📦 新增: math-textbook-compiler skill

- 包含完整的数学教材编译工作流
- 定义了 LaTeX 编译规范和 Python 习题约束
- 配置了 Git 部署流程和 Cron 定时任务
- 清理了旧的 ArXiv 相关文件

generate_arxiv_digest.js

@@ -1,208 +0,0 @@
-const fs = require('fs');
-const https = require('https');
-const Mustache = require('mustache');
-
-// Function to fetch and parse RSS feed
-function fetchRSS(url) {
-    return new Promise((resolve, reject) => {
-        https.get(url, (res) => {
-            let data = '';
-            res.on('data', (chunk) => {
-                data += chunk;
-            });
-            res.on('end', () => {
-                resolve(data);
-            });
-        }).on('error', (err) => {
-            reject(err);
-        });
-    });
-}
-
-// Simple XML parser for RSS feeds
-function parseRSS(rssData) {
-    const items = [];
-    
-    // Regular expressions to extract data from RSS
-    const itemRegex = /<item>([\s\S]*?)<\/item>/g;
-    const titleRegex = /<title><!\[CDATA\[(.*?)\]\]><\/title>/;
-    const descRegex = /<description><!\[CDATA\[(.*?)\]\]><\/description>/;
-    const linkRegex = /<guid[^>]*>(.*?)<\/guid>/;
-    const authorRegex = /<dc:creator>(.*?)<\/dc:creator>/g;
-    
-    let match;
-    while ((match = itemRegex.exec(rssData)) !== null) {
-        const itemData = match[1];
-        
-        const titleMatch = itemData.match(titleRegex);
-        const descMatch = itemData.match(descRegex);
-        const linkMatch = itemData.match(linkRegex);
-        
-        if (titleMatch && descMatch && linkMatch) {
-            const title = titleMatch[1].replace(/&amp;/g, '&').replace(/&lt;/g, '<').replace(/&gt;/g, '>');
-            const description = descMatch[1].replace(/&amp;/g, '&').replace(/&lt;/g, '<').replace(/&gt;/g, '>');
-            const link = linkMatch[1];
-            
-            // Extract authors
-            const authors = [];
-            let authorMatch;
-            while ((authorMatch = authorRegex.exec(itemData)) !== null) {
-                authors.push(authorMatch[1]);
-            }
-            
-            // Extract arXiv ID from link
-            const arxivId = link.split('/').pop();
-            
-            items.push({
-                title,
-                description,
-                link,
-                authors: authors.join(', '),
-                arxivId
-            });
-        }
-    }
-    
-    return items;
-}
-
-async function getLatestPapers() {
-    // Search queries for different categories
-    const queries = [
-        'cat:cs.RO+OR+cat:cs.AI+OR+cat:cs.CV+OR+cat:cs.LG+OR+cat:cs.CL+OR+cat:cs.MM', // General AI categories
-    ];
-    
-    let allPapers = [];
-    
-    for (const query of queries) {
-        const url = `https://export.arxiv.org/api/query?search_query=${encodeURIComponent(query)}&sortBy=submittedDate&sortOrder=descending&max_results=20`;
-        console.log(`Fetching papers from: ${url}`);
-        
-        try {
-            const rssData = await fetchRSS(url);
-            const papers = parseRSS(rssData);
-            allPapers = allPapers.concat(papers);
-        } catch (error) {
-            console.error(`Error fetching papers for query ${query}:`, error);
-        }
-    }
-    
-    // Remove duplicates based on arXiv ID
-    const seenIds = new Set();
-    const uniquePapers = allPapers.filter(paper => {
-        if (seenIds.has(paper.arxivId)) {
-            return false;
-        }
-        seenIds.add(paper.arxivId);
-        return true;
-    });
-    
-    // Sort by some relevance criteria (for now just take first 10)
-    return uniquePapers.slice(0, 10);
-}
-
-function extractTags(title, abstract) {
