Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
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.
npx skills add Galaxy-Dawn/claude-scholar --skill writing-anti-aigit clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarWrote 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.
[](https://agentmods.dev/skills/galaxy-dawn/claude-scholar/writing-anti-ai)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/writing-anti-ai"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/writing-anti-ai/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/writing-anti-ai"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/writing-anti-ai.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.02360 |
| Opus 5.5 | $0.00042 | $0.00944 |
| Sonnet 5.5 | $0.00021 | $0.00472 |
| Haiku 4.5 | $0.00011 | $0.00236 |
Grade A, and why
writing-anti-ai 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Anti-AI
Remove AI-generated writing patterns from text to make it sound natural and human-written. Supports both English and Chinese.
When editing academic prose, check the active installed
skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md
under the current client's skill home for relevant source-attributed writing
examples. Use it only to guide natural structure and rhythm. Keep the author's
meaning, evidence, and target venue constraints intact. If the memory is absent
or irrelevant, continue with this skill's own pattern references.
Overview
This skill identifies and eliminates predictable AI writing patterns from prose, based on Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup.
Core insight: LLMs use statistical algorithms to predict what should come next. The result tends toward the most statistically likely outcome that applies to the widest variety of cases—creating detectable patterns.
When to Use This Skill
Trigger phrases:
- "Humanize this text" / "人性化处理这段文字"
- "Remove AI writing patterns" / "去除 AI 写作痕迹"
- "Make this sound more natural" / "让这段文字更自然"
- "This sounds robotic/AI-generated" / "这听起来像机器写的"
- "Fix the AI patterns" / "修复 AI 模式"
Use cases:
- Editing AI-generated content to sound human
- Reviewing text for AI patterns before publication
- Polishing academic or professional writing
- Removing "slop" from prose
Core Rules (快速检查清单)
1. Cut Filler Phrases
Remove throat-clearing openers and emphasis crutches.
English examples:
- "In order to achieve this goal" → "To achieve this"
- "Due to the fact that" → "Because"
- "It is important to note that" → (delete)
中文示例:
- "为了实现这一目标" → "为了实现这一点"
- "值得注意的是" → (删除)
- "基于……的事实" → "因为"
2. Break Formulaic Structures
Avoid binary contrasts, dramatic fragmentation, rhetorical setups.
Patterns to avoid:
- Negative parallelisms: "It's not just X, it's Y"
- Rule of three: "A, B, and C" (prefer two or four items)
- Em-dash reveals: "X—Y" (just use commas)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +7 lines 5eac5eaef6cc
- 1mo ago First seen · 230 lines · 105 tokens per session scan A b86ea0c27159
writing-anti-ai is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,917 stars, last pushed 8d ago), licensed MIT. It adds 105 tokens to every session and 2,360 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
writing-anti-ai
This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI…
writing-anti-ai
This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI…
kami-deck
A lab-meeting deck on gut-microbiome links to sleep quality — the design, the results, the caveats, and the next experiment. Built as a decision-grade academic research deck for lab group, PI.
analyze-constraints-readiness
Characterize what blocks a research target: readiness dimensions, obstacles, bottlenecks, resource gaps, causal constraints, dependencies, conflicts, removability, and mitigation pathways. Depth selects lightweight triage, readiness assessment, or causal/TOC-style constraint analysis.
design-experiment
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness designs are modes, not strategy nodes.
acl-topic-selection
Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work, long-versus-short paper choice, the annual theme track, Findings-tier expectations, and sharpening the computational-linguistics framing before…