5 Figure quality review
  7. Banned-vocabulary and em-dash scan
  8. Section-by-section review
  9. Integrity gate
  10. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 207bc6f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pre Submission Reviewer loads about 3.2k tokens when it runs, and up to ~9.2k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 1,610 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUSTDial/Supervisor-Skills at commit 207bc6f, republished under its CC-BY-4.0 licence (© HKUSTDial). 1,610 words, ~3,172 tokens.

Download SKILL.mdSave it as .claude/skills/pre-submission-reviewer/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
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 against the reference list; a missing canonical baseline, founding paper, or recent survey is MAJOR.

Retrieval results support metadata-level judgments only; never quote numbers or method details from search snippets. Without any retrieval capability, skip these two checks and say so in the summary.

Step 2: Dimension 2 Writing details review

See: references/logic-and-structure.md for paragraph-level rules.

Check:

  • Every paragraph has a topic sentence.
  • Paragraphs transition smoothly; no orphan paragraphs.
  • Paragraphs are not over 10 lines; split if so.
  • No repeated or redundant passages.
  • Abstract covers problem, method, result.
Step 3: Dimension 3 English grammar review

See: references/grammar-rules.md for the canonical list of errors common to non-native English authors, with corrections and examples.

Check the usual suspects:

  • Article use (a, an, the).
  • Subject-verb agreement (third-person singular).
  • Tense consistency (Related Work past, method present).
  • Passive-voice overuse.
  • Which versus that.
  • Sentence length; split long sentences at "Specifically,".
  • Chinglish patterns.
Step 4: Dimension 4 LaTeX format review

See: references/latex-rules.md for the canonical list of LaTeX- specific issues.

Check:

  • Equation numbering contiguous; every numbered equation referenced.
  • Figures and tables have captions; captions are detailed.
  • Citations use the correct command and the non-breaking tilde (for example, ResNet~\cite{X}, never ResNet\cite{X}).
  • Labels use underscores, not spaces or hyphens.
  • Vector figure format; no raster.
  • Page-limit compliance.
Step 5: Dimension 5 Figure quality review

See: references/forbidden-patterns.md for chartjunk patterns and the full figure-quality checklist.

For each figure:

  • Vector format.
  • Font size large enough post-scaling.
  • Colour-blind-safe palette; dual encoding.
  • Self-contained caption with a finding in the first sentence.
  • No chartjunk.
  • Motivated example is concrete and failure-revealing.
  • Solution overview has labels matching section titles.
Show full SKILL.md (625 more words)Show less
Step 6: Banned-vocabulary and em-dash scan

See: references/forbidden-patterns.md for the banned-word list.

Scan the full paper for:

  • Em-dashes used as sentence connectors (banned; project rule).
  • AI-tone words: innovative, pioneering, revolutionary paradigm, transformative framework, superior, surpass, excel, remarkable, unprecedented, breakthrough performance, general-purpose, is capable of, notably, yet, yielding, at its essence, encompass, differentiate, reveal, underscore, pave the way for, highlight the potential of, profound challenges, stems from, rigid, impede.

Flag each occurrence with a severity tag. Em-dashes are MAJOR by default; banned AI-tone words are MAJOR if they appear three or more times.

Step 7: Section-by-section review

See: references/section-guides.md for the per-section writing guides for Abstract, Introduction, Problem Formulation, Framework or Method, Experiments, Related Work, and Conclusion.

For each section, check that the section's content matches the guide's canonical structure (for example, Abstract's five-sentence formula: what, why, challenges, how, results).

Step 8: Integrity gate

Run the checks in the Integrity gate section below.

Step 9: Output

Emit the review in the Output format below.

Severity taxonomy

  • CRITICAL: blocks submission. Example: contributions do not map to sections; introduction flowchart broken; no real-world running example; raster figure in final draft; missing key baseline; page-limit violation.
  • MAJOR: reviewers will flag in first round. Example: topic-sentence absent from 3+ paragraphs; em-dash in 5+ places; banned AI-tone word in 3+ places; Table 1 comparison missing; chart type mismatched with data.
  • MINOR: polish. Example: two long sentences that could be split; default Matplotlib styling; single article error.

Severity honesty cuts both ways. A review that lists a dozen MINOR items while missing the one rejection-level flaw sends the author to submission with false confidence; a review that inflates taste issues into CRITICAL destroys trust. The overall recommendation must match the findings: any unresolved CRITICAL forbids "ready to submit", and a near-ready verdict requires zero CRITICAL and at most two MAJOR.

Integrity gate

Each bullet is tagged [inspection] (LLM verifies from the paper text) or [attestation] (LLM runs the procedure and states it has done so; user remains responsible for confirming completeness).