-    const text = `${title} ${abstract}`.toLowerCase();
-    const tags = [];
-    
-    if (text.includes('embodied') || text.includes('robot')) {
-        tags.push('embodied');
-    }
-    if (text.includes('representation') || text.includes('representations') || text.includes('learning representation')) {
-        tags.push('representation');
-    }
-    if (text.includes('reinforcement learning') || text.includes('rl ') || text.includes(' rl')) {
-        tags.push('rl');
-    }
-    if (text.includes('vision') || text.includes('visual')) {
-        tags.push('vision');
-    }
-    if (text.includes('language')) {
-        tags.push('language');
-    }
-    if (text.includes('multimodal')) {
-        tags.push('multimodal');
-    }
-    if (text.includes('manipulation')) {
-        tags.push('manipulation');
-    }
-    if (text.includes('navigation')) {
-        tags.push('navigation');
-    }
-    if (text.includes('world model') || text.includes('world-model')) {
-        tags.push('world-model');
-    }
-    
-    return [...new Set(tags)]; // Remove duplicate tags
-}
-
-function generateSummary(title, abstract) {
-    // This is a placeholder for a more sophisticated summary
-    // In a real implementation, this could use an LLM to generate insights
-    const insights = [
-        "This paper introduces novel approaches to the problem.",
-        "The methodology shows promising results compared to baseline methods.",
-        "The findings have implications for future research directions."
-    ];
-    
-    return insights[Math.floor(Math.random() * insights.length)];
-}
-
-async function generateDigest() {
-    console.log("Starting ArXiv digest generation...");
-    
-    const papers = await getLatestPapers();
-    console.log(`Found ${papers.length} papers`);
-    
-    // Filter papers to top 5 based on our criteria
-    const filteredPapers = papers
-        .map(paper => {
-            const tags = extractTags(paper.title, paper.description);
-            return { ...paper, tags };
-        })
-        .filter(paper => paper.tags.length > 0) // Only papers with relevant tags
-        .slice(0, 5); // Take top 5
-    
-    console.log(`Filtered to ${filteredPapers.length} relevant papers`);
-    
-    // Prepare data for template
-    const templateData = {
-        date: new Date().toISOString().split('T')[0],
-        category: 'AI Research',
-        time: new Date().toLocaleTimeString('zh-CN'),
-        papers: filteredPapers.map(paper => ({
-            title: paper.title,
-            authors: paper.authors,
-            arxiv_id: paper.arxivId,
-            arxiv_url: paper.link,
-            tags: paper.tags,
-            summary: generateSummary(paper.title, paper.description)
-        }))
-    };
-    
-    // Read the template
-    const template = fs.readFileSync('/home/zhn/.nvm/versions/node/v22.22.0/lib/node_modules/openclaw/skills/arxiv-digest/assets/template.html', 'utf8');
-    
-    // Render the template
-    const output = Mustache.render(template, templateData);
-    
-    // Write to file with today's date
-    const dateStr = new Date().toISOString().split('T')[0].replace(/-/g, '-');
-    const filename = `/home/zhn/arxiv-digests/arxiv-digest-${dateStr}.html`;
-    
-    fs.writeFileSync(filename, output);
-    
-    console.log(`Digest generated successfully: ${filename}`);
-    return filename;
-}
-
-// Run the generator
-generateDigest()
-    .then(filename => {
-        console.log('ArXiv digest generation completed:', filename);
-        process.exit(0);
-    })
-    .catch(error => {
-        console.error('Error generating digest:', error);
-        process.exit(1);
-    });

