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Phd Pre Submission Reviewer

Community
Immortalqx
phd-pre-submission-reviewer

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.

Overview

PublisherImmortalqx
Repositorymy_codex_skills
Skill namephd-pre-submission-reviewer
Stars
56
Forks
0
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Immortalqx on GitHub. Read the source before you install it.

Installation

Install the Phd Pre Submission Reviewer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Immortalqx/my_codex_skills/tree/main/phd-pre-submission-reviewer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Immortalqx/my_codex_skills.git /tmp/my_codex_skills
mkdir -p .claude/skills
cp -r /tmp/my_codex_skills/phd-pre-submission-reviewer .claude/skills/phd-pre-submission-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Phd Pre Submission Reviewer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Phd Pre Submission Reviewer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Phd Pre Submission Reviewer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 phd-tech-paper-template, phd-intro-drafter, or phd-benchmark-paper-template first.
  • The user wants structural advice rather than review. Use the drafting skills instead.

Core procedure

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.

Every break in the chain is CRITICAL.

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.

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.

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.

Output format

Summary

Dimension 1: Macro logic

Dimension 2: Writing details

Dimension 3: English grammar

Dimension 4: LaTeX format

Dimension 5: Figure quality

Banned-vocabulary and em-dash scan

Integrity gate result

Final score (1-10)

Submission recommendation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Phd Pre Submission Reviewer AI skill do?

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.

Why use Phd Pre Submission Reviewer on TypingMind?

Because you install it once and use it with any model. Phd Pre Submission Reviewer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Phd Pre Submission Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Immortalqx/my_codex_skills/tree/main/phd-pre-submission-reviewer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Phd Pre Submission Reviewer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Phd Pre Submission Reviewer?

As many as you like. As long as a model supports skills, you can use Phd Pre Submission Reviewer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Phd Pre Submission Reviewer AI skill free?

Yes. It is published on GitHub by Immortalqx under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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