chore: use skills instead of rules

cli.js

@@ -3,7 +3,7 @@
 import { fileURLToPath } from 'url';
 import { dirname, join } from 'path';
 import { existsSync, mkdirSync, writeFileSync, readFileSync } from 'fs';
-import { spawnSync } from 'child_process';
+import { spawnSync, spawn } from 'child_process';
 import inquirer from 'inquirer';
 import chalk from 'chalk';
 import fs from 'fs-extra';
@@ -208,12 +208,14 @@ async function main() {
         name: 'aiTools',
         message:
           'Which AI coding assistant(s) are you using? (Select all that apply)\n' +
-          chalk.gray('   We’ll add local instruction files so your assistant knows Cloudinary patterns.\n'),
+          chalk.gray('   We\'ll install Cloudinary skills in the right location for each tool.\n'),
         choices: [
           { name: 'Cursor', value: 'cursor' },
-          { name: 'GitHub Copilot', value: 'copilot' },
           { name: 'Claude Code', value: 'claude' },
-          { name: 'Other / Generic AI tools', value: 'generic' },
+          { name: 'GitHub Copilot', value: 'copilot' },
+          { name: 'OpenAI Codex', value: 'codex' },
+          { name: 'Gemini CLI', value: 'gemini' },
+          { name: 'Other', value: 'generic' },
         ],
         default: ['cursor'],
       },
@@ -316,35 +318,68 @@ async function main() {
     copyTemplate(file);
   });
 
-  // Create AI rules based on user's tool selection
-  const aiRulesTemplatePath = join(TEMPLATES_DIR, '.cursorrules.template');
-  if (existsSync(aiRulesTemplatePath) && aiTools && aiTools.length > 0) {
-    const aiRulesContent = replaceTemplate(
-      readFileSync(aiRulesTemplatePath, 'utf-8'),
-      templateVars
-    );
+  // Install Cloudinary skills into the directories each selected tool actually reads:
+  // .cursor/skills/  = Cursor
+  // .claude/skills/  = Claude Code
+  // .agents/skills/  = Copilot, Codex, Gemini, Generic (and any unrecognised tool)
+  console.log(chalk.blue('\n🤖 Installing Cloudinary AI skills...\n'));
+
+  const skillTargetDirs = new Set();
+  if (aiTools && aiTools.includes('cursor')) skillTargetDirs.add(join(projectPath, '.cursor', 'skills'));
+  if (aiTools && aiTools.includes('claude')) skillTargetDirs.add(join(projectPath, '.claude', 'skills'));
+  if (!aiTools || aiTools.some(t => !['cursor', 'claude'].includes(t))) {
+    skillTargetDirs.add(join(projectPath, '.agents', 'skills'));
+  }
 
-    // Generate files based on selected tools
-    if (aiTools.includes('cursor')) {
-      writeFileSync(join(projectPath, '.cursorrules'), aiRulesContent);
-    }
+  const skills = ['cloudinary-docs', 'cloudinary-react', 'cloudinary-transformations'];
+  // Download each skill once then copy to any additional targets
+  const [primaryDir, ...additionalDirs] = skillTargetDirs;
+  mkdirSync(primaryDir, { recursive: true });
 
-    if (aiTools.includes('copilot')) {
-      const githubDir = join(projectPath, '.github');
-      mkdirSync(githubDir, { recursive: true });
-      writeFileSync(join(githubDir, 'copilot-instructions.md'), aiRulesContent);
-    }
+  // Print all skill names upfront so the user sees activity immediately
+  for (const skill of skills) {
+    console.log(chalk.gray(`   Fetching ${skill}...`));
+  }
 
-    if (aiTools.includes('claude')) {
-      writeFileSync(join(projectPath, 'CLAUDE.md'), aiRulesContent);
+  // Download all 3 skills in parallel
+  const results = await Promise.all(
+    skills.map(skill => new Promise(resolve => {
+      const proc = spawn(
+        'npx',
+        ['--yes', 'degit', `cloudinary-devs/skills/skills/${skill}`, join(primaryDir, skill)],
+        { stdio: 'pipe', shell: false }
+      );
+      proc.on('close', code => resolve({ skill, ok: code === 0 }));
+    }))
+  );
+
+  const BUNDLED_SKILLS_DIR = join(__dirname, 'skills');
+
+  let skillsInstalled = 0;
+  for (const { skill, ok } of results) {
+    if (ok) {
+      console.log(chalk.gray(`   ✓ ${skill}`));
+      skillsInstalled++;
+    } else {
+      const bundled = join(BUNDLED_SKILLS_DIR, skill);
+      if (existsSync(bundled)) {
+        fs.copySync(bundled, join(primaryDir, skill));
+        console.log(chalk.gray(`   ✓ ${skill} (installed from local cache)`));
+        skillsInstalled++;
+      } else {
+        console.warn(chalk.yellow(`   ⚠ Could not install ${skill}`));
+      }
     }
+  }
 
-    if (aiTools.includes('generic')) {
-      writeFileSync(join(projectPath, 'AI_INSTRUCTIONS.md'), aiRulesContent);
-      writeFileSync(join(projectPath, 'PROMPT.md'), aiRulesContent);
+  if (skillsInstalled > 0) {
+    for (const target of additionalDirs) {
+      fs.copySync(primaryDir, target);
     }
+  }
 
-    // Generate MCP configuration: Cursor uses .cursor/mcp.json, Claude Code uses .mcp.json in project root
+  // Generate MCP configuration: Cursor uses .cursor/mcp.json, Claude Code uses .mcp.json in project root
+  if (aiTools && aiTools.length > 0) {
     const mcpTemplatePath = join(TEMPLATES_DIR, '.cursor/mcp.json.template');
     if (existsSync(mcpTemplatePath)) {
       const mcpContent = replaceTemplate(
@@ -370,26 +405,16 @@ async function main() {
 
   console.log(chalk.green('✅ Project created successfully!\n'));
 