generate_arxiv_digest_v2.js

@@ -1,190 +0,0 @@
-const fs = require('fs');
-const arxiv = require('arxiv-api');
-const Mustache = require('mustache');
-
-// Function to search arXiv with specific categories
-async function searchPapers(query, maxResults = 20) {
-    try {
-        // Using the arxiv-api package to search for papers
-        const search = new arxiv.Search({
-            query: query,
-            maxResults: maxResults,
-            sortBy: 'submittedDate',
-            sortOrder: 'descending'
-        });
-
-        const results = await new Promise((resolve, reject) => {
-            search.fetch(resolve, reject);
-        });
-
-        return results.entries || [];
-    } catch (error) {
-        console.error('Error searching arXiv:', error);
-        return [];
-    }
-}
-
-function extractTags(title, summary) {
-    const text = `${title} ${summary}`.toLowerCase();
-    const tags = [];
-    
-    if (text.includes('embodied') || text.includes('robot') || text.includes('physical interaction')) {
-        tags.push('embodied');
-    }
-    if (text.includes('representation') || text.includes('representations') || text.includes('learning representation')) {
-        tags.push('representation');
-    }
-    if (text.includes('reinforcement learning') || text.includes('rl ') || text.includes(' rl') || text.includes('deep rl')) {
-        tags.push('rl');
-    }
-    if (text.includes('vision') || text.includes('visual')) {
-        tags.push('vision');
-    }
-    if (text.includes('language')) {
-        tags.push('language');
-    }
-    if (text.includes('multimodal')) {
-        tags.push('multimodal');
-    }
-    if (text.includes('manipulation')) {
-        tags.push('manipulation');
-    }
-    if (text.includes('navigation')) {
-        tags.push('navigation');
-    }
-    if (text.includes('world model') || text.includes('world-model')) {
-        tags.push('world-model');
-    }
-    if (text.includes('transformer') || text.includes('attention')) {
-        tags.push('transformers');
-    }
-    
-    return [...new Set(tags)]; // Remove duplicate tags
-}
-
-function generateSummary(title, summary) {
-    // Simple heuristic to generate insights - in practice, this could be enhanced with LLM
-    const insights = [
-        "This paper introduces novel approaches to the problem with promising experimental results.",
-        "An interesting contribution to the field with potential applications in real-world scenarios.",
-        "Methodologically sound approach with comprehensive evaluation against baseline methods.",
-        "Theoretical contributions with practical implications for future research directions.",
-        "Innovative combination of existing techniques showing improved performance."
-    ];
-    
-    return insights[Math.floor(Math.random() * insights.length)] + ` Abstract: ${summary.substring(0, 150)}...`;
-}
-
-async function generateDigest() {
-    console.log("Starting ArXiv digest generation...");
-    
-    // Search queries for different categories
-    const queries = [
-        'cat:cs.RO OR cat:cs.AI OR cat:cs.CV OR cat:cs.LG OR cat:cs.CL OR cat:cs.MM' // General AI categories
-    ];
-    
-    let allPapers = [];
-    
-    for (const query of queries) {
-        console.log(`Searching for papers with query: ${query}`);
-        const papers = await searchPapers(query, 15);
-        console.log(`Found ${papers.length} papers for query: ${query}`);
-        allPapers = allPapers.concat(papers);
-    }
-    
-    // Remove duplicates based on ID
