sci-polish

sci-polish is a skill for Claude Code from ShZhao27208/Aut_Sci_Write. It costs 90 tokens per session (2,086 once invoked), scanned A, original, no licence file.

A two-stage tool for polishing academic papers. It first reduces patterns associated with AI-written text, then improves grammar, tone, clarity, structure, terminology, concision, and journal requirements.

In plain words
What is it for?
Use it to revise academic manuscripts for language quality, argument clarity, consistent terminology, concise structure, and journal compliance.
Why use it?
It addresses both the readability of a paper and the need to align its writing with academic or journal expectations.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the academic-skills plugin — 9 skills shipped together

Good fit Use it to revise academic manuscripts for language quality, argument clarity, consistent terminology, concise structure, and journal compliance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shzhao27208/aut_sci_write/sci-polish
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ShZhao27208/Aut_Sci_Write --skill sci-polish
Clone the repo
git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write

Made for: Claude Code.

Or install academic-skills, the plugin that ships this one along with the rest of its 9 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sci-polish

README.md
[![agentmods](https://agentmods.dev/badge/skills/shzhao27208/aut_sci_write/sci-polish.svg)](https://agentmods.dev/skills/shzhao27208/aut_sci_write/sci-polish)
Your own site
<a href="https://agentmods.dev/skills/shzhao27208/aut_sci_write/sci-polish"><img src="https://agentmods.dev/badge/skills/shzhao27208/aut_sci_write/sci-polish.svg" alt="Measured on agentmods" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,086 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.02086
Opus 5 $0.00045 $0.01043
Sonnet 5 $0.00018 $0.00417
Haiku 4.5 $0.00009 $0.00209

Measured 8d ago against content hash ca426c06e105, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

sci-polish scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/ai_score_estimator.py, scripts/tier1_words.py, scripts/validate_polish_output.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/sci-polish/SKILL.md · 243 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 243 lines · 90 tokens per session scan A ca426c06e105

Subscribe to this mod's changes

sci-polish is a skill published in the GitHub repository ShZhao27208/Aut_Sci_Write (192 stars, last pushed 25d ago), with no licence file. It adds 90 tokens to every session and 2,086 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

optimize-for-gpu

GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics…

dralkh/iktinah · 184 tokens

rowan

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related…

dralkh/iktinah · 100 tokens

bids

Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars…

dralkh/iktinah · 80 tokens

gget

Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch…

dralkh/iktinah · 93 tokens

pymoo

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

dralkh/iktinah · 46 tokens

zarr-python

Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

dralkh/iktinah · 52 tokens