Agent skill

Sci Polish

by ShZhao27208 in ShZhao27208/Aut_Sci_Write

Two-stage academic paper polishing skill. An agent skill from ShZhao27208/Aut_Sci_Write.

MITAuto-check passedWriting & Content

Install Sci Polish

skills CLI
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-polish -a claude-code

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

GitHub CLI
$ gh skill install ShZhao27208/Aut_Sci_Write sci-polish --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/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sci-polish .claude/skills/sci-polish && 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
sci-polish
GitHub stars
209
Token cost
~2.1k tokens
SKILL.md length
808 words
Files
12 (incl. scripts, references)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Two-stage academic paper polishing skill. An agent skill from ShZhao27208/Aut_Sci_Write.

  • Works in 4 steps: Input Processing → Phase A — De-AI Editing → Phase B — 8-Dimension Polish → …
  • The user asks to polish
  • SKILL.md covers Hard Rules (NEVER violate), Execution Modes, Step 0: Input Processing and Step 1: Phase A — De-AI Editing, plus 6 more sections
  • Runs Python scripts from its folder

What it does

Sci Polish is an agent skill from ShZhao27208/Aut_Sci_Write. Two-stage academic paper polishing skill. Stage A reduces AI detection traces (targeting GPTZero, Turnitin, Originality.ai). Stage B performs 8-dimension quality improvement (grammar, tone, coherence, conciseness, terminology, structure, argument clarity, journal compliance). Use whenever the user asks to polish, proofread, humanize, reduce AI rate, improve writing quality, or prepare a paper for submission.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts and reference files (for example `_meta.json`, `references/ai-detection-patterns.md` and `references/de-ai-strategies.md`).

It sits in Writing & Content, covering Humanizing AI text and Copy editing and proofreading. The repository describes itself as: Academic research skills suite for AI Agent — literature search/download (WoS+Elsevier+Springer), PDF extraction, figure cropping, review writing, Zotero sync, and PPT/Html… The licence is MIT.

When your agent uses it

  • The user asks to polish
  • Improve writing quality
  • Prepare a paper for submission

Example prompts

  • “/sci-polish”

Requirements

  • Python 3

Workflow steps

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

  1. Input Processing
  2. Phase A — De-AI Editing
  3. Phase B — 8-Dimension Polish
  4. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 357766f. 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

    Ships 3 files in scripts/ (Python), which the agent can run.

    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

Sci Polish loads about 2.1k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 808 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from ShZhao27208/Aut_Sci_Write at commit 357766f, republished under its MIT licence (© ShZhao27208). 808 words, ~2,109 tokens.

Download SKILL.mdSave it as .claude/skills/sci-polish/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
sci-polish
description
Two-stage academic paper polishing skill. Stage A reduces AI detection traces (targeting GPTZero, Turnitin, Originality.ai). Stage B performs 8-dimension quality improvement (grammar, tone, coherence, conciseness, terminology, structure, argument clarity, journal compliance). Use whenever the user asks to polish, proofread, humanize, reduce AI rate, improve writing quality, or prepare a paper for submission.
author
Shuo Zhao
license
MIT
copyright
© 2026 Shuo Zhao. All rights reserved.
triggers
润色论文, polish paper, reduce AI detection, 降AI率, de-AI, humanize paper, academic polish, 论文润色, 学术改写, improve writing, proofread, 降低AI率, 论文降重, paper polishing…

Sci-Polish — Academic Paper Polishing & De-AI

Two-stage academic paper polishing: AI trace reduction + 8-dimension quality improvement.


Hard Rules (NEVER violate)

  1. NEVER modify: LaTeX commands, math environments ($...$, \[...\], equation, align, etc.), \cite{}, \ref{}, \label{}, algorithm blocks, code listings, figure/table environments (only caption text is editable)
  2. NEVER fabricate: data, citations, experimental results, baselines, metrics, author names
  3. NEVER remove: author's technical claims without explicit permission
  4. NEVER introduce: Tier 1 AI words (see references/word-choice-anti-ai.md) during Phase B edits
  5. Mark as [PENDING VERIFICATION]: any claim you cannot verify from the text
  6. Preserve meaning: the exact semantic content of every technical statement must be unchanged
  7. Output format: revised text + polish report. No preamble or trailing changelog unless user asks
  8. When uncertain about domain terminology: keep the original, flag for user review

Execution Modes

ModeCommandBehavior
full (default)"polish this paper"Phase A → Phase B (all 8 dimensions)
deai-only"just reduce AI rate" / "只降AI率"Phase A only
polish-only"just polish, skip de-AI" / "只润色"Phase B only (all 8 dimensions)
polish-select"fix grammar and coherence"Phase B, selected dimensions only

