Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Access reMarkable tablet documents, notebooks, PDFs, and EPUBs. Use when the user wants to read, search, browse, or extract text from their reMarkable tablet. Supports handwriting OCR, typed text, annotations, highlights, and page rendering to PNG/SVG.
Summarises text into a concise overview. Use when the user asks to summarise, condense, or get the key points from a piece of text.
Use when the user wants OCR on images, screenshots, scans, receipts, diagrams, or image files; extract text from a local image path, image URL, or base64 image; convert OCR output to plain text, markdown table, structured JSON, or code comments; or rename, summarize, or post-process files based on recognized text. Prefer this skill for image-to-text workflows backed by the local ocrtool-mcp binary.
Run comprehensive pre-press preflight checks on Adobe Illustrator documents using illustrator-mcp tools. Detects print-critical issues (RGB in CMYK, broken links, low-res images, white overprint, text not outlined), text consistency problems (dummy text, notation variations), and PDF/X compliance. Use when user asks to check a document before printing, submission, or handoff — or mentions "preflight", "pre-press check", "print check", "submission check".
Use when writing, editing, or reviewing Russian-language text, or when user mentions ru-text. Covers typography, info-style, editorial, UX writing, business correspondence. Auto-activates on Russian text output. --- # ru-text — Russian Text Quality Independent Russian text quality reference by Arseniy Kamyshev. With gratitude to the authors whose work shaped modern Russian text standards.
C刊（CSSCI来源期刊）论文全面分析工具。当用户提供一个具体的C刊期刊名称（如"管理世界"、 "社会学研究"、"经济研究"等）时，自动通过知网（CNKI）查询该期刊最近5年所有期次的文章 目录、作者和摘要信息，并生成专业的Word分析报告。报告包含：选题热点趋势、高频关键词、 研究方法偏好、核心作者群、栏目主题演变、研究空白识别、投稿方向建议等全维度分析。 触发条件：用户提到需要分析某个C刊/CSSCI期刊/核心期刊的发文趋势、选题偏好、投稿方向； 或提供中文学术期刊名称并要求查看近年发表论文的主题分布和趋势；或说"帮我分析一下XX期刊"。 注意：本skill用于期刊层面的宏观分析，不同于paper-analyzer（单篇论文拆解）和 literature-review-writer（文献综述写作）。 --- # C刊论文全面分析工具 ## Phase 1: 确定期刊与CNKI代码 1. 从用户输入中提取期刊名称 2. 查询 `references/journal_codes.md` 获取CNKI代码（如"管理世界"→ `GLSJ`） 3. 若未收录，用WebSearch搜索 `site:navi.cnki.net/knavi/journals "{期刊名}"` 从URL提取代码 4. 向用户确认期刊后继续 ## Phase 2: 浏览器数据采集 使用Chrome DevTools MCP工具从知网采集数据。 ### Step 2.1: 打开期刊页 ``` navigate_page → https://navi.cnki.net/knavi/journals/{CODE}/detail ``` **验证码处理**：若页面出现"请完成安全验证"或"拖动下方拼图"，立即提示用户： > "知网需要安全验证，请在浏览器中完成滑块验证，完成后告诉我。" 等用户确认后，用 `navigate_page` 重新加载页面。 ### Step 2.2: 提取期刊基本信息 ```javascript () => { const title = document.querySelector('h3')?.textContent?.trim() || ''; const info = {}; document.querySelectorAll('.detailInfo p, .s-info p').forEach(p => { const text = p.textContent; if (text.includes('主办单位')) info.sponsor = text.split('：')[1]?.trim(); if (text.includes('ISSN')) info.issn = text.split('：')[1]?.trim(); if (text.includes('CN')) info.cn = text.split('：')[1]?.trim(); if (text.includes('出版周期')) info.frequency = text.split('：')[1]?.trim(); if (text.includes('复合影响因子')) info.cif = text.split('：')[1]?.trim(); if (text.includes('综合影响因子')) info.aif = text.split('：')[1]?.trim(); }); return { title, ...info }; } ``` 也可直接从snapshot中读取基本信息（StaticText节点）。 ### Step 2.3: 点击"论文"标签并提取年份期次 点击 uid 对应"论文"的链接，等待加载，然后提取： ```javascript () => { const results = []; document.querySelectorAll('dl[id$="_Year_Issue"]').forEach(dl => { const year = dl.querySelector('dt em')?.textContent?.trim(); if (!year) return; const issues = []; dl.querySelectorAll('dd a').forEach(a => { issues.push({ id: a.id, issue: a.textContent.trim(), value: a.getAttribute('value') }); }); results.push({ year: parseInt(year), issues }); }); return results;