-    const seenIds = new Set();
-    const uniquePapers = allPapers.filter(paper => {
-        if (seenIds.has(paper.id)) {
-            return false;
-        }
-        seenIds.add(paper.id);
-        return true;
-    });
-    
-    console.log(`Total unique papers found: ${uniquePapers.length}`);
-    
-    // Filter papers to top 5 based on relevance tags
-    const filteredPapers = uniquePapers
-        .map(paper => {
-            const tags = extractTags(paper.title, paper.summary);
-            return { 
-                ...paper, 
-                tags,
-                arxivId: paper.id.split('/').pop().replace('arXiv:', ''),
-                arxivUrl: paper.id
-            };
-        })
-        .filter(paper => paper.tags.length > 0) // Only papers with relevant tags
-        .slice(0, 5); // Take top 5
-    
-    console.log(`Filtered to ${filteredPapers.length} relevant papers`);
-    
-    if (filteredPapers.length === 0) {
-        console.log("No relevant papers found, creating digest with placeholders");
-        
-        // Create a default template with placeholder content
-        const templateData = {
-            date: new Date().toISOString().split('T')[0],
-            category: 'AI Research',
-            time: new Date().toLocaleTimeString('zh-CN'),
-            papers: [] // Empty array - template should handle this gracefully
-        };
-        
-        // Read the template
-        const template = fs.readFileSync('/home/zhn/.nvm/versions/node/v22.22.0/lib/node_modules/openclaw/skills/arxiv-digest/assets/template.html', 'utf8');
-        
-        // Render the template
-        const output = Mustache.render(template, templateData);
-        
-        // Write to file with today's date
-        const dateStr = new Date().toISOString().split('T')[0].replace(/-/g, '-');
-        const filename = `/home/zhn/arxiv-digests/arxiv-digest-${dateStr}.html`;
-        
-        fs.writeFileSync(filename, output);
-        
-        console.log(`Digest generated with placeholders: ${filename}`);
-        return filename;
-    }
-    
-    // Prepare data for template
-    const templateData = {
-        date: new Date().toISOString().split('T')[0],
-        category: 'AI Research',
-        time: new Date().toLocaleTimeString('zh-CN'),
-        papers: filteredPapers.map(paper => ({
-            title: paper.title,
-            authors: paper.authors.map(a => a.name).join(', '),
-            arxiv_id: paper.arxivId,
-            arxiv_url: paper.arxivUrl,
-            tags: paper.tags,
-            summary: generateSummary(paper.title, paper.summary)
-        }))
-    };
-    
-    // Read the template
-    const template = fs.readFileSync('/home/zhn/.nvm/versions/node/v22.22.0/lib/node_modules/openclaw/skills/arxiv-digest/assets/template.html', 'utf8');
-    
-    // Render the template
-    const output = Mustache.render(template, templateData);
-    
-    // Write to file with today's date
-    const dateStr = new Date().toISOString().split('T')[0].replace(/-/g, '-');
-    const filename = `/home/zhn/arxiv-digests/arxiv-digest-${dateStr}.html`;
-    
-    fs.writeFileSync(filename, output);
-    
-    console.log(`Digest generated successfully: ${filename}`);
-    return filename;
-}
-
-// Run the generator
-generateDigest()
-    .then(filename => {
-        console.log('ArXiv digest generation completed:', filename);
-        process.exit(0);
-    })
-    .catch(error => {
-        console.error('Error generating digest:', error);
-        process.exit(1);
-    });