Step 0: Input Processing

  1. Identify input format:
    • PDF → extract visible text (preserve section structure)
    • LaTeX → extract visible paragraphs (protect all commands)
    • Markdown / plain text → use directly
  2. Identify protection zones: math environments, citations, labels, algorithm blocks, code blocks, figure/table environments
  3. Ask target journal (optional): affects Dimension 8 scoring. If not specified, use general academic standards.
  4. Confirm execution mode: full / deai-only / polish-only / polish-select

Step 1: Phase A — De-AI Editing

Reference files:

  • references/ai-detection-patterns.md — pattern library for scanning
  • references/de-ai-strategies.md — rewriting strategies
  • references/word-choice-anti-ai.md — word replacement table
1.1 Scan

Scan text against ai-detection-patterns.md. Categorize findings:

  • High priority: Tier 1 words, template phrases, burstiness deficit (SD < 8), RLHF fingerprints
  • Medium priority: Tier 2 threshold violations, structural symmetry, transition stacking
  • Low priority: Tier 3 context-dependent, punctuation fingerprints, minor style uniformity
1.2 Rewrite (Priority Order)

Follow the strategy priority from de-ai-strategies.md:

a. Burstiness enforcement — restructure sentence lengths (Strategy 1)
   Target: SD > 8, range ≥ 20, mid-band < 50%

b. Vocabulary de-AI — replace Tier 1 words, compress template phrases (Strategy 2)
   Consult word-choice-anti-ai.md for replacements

c. Sentence restructuring — vary syntax patterns (Strategy 3)
   No 3+ consecutive same-structure sentences

d. Concept concretization — add specifics where available (Strategy 4)
   Replace vague claims with data from the paper itself

e. Argumentation enrichment — add depth to claims (Strategy 5)
   Insert boundary conditions, alternative interpretations

f. Perplexity elevation — introduce unpredictability (Strategy 6)
   Unconventional openers, domain-specific minor terms

g. Style disruption — break uniformity (Strategy 7)
   Vary paragraph density and register between sections
1.3 Self-Audit

After rewriting, re-scan output against ai-detection-patterns.md:

  • Any remaining High-priority patterns? → fix them
  • Burstiness check: SD > 8? Range ≥ 20? Mid-band < 50%?
  • Protection zones intact? (LaTeX commands, math, citations unchanged)
  • Meaning preserved? (no semantic drift from original)

Step 2: Phase B — 8-Dimension Polish

Reference file: references/polish-dimensions.md

2.1 Score All Dimensions (1-5)

Rate each dimension based on the checkpoints in polish-dimensions.md:

  1. Grammar & Syntax
  2. Academic Tone
  3. Coherence & Flow
  4. Conciseness
  5. Terminology
  6. Structure & Formatting
  7. Clarity of Argument
  8. Journal Compliance
2.2 Edit Dimensions Scoring ≤ 3

Edit in this order (foundation → logic → style → format):

Dim 1 (Grammar) → Dim 7 (Clarity) → Dim 3 (Coherence) →
Dim 4 (Conciseness) → Dim 2 (Tone) → Dim 5 (Terminology) →
Dim 6 (Structure) → Dim 8 (Journal)
2.3 Preservation Check

During Phase B editing, verify Phase A improvements are maintained:

  • Burstiness NOT reduced (sentence length variety preserved)
  • No Tier 1 AI words reintroduced
  • No template phrases reintroduced
  • Punctuation fingerprints still clean (em dash count, etc.)
2.4 Re-Score

Rate edited dimensions again. Record "Before" and "After" scores for the report.


Step 3: Output

3.1 Revised Text

Output the complete polished text with all modifications applied. Format matches the input format (LaTeX → LaTeX, Markdown → Markdown, etc.).

Show full SKILL.md (320 more words)Show less
3.2 Polish Report

Generate report using templates/polish-report.md:

  • Phase A summary: Number of AI traces detected and fixed, by priority level
  • Phase B scorecard: 8 dimensions, before/after scores, key changes per dimension
  • Top modifications: Up to 10 most significant before/after comparisons with reasoning
  • Pending items: Anything marked [PENDING VERIFICATION] or requiring user decision

Section-Specific Guidance

Abstract
  • Phase A focus: Remove "In recent years", overclaiming words, template openings
  • Phase B focus: Ensure all 6 elements present (context, gap, approach, insight, results, significance)
  • Length: Check against venue limits (typically 150-250 words)
Introduction
  • Phase A focus: Break three-part parallel structures, remove "has attracted growing attention"
  • Phase B focus: Contributions must be specific and verifiable, not vague
Methods
  • Phase A focus: Restore active subjects ("We" not passive-only), vary procedural sentence length
  • Phase B focus: Every symbol defined before use, equations motivated and interpreted
Results
  • Phase A focus: Replace "clearly demonstrates" with data speaking for itself
  • Phase B focus: Every claim backed by table/figure reference, statistical measures included
Discussion
  • Phase A focus: Break balanced-hedging patterns, ensure position is taken
  • Phase B focus: Limitations acknowledged specifically, future work concrete
Conclusion
  • Phase A focus: Remove "In conclusion, this paper presents" opener
  • Phase B focus: Brief, no methodology restatement, concrete future work