Before emitting the review:

  1. [inspection] Every finding quotes specific text (sentence, phrase, figure name); no "the Introduction is unclear" without a quoted line.
  2. [inspection] Every CRITICAL finding has a concrete fix suggestion, not "rewrite entirely".
  3. [inspection] No fabricated quotes: only text actually present in the submitted material.
  4. [inspection] Severity assignments follow the taxonomy; nothing is marked CRITICAL for taste reasons.
  5. [inspection] Dimension 3 (grammar) findings cite the specific grammar rule from references/grammar-rules.md.
  6. [attestation] Dimension 6 banned-vocabulary scan is run in full on the entire paper, not sampled. The skill attests the full scan; if the paper is extremely long, the skill states it chunked the input and describes the chunking strategy.
  7. [inspection] Final score matches the CRITICAL + MAJOR count; a score of 9 or 10 requires zero CRITICAL and at most two MAJOR items.

If any [inspection] check fails, mark the output as "needs user attention". For [attestation] bullets, the skill states the scope of its scan and the user confirms completeness.

Run the gate silently. Do not print a per-gate pass or fail report; a failure surfaces as a concrete finding in the affected dimension, and the delivered review stays free of internal checking rituals.

Output format

Summary
  • CRITICAL: <n>
  • MAJOR: <m>
  • MINOR: <k>
  • Top three fixes first: ...
Dimension 1: Macro logic
#FindingSeveritySuggested fix
1<quoted text>CRITICAL or MAJOR or MINOR<fix>
Dimension 2: Writing details
<same table shape>
Dimension 3: English grammar

<same table shape, citing grammar-rule ID>

Dimension 4: LaTeX format
<same table shape>
Dimension 5: Figure quality
<same table shape>
Banned-vocabulary and em-dash scan
<list with line references>
Final score (1-10)
<score>
Submission recommendation
  • <Ready to submit | Needs 1-2 days more work | Needs major revision before submission>

© HKUSTDial, CC-BY-4.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/pre-submission-reviewer of HKUSTDial/Supervisor-Skills.

  • SKILL.md
  • references/forbidden-patterns.md
  • references/grammar-rules.md
  • references/latex-rules.md
  • references/logic-and-structure.md
  • references/section-guides.md

Open the folder on GitHubat commit 207bc6f

Compare with similar skills

Pre Submission Reviewer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Pre Submission Reviewer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pre Submission Reviewer this skillHKUSTDial/Supervisor-Skills8.8k—~3.2kAutomated safety check: PassCC-BY-4.0
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Brief Compliance Checkflonat/flonat-research146—~1.7kAutomated safety check: PassMIT
Latex Diffflonat/flonat-research146—~1.9kAutomated safety check: PassMIT
Paper Polishbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~7.3kAutomated safety check: NotesCustom licence
Nature-Style Academic PolishingYuan1z0825/nature-skills47k—~1.5kAutomated safety check: PassApache-2.0

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Works with

Questions about Pre Submission Reviewer

What does Pre Submission Reviewer do?

Runs a pre-submission review of a technical paper across five dimensions: macro logic, writing details, English grammar, LaTeX formatting, and figure quality. Pre Submission Reviewer is an agent skill from HKUSTDial/Supervisor-Skills. Runs a pre-submission review of a technical paper across five dimensions: macro logic, writing details, English grammar, LaTeX formatting, and figure quality.

When should I use Pre Submission Reviewer?

Pre Submission Reviewer fits situations like: the user asks to review this paper; audit before submission; check the draft; within one week of a submission deadline.

How do I install Pre Submission Reviewer in Claude Code?

Run `npx skills add HKUSTDial/Supervisor-Skills --skill pre-submission-reviewer -a claude-code`. Or copy the skill folder (skills/pre-submission-reviewer in HKUSTDial/Supervisor-Skills) into .claude/skills/pre-submission-reviewer in your project. Claude Code loads it when a task matches its description.

How do I install Pre Submission Reviewer in Codex?

Run `npx skills add HKUSTDial/Supervisor-Skills --skill pre-submission-reviewer -a codex`. Or copy the skill folder (skills/pre-submission-reviewer in HKUSTDial/Supervisor-Skills) into .agents/skills/pre-submission-reviewer in your project. Codex loads it when a task matches its description.

Can I use Pre Submission Reviewer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUSTDial/Supervisor-Skills --skill pre-submission-reviewer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pre-submission-reviewer, .gemini/skills/pre-submission-reviewer, .github/skills/pre-submission-reviewer and .opencode/skills/pre-submission-reviewer in your project.

What does Pre Submission Reviewer need to run?

SKILL.md names no scripts, command-line tools or credentials: Pre Submission Reviewer is instructions for the agent only.

Does Pre Submission Reviewer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Pre Submission Reviewer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Pre Submission Reviewer use?

Pre Submission Reviewer is published under the CC-BY-4.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pre Submission Reviewer use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6k tokens, read only when the agent opens those files.

What are the alternatives to Pre Submission Reviewer?

Skills that share tags, products or a category with Pre Submission Reviewer: Beamer (Noi1r/beamer-skill, 364 stars), Brief Compliance Check (flonat/flonat-research, 146 stars), Latex Diff (flonat/flonat-research, 146 stars) and Paper Polish (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pre Submission Reviewer?

HKUSTDial (a GitHub organization) maintains it in HKUSTDial/Supervisor-Skills, which has 8,783 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 5, 2026.

Source: HKUSTDial/Supervisor-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.