generate_arxiv_digest_v3.js

@@ -1,285 +0,0 @@
-const fs = require('fs');
-const https = require('https');
-const Mustache = require('mustache');
-const { parseString } = require('xml2js');
-
-// Function to fetch and parse RSS feed from arXiv API
-function fetchRSS(url) {
-    return new Promise((resolve, reject) => {
-        https.get(url, (res) => {
-            let data = '';
-            res.on('data', (chunk) => {
-                data += chunk;
-            });
-            res.on('end', () => {
-                resolve(data);
-            });
-        }).on('error', (err) => {
-            reject(err);
-        });
-    });
-}
-
-// Parse XML using xml2js
-function parseXML(xmlData) {
-    return new Promise((resolve, reject) => {
-        parseString(xmlData, { explicitArray: false, ignoreAttrs: false }, (err, result) => {
-            if (err) {
-                reject(err);
-            } else {
-                resolve(result);
-            }
-        });
-    });
-}
-
-async function searchPapers(query, maxResults = 20) {
-    const url = `https://export.arxiv.org/api/query?search_query=${encodeURIComponent(query)}&sortBy=submittedDate&sortOrder=descending&max_results=${maxResults}`;
-    console.log(`Fetching papers from: ${url}`);
-    
-    try {
-        const rssData = await fetchRSS(url);
-        const parsedData = await parseXML(rssData);
-        
-        if (!parsedData.feed || !parsedData.feed.entry) {
-            console.log('No entries found in response');
-            return [];
-        }
-        
-        const entries = Array.isArray(parsedData.feed.entry) ? parsedData.feed.entry : [parsedData.feed.entry];
-        
-        return entries.map(entry => {
-            // Extract authors
-            const authors = Array.isArray(entry.author) ? entry.author.map(a => a.name) : [entry.author.name];
-            
-            // Extract arXiv ID from the ID field
-            const idMatch = entry.id ? entry.id.match(/\/abs\/(.+)$/) : null;
-            const arxivId = idMatch ? idMatch[1] : (entry.id || '').split('/').pop();
-            
-            return {
-                id: entry.id,
-                title: entry.title,
-                summary: entry.summary,
-                published: entry.published,
-                updated: entry.updated,
-                authors: authors,
-                arxivId: arxivId
-            };
-        });
-    } catch (error) {
-        console.error('Error fetching or parsing papers:', error);
-        return [];
-    }
-}
-
-function extractTags(title, summary) {
-    const text = `${title} ${summary}`.toLowerCase();
-    const tags = [];
-    
-    if (text.includes('embodied') || text.includes('robot') || text.includes('physical interaction')) {
-        tags.push('embodied');
-    }
-    if (text.includes('representation') || text.includes('representations') || text.includes('learning representation')) {
-        tags.push('representation');
-    }
-    if (text.includes('reinforcement learning') || text.includes('rl ') || text.includes(' rl') || text.includes('deep rl')) {
-        tags.push('rl');
-    }
-    if (text.includes('vision') || text.includes('visual')) {
-        tags.push('vision');
-    }
-    if (text.includes('language')) {
-        tags.push('language');
-    }
-    if (text.includes('multimodal')) {
-        tags.push('multimodal');
-    }
-    if (text.includes('manipulation')) {
-        tags.push('manipulation');
-    }
-    if (text.includes('navigation')) {
-        tags.push('navigation');
-    }
-    if (text.includes('world model') || text.includes('world-model')) {
-        tags.push('world-model');
-    }
-    if (text.includes('transformer') || text.includes('attention')) {
-        tags.push('transformers');
-    }
-    
-    return [...new Set(tags)]; // Remove duplicate tags
-}
-
-function generateSummary(title, summary) {
-    // Simple heuristic to generate insights
-    const insights = [
-        "This paper introduces novel approaches to the problem with promising experimental results.",
-        "An interesting contribution to the field with potential applications in real-world scenarios.",
-        "Methodologically sound approach with comprehensive evaluation against baseline methods.",
-        "Theoretical contributions with practical implications for future research directions.",
-        "Innovative combination of existing techniques showing improved performance."
-    ];
-    
-    return insights[Math.floor(Math.random() * insights.length)] + ` Abstract: ${(summary || '').substring(0, 150)}...`;
-}
-
-async function generateDigest() {
-    console.log("Starting ArXiv digest generation...");
-    
-    // Search queries for different categories
-    const queries = [
-        'cat:cs.RO OR cat:cs.AI OR cat:cs.CV OR cat:cs.LG OR cat:cs.CL OR cat:cs.MM' // General AI categories
-    ];
-    
-    let allPapers = [];
-    
-    for (const query of queries) {
-        console.log(`Searching for papers with query: ${query}`);
-        const papers = await searchPapers(query, 15);
-        console.log(`Found ${papers.length} papers for query: ${query}`);
-        allPapers = allPapers.concat(papers);
-    }
-    
-    // Remove duplicates based on ID
-    const seenIds = new Set();
-    const uniquePapers = allPapers.filter(paper => {
-        if (seenIds.has(paper.id)) {
-            return false;
-        }
-        seenIds.add(paper.id);
-        return true;
-    });
-    
-    console.log(`Total unique papers found: ${uniquePapers.length}`);
-    
-    // Filter papers to top 5 based on relevance tags
-    const filteredPapers = uniquePapers
-        .map(paper => {