Voice Calibration (Optional)

If the user provides their previously published papers:

  1. Extract author writing profile (sentence length distribution, connector preferences, punctuation habits)
  2. Use author's actual patterns as the target instead of generic "human-sounding" defaults
  3. Note in report: "Voice calibrated against [paper title]"

This replaces generic de-AI rewrites with author-specific style matching.


Usage Examples

Full polish:

"请润色这篇论文" / "Polish this paper for NeurIPS submission"

De-AI only:

"只帮我降AI率" / "Reduce the AI detection score, don't change anything else"

Selective polish:

"只检查语法和术语一致性" / "Just fix grammar and check terminology consistency"

With voice calibration:

"用我之前发的这篇论文的风格来润色" / "Polish matching my writing style from this published paper"


© License

MIT License — © 2026 Shuo Zhao. All rights reserved.

This skill is part of the Aut_Sci_Write suite.

© ShZhao27208, MIT. 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 11 other files (scripts, references) in skills/sci-polish of ShZhao27208/Aut_Sci_Write.

  • SKILL.md
  • _meta.json
  • references/ai-detection-patterns.md
  • references/de-ai-strategies.md
  • references/journal-style-db.json
  • references/polish-dimensions.md
  • references/word-choice-anti-ai.md
  • scripts/ai_score_estimator.py
  • scripts/tier1_words.py
  • scripts/validate_polish_output.py
  • templates/polish-report.md
  • tests/golden_cases.json

Open the folder on GitHubat commit 357766f

Compare with similar skills

Sci Polish 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.

Sci Polish compared with similar skills
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Sci Polish this skillShZhao27208/Aut_Sci_Write209—~2.1kAutomated safety check: PassMIT
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Chinese Text Humanizerop7418/Humanizer-zh19k—~2kAutomated safety check: PassMIT
Natural Japanese Business Writingcoji/natural-japanese1.9k—~2.1kAutomated safety check: PassMIT
Zero Slop Prose Editoriflytek/skillhub5.2k—~1.5kAutomated safety check: PassMIT
Web Novel AI-Trace Removerzenstory-ai/oh-story-claudecode7.4k1 repos~2.6kAutomated safety check: PassMIT

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Questions about Sci Polish

What does Sci Polish do?

Two-stage academic paper polishing skill. An agent skill from ShZhao27208/Aut_Sci_Write. Sci Polish is an agent skill from ShZhao27208/Aut_Sci_Write. Two-stage academic paper polishing skill.

When should I use Sci Polish?

Sci Polish fits situations like: the user asks to polish; improve writing quality; prepare a paper for submission.

How do I install Sci Polish in Claude Code?

Run `npx skills add ShZhao27208/Aut_Sci_Write --skill sci-polish -a claude-code`. Or copy the skill folder (skills/sci-polish in ShZhao27208/Aut_Sci_Write) into .claude/skills/sci-polish in your project. Claude Code loads it when a task matches its description.

How do I install Sci Polish in Codex?

Run `npx skills add ShZhao27208/Aut_Sci_Write --skill sci-polish -a codex`. Or copy the skill folder (skills/sci-polish in ShZhao27208/Aut_Sci_Write) into .agents/skills/sci-polish in your project. Codex loads it when a task matches its description.

Can I use Sci Polish 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 ShZhao27208/Aut_Sci_Write --skill sci-polish -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sci-polish, .gemini/skills/sci-polish, .github/skills/sci-polish and .opencode/skills/sci-polish in your project.

What does Sci Polish need to run?

Going by SKILL.md and its folder, Sci Polish needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Sci Polish 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 Sci Polish 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Sci Polish use?

Sci Polish is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Sci Polish use?

About 2.1k tokens (SKILL.md is roughly 8.4k 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 12k tokens, read only when the agent opens those files.

What are the alternatives to Sci Polish?

Skills that share tags, products or a category with Sci Polish: User-Facing Text Cleanup (guillaumemeyer/watermarks-remover, 24k stars), Chinese Text Humanizer (op7418/Humanizer-zh, 19k stars), Natural Japanese Business Writing (coji/natural-japanese, 1.9k stars) and Zero Slop Prose Editor (iflytek/skillhub, 5.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sci Polish?

ShZhao27208 (a GitHub user) maintains it in ShZhao27208/Aut_Sci_Write, which has 209 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on August 13, 2026.

Source: ShZhao27208/Aut_Sci_Write on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.