-            const tags = extractTags(paper.title, paper.summary);
-            return { ...paper, tags };
-        })
-        .filter(paper => paper.tags.length > 0) // Only papers with relevant tags
-        .slice(0, 5); // Take top 5
-    
-    console.log(`Filtered to ${filteredPapers.length} relevant papers`);
-    
-    if (filteredPapers.length === 0) {
-        console.log("No relevant papers found, using fallback papers");
-        
-        // Create some sample papers for demonstration
-        const samplePapers = [
-            {
-                title: "Advances in Embodied AI: Challenges and Opportunities",
-                authors: ["Jane Smith", "John Doe"],
-                arxivId: "2602.01234",
-                id: "http://arxiv.org/abs/2602.01234v1",
-                tags: ["embodied", "ai"],
-                summary: "This paper explores the current state of embodied AI systems, discussing challenges in real-world deployment and proposing solutions for more robust implementations."
-            },
-            {
-                title: "Self-Supervised Representation Learning with Contrastive Predictive Coding",
-                authors: ["Alice Johnson", "Bob Wilson"],
-                arxivId: "2602.02345",
-                id: "http://arxiv.org/abs/2602.02345v1",
-                tags: ["representation", "learning"],
-                summary: "We present a novel approach to self-supervised learning that improves representation quality by leveraging predictive coding mechanisms."
-            },
-            {
-                title: "Deep Reinforcement Learning for Continuous Control Tasks",
-                authors: ["Charlie Brown", "Diana Prince"],
-                arxivId: "2602.03456",
-                id: "http://arxiv.org/abs/2602.03456v1",
-                tags: ["rl", "control"],
-                summary: "Our method achieves state-of-the-art results on continuous control benchmarks by combining actor-critic algorithms with advanced exploration strategies."
-            },
-            {
-                title: "Multimodal Fusion Networks for Cross-Modal Understanding",
-                authors: ["Eve Adams", "Frank Miller"],
-                arxivId: "2602.04567",
-                id: "http://arxiv.org/abs/2602.04567v1",
-                tags: ["multimodal", "vision", "language"],
-                summary: "We propose a new architecture for fusing visual and textual information, achieving superior performance on cross-modal retrieval tasks."
-            },
-            {
-                title: "World Models for Sample-Efficient Robot Learning",
-                authors: ["Grace Lee", "Henry Taylor"],
-                arxivId: "2602.05678",
-                id: "http://arxiv.org/abs/2602.05678v1",
-                tags: ["world-model", "embodied", "rl"],
-                summary: "This work demonstrates how world models can significantly improve sample efficiency in robot learning tasks through environment simulation."
-            }
-        ];
-        
-        // Prepare data for template
-        const templateData = {
-            date: new Date().toISOString().split('T')[0],
-            category: 'AI Research',
-            time: new Date().toLocaleTimeString('zh-CN'),
-            papers: samplePapers
-        };
-        
-        // Read the template
-        const template = fs.readFileSync('/home/zhn/.nvm/versions/node/v22.22.0/lib/node_modules/openclaw/skills/arxiv-digest/assets/template.html', 'utf8');
-        
-        // Render the template
-        const output = Mustache.render(template, templateData);
-        
-        // Write to file with today's date
-        const dateStr = new Date().toISOString().split('T')[0].replace(/-/g, '-');
-        const filename = `/home/zhn/arxiv-digests/arxiv-digest-${dateStr}.html`;
-        
-        fs.writeFileSync(filename, output);
-        
-        console.log(`Digest generated with sample papers: ${filename}`);
-        return filename;
-    }
-    
-    // Prepare data for template
-    const templateData = {
-        date: new Date().toISOString().split('T')[0],
-        category: 'AI Research',
-        time: new Date().toLocaleTimeString('zh-CN'),
-        papers: filteredPapers.map(paper => ({
-            title: paper.title,
-            authors: paper.authors.join(', '),
-            arxiv_id: paper.arxivId,
-            arxiv_url: paper.id,
-            tags: paper.tags,
-            summary: generateSummary(paper.title, paper.summary)
-        }))
-    };
-    
-    // Read the template
-    const template = fs.readFileSync('/home/zhn/.nvm/versions/node/v22.22.0/lib/node_modules/openclaw/skills/arxiv-digest/assets/template.html', 'utf8');
-    
-    // Render the template
-    const output = Mustache.render(template, templateData);
-    
-    // Write to file with today's date
-    const dateStr = new Date().toISOString().split('T')[0].replace(/-/g, '-');
-    const filename = `/home/zhn/arxiv-digests/arxiv-digest-${dateStr}.html`;
-    
-    fs.writeFileSync(filename, output);
-    
-    console.log(`Digest generated successfully: ${filename}`);
-    return filename;
-}
-
-// Install xml2js if not available
-try {
-    require.resolve('xml2js');
-} catch (e) {
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-    require('child_process').execSync('npm install xml2js');
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-generateDigest()
-    .then(filename => {
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-        process.exit(0);
-    })
-    .catch(error => {
-        console.error('Error generating digest:', error);
-        process.exit(1);
-    });

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package.json

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-{
-  "dependencies": {
-    "arxiv-api": "^1.1.1",
-    "jsdom": "^28.0.0",
-    "mustache": "^4.2.0",
-    "xml2js": "^0.6.2"
-  }
-}

skills/math-textbook-compiler/SKILL.md

@@ -0,0 +1,184 @@
+---
+name: math-textbook-compiler
+description: Hacker-Style Math-for-DL/RL Textbook Compiler. Use when: (1) Converting math textbooks into interactive courseware, (2) Generating daily learning materials from source textbooks, (3) Creating Python exercises with numpy/torch, (4) Scheduling batch compilation and deployment workflows, (5) Managing Git repositories for MathLab and RobotDaily projects
+---
+
+# Math Textbook Compiler
+
+## 🎯 Core Mission
+
+Convert mathematical formulas into engineering APIs to help overcome "symbol phobia". Transform dense math textbooks into interactive, hands-on learning materials.
+
+## 📚 Course Structure
+
+### Phase 1: Statistical Learning Methods (Symbol Desensitization)
+- Symbol familiarization
+- Convex optimization
+- Curse of dimensionality
+
+### Phase 2: Deep Learning (Deep Dive)
+- Computational graphs
+- Automatic differentiation
+- Tensor mappings
+
+### Phase 3: RL Math Foundations
+- State spaces
+- Markov Decision Processes (MDP)
+- Dynamic agent interactions
+
+## 🗂️ Directory Structure
+
+```
+clawd/
+├── staging/          # Draft materials awaiting release
+│   ├── course_dayN.html
+│   ├── exercises/dayN_task.py
+│   └── tests/test_dayN.py
+├── courseware/       # Final published course materials
+├── exercises/        # Python exercise files
+├── tests/            # Test cases
+├── templates/        # HTML/Python templates
+├── scripts/          # Automation scripts
+└── textbook/         # Source textbook files
+```
+
+## 🔧 Workflow
+
+### Batch Compilation (Saturday 10:00 AM)
+1. Read source textbooks from `/mnt/ai/textbooks/`
+   - `lihang-code` (Statistical Learning Methods)
+   - `d2l-zh` (Dive into Deep Learning)
+   - `Book-Mathematical-Foundation-of-Reinforcement-Learning`
+2. Generate 7 days of learning materials to `staging/`
+3. Each day includes:
+   - HTML courseware (6-module structure)
+   - Python exercises
+   - Test cases
+
+### Daily Deployment (14:00 PM)
+1. Move today's files from `staging/` to `courseware/`, `exercises/`, `tests/`
+2. Git commit and push to repositories
+3. Send Telegram notification
+
+## 📝 LaTeX Rules (STRICT)
+
+- **Inline formulas**: `$formula$` (no spaces inside delimiters)
+  - ✅ Correct: `$x + y$`
+  - ❌ Wrong: `$ x + y $`
+- **Display formulas**: `$$formula$$` (no spaces inside delimiters)
+  - ✅ Correct: `$$\sum_{i=1}^{n} x_i$$`
+  - ❌ Wrong: `$$ \sum_{i=1}^{n} x_i $$`
+
+## 🐍 Python Exercise Constraints
+
+- **Allowed**: `numpy`, `torch` (basic tensor operations only)
+- **Forbidden**: `torch.nn` and other high-level APIs
+- **Goal**: Implement algorithms from scratch to understand the math
+
+## 🌐 Git Repositories
+
+- **MathLab**: `https://code.indigofloyd.space/ClawLab/MathLab.git`
+  - Remote name: `origin`
+  - Contains: Textbook compiler, courseware, exercises
+- **RobotDaily**: `https://code.indigofloyd.space/ClawLab/RobotDaily.git`
+  - Remote name: `dailybot`
+  - Contains: Daily report deployment code
+
+### Git Configuration
+```bash
+git config http.sslVerify false  # Self-signed certificate
+git config credential.helper store
+```
+
+## 📋 Templates
+
+### HTML Course Module (6 sections)
+See `templates/course_module_template.html` for structure:
+1. Learning objectives
+2. Core concepts
+3. Visual explanations
+4. Interactive examples
+5. Practice exercises
+6. Summary & preview
+
+### Exercise Template
+See `templates/exercise_template.py`:
+```python
+"""
+Day N: [Topic]
+Implement [algorithm] from scratch using numpy/torch
+"""
+import numpy as np
+# or import torch
+
+def your_implementation():
+    pass
+```
+
+### Test Template
+See `templates/test_template.py`:
+```python
+"""
+Test cases for Day N exercises
+"""
+import numpy as np
+
+def test_implementation():
+    # Verify correctness
+    pass
+```
+
+## 🤖 Cron Jobs
+
+### Weekend Batch Compile
+- **Schedule**: Saturday 10:00 AM (Asia/Shanghai)
+- **Cron**: `0 10 * * 6`
+- **Job ID**: `87041122-59af-4f79-8599-e987fd494dad`
+- **Task**: Generate 7 days of materials from textbooks
+
+### Daily Deploy
+- **Schedule**: Daily 14:00 PM (Asia/Shanghai)
+- **Cron**: `0 14 * * *`
+- **Job ID**: `78dee83d-4c29-4f79-8599-e987fd494dad`
+- **Task**: Deploy staging files, git push, Telegram notify
+
+## 🛠️ Scripts
+
+### config-dailybot.sh
+Configure RobotDaily repository settings:
+```bash
+#!/bin/bash
+git remote add dailybot <url>
+git config remote.dailybot.url <url>
+```
+
+## 📦 Packaging
+
+When skill updates are needed:
+```bash
+cd /home/zhn/.nvm/versions/node/v22.22.0/lib/node_modules/openclaw/skills/skill-creator
+python3 scripts/package_skill.py ../math-textbook-compiler
+```
+
+## 🔍 Troubleshooting
+
+### HTTP 403 on Git Push
+- Check repository permissions on Gogs
+- Verify credentials in `~/.git-credentials`
+- Ensure `git config http.sslVerify false` for self-signed certs
+
+### Missing Textbook Files
+- Verify path: `/mnt/ai/textbooks/`
+- Check mount status of AI drive
+- Confirm file permissions
+
+### LaTeX Rendering Issues
+- Ensure no spaces in `$...$` or `$$...$$`
+- Check for unescaped special characters
+- Validate HTML template syntax
+
+## 📖 Reference Files
+
+- **Workflow details**: See `references/workflow.md`
+- **LaTeX cheat sheet**: See `references/latex-rules.md`
+- **Exercise patterns**: See `references/exercise-patterns.md`

skills/math-textbook-compiler/assets/example_asset.txt

@@ -0,0 +1,24 @@
+# Example Asset File
+
+This placeholder represents where asset files would be stored.
+Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
+
+Asset files are NOT intended to be loaded into context, but rather used within
+the output Codex produces.
+
+Example asset files from other skills:
+- Brand guidelines: logo.png, slides_template.pptx
+- Frontend builder: hello-world/ directory with HTML/React boilerplate
+- Typography: custom-font.ttf, font-family.woff2
+- Data: sample_data.csv, test_dataset.json
+
+## Common Asset Types
+
+- Templates: .pptx, .docx, boilerplate directories
+- Images: .png, .jpg, .svg, .gif
+- Fonts: .ttf, .otf, .woff, .woff2
+- Boilerplate code: Project directories, starter files
+- Icons: .ico, .svg
+- Data files: .csv, .json, .xml, .yaml
+
+Note: This is a text placeholder. Actual assets can be any file type.

skills/math-textbook-compiler/references/api_reference.md

@@ -0,0 +1,34 @@
+# Reference Documentation for Math Textbook Compiler
+
+This is a placeholder for detailed reference documentation.
+Replace with actual reference content or delete if not needed.
+
+Example real reference docs from other skills:
+- product-management/references/communication.md - Comprehensive guide for status updates
+- product-management/references/context_building.md - Deep-dive on gathering context
+- bigquery/references/ - API references and query examples
+
+## When Reference Docs Are Useful
+
+Reference docs are ideal for:
+- Comprehensive API documentation
+- Detailed workflow guides
+- Complex multi-step processes
+- Information too lengthy for main SKILL.md
+- Content that's only needed for specific use cases
+
+## Structure Suggestions
+
+### API Reference Example
+- Overview
+- Authentication
+- Endpoints with examples
+- Error codes
+- Rate limits
+
+### Workflow Guide Example
+- Prerequisites
+- Step-by-step instructions
+- Common patterns
+- Troubleshooting
+- Best practices

skills/math-textbook-compiler/scripts/example.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+"""
+Example helper script for math-textbook-compiler
+
+This is a placeholder script that can be executed directly.
+Replace with actual implementation or delete if not needed.
+
+Example real scripts from other skills:
+- pdf/scripts/fill_fillable_fields.py - Fills PDF form fields
+- pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
+"""
+
+def main():
+    print("This is an example script for math-textbook-compiler")
+    # TODO: Add actual script logic here
+    # This could be data processing, file conversion, API calls, etc.
+
+if __name__ == "__main__":
+    main()