-  if (aiTools && aiTools.length > 0) {
-    // Count actual files created
-    let fileCount = 0;
-    if (aiTools.includes('cursor')) fileCount += 2; // .cursorrules + mcp.json
-    if (aiTools.includes('copilot')) fileCount += 1;
-    if (aiTools.includes('claude')) fileCount += 2; // CLAUDE.md + .mcp.json
-    if (aiTools.includes('generic')) fileCount += 2; // AI_INSTRUCTIONS.md + PROMPT.md
-
-    const filesText = fileCount === 1 ? 'file' : 'files';
-    console.log(chalk.cyan(`📋 AI assistant configuration ${filesText} created:`));
-    if (aiTools.includes('cursor')) console.log(chalk.gray('   • Cursor: .cursorrules'));
-    if (aiTools.includes('copilot')) console.log(chalk.gray('   • GitHub Copilot: .github/copilot-instructions.md'));
-    if (aiTools.includes('claude')) console.log(chalk.gray('   • Claude: CLAUDE.md'));
-    if (aiTools.includes('generic')) console.log(chalk.gray('   • Generic: AI_INSTRUCTIONS.md, PROMPT.md'));
-    if (aiTools.includes('cursor')) console.log(chalk.gray('   • MCP (Cursor): .cursor/mcp.json'));
-    if (aiTools.includes('claude')) console.log(chalk.gray('   • MCP (Claude Code): .mcp.json'));
-    console.log(chalk.gray(`\n   ${fileCount === 1 ? 'This file teaches' : 'These files teach'} your AI assistant about Cloudinary patterns and best practices.`));
-    console.log(chalk.gray(`\n   💡 How to use ${fileCount === 1 ? 'this file' : 'these files'}:`));
-    console.log(chalk.gray('   • Simply open your project in your AI assistant - the configuration is already loaded'));
-    console.log(chalk.gray('   • Ask your AI to help build Cloudinary features, and it will follow these patterns'));
+  if (skillsInstalled > 0) {
+    console.log(chalk.cyan(`\n📋 AI skills installed → .agents/skills/ (works with Cursor, Claude Code, Copilot, and more):`));
+    console.log(chalk.gray('   • cloudinary-docs — looks up live Cloudinary documentation'));
+    console.log(chalk.gray('   • cloudinary-react — uses React SDK patterns and best practices'));
+    console.log(chalk.gray('   • cloudinary-transformations — creates and debugs Cloudinary transformation URLs from natural language instructions'));
+    if (aiTools && aiTools.includes('cursor')) console.log(chalk.gray('   • MCP (Cursor): .cursor/mcp.json'));
+    if (aiTools && aiTools.includes('claude')) console.log(chalk.gray('   • MCP (Claude Code): .mcp.json'));
+    console.log(chalk.gray('\n   💡 How to use:'));
+    console.log(chalk.gray('   • Open your project in your AI assistant — skills are picked up automatically'));
+    console.log(chalk.gray('   • Ask your AI assistant to help build Cloudinary features and it will use these skills'));
     console.log(chalk.gray('   • Example prompts: "Add image upload", "Create a transformation gallery"\n'));
   }
 

skills/cloudinary-docs/SKILL.md

@@ -0,0 +1,53 @@
+---
+name: cloudinary-docs
+description: Local version -- Looks up implementation details in the latest Cloudinary docs via llms.txt. Use when building code or answering questions relating to image or video uploads, optimization, or transformations, and for Cloudinary SDKs, APIs, webhooks, or integrations.
+license: MIT
+metadata:
+  author: cloudinary
+  version: '1.0.1'
+---
+
+# Cloudinary Documentation
+
+Helps developers integrate Cloudinary into their applications by providing documentation and code examples retrieved directly from the optimized markdown files in the Cloudinary documentation.
+
+## When to Use
+
+- When a user asks questions or requests code implementation relating to image or video upload, management, optimization, or transformations (resizing, applying effects, visual improvements, adding overlays, generative AI, etc.)
+- User asks about Cloudinary SDKs, upload APIs, or integration guides
+- General Cloudinary documentation lookup (account settings, webhooks, DAM features)
+- Looking up specific Cloudinary API endpoints or SDK methods
+- Use this skill in conjunction with more specialized Cloudinary skills when relevant.
+
+## Instructions
+
+When answering image and video upload, management, optimization, or transformation questions or when implementing Cloudinary code:
+
+1. **First, get the documentation index** using llms.txt with the llms.txt URL - https://cloudinary.com/documentation/llms.txt
+2. **Analyze the llms.txt content** to understand what documentation pages are available
+3. **Reflect on the user's question** and identify which specific documentation URLs would be most relevant
+4. **Navigate** to the specific relevant documentation URLs from the llms.txt index (you can make multiple calls)
+5. **Use the fetched documentation** to provide a comprehensive, accurate answer or code implementation.  When relevant, use in conjunction with more specialized Cloudinary skills like cloudinary-transformations. The best practices defined in the specialized skills should guide which doc instructions to use.
+
+Example workflows:
+
+**Example 1: Upload question**
+- User asks: "How do I upload images to Cloudinary?"
+- You retrieve the llms.txt index: https://cloudinary.com/documentation/llms.txt
+- You analyze the llms.txt content to understand what documentation pages are available
+- You identify relevant pages like "image_upload.md" or "upload_api.md"
+- You retrieve those specific pages from the llms.txt index
+- You provide an answer with code examples and citations
+
+**Example 2: Transformation question**
+- User asks: "How do I resize and crop images?"
+- You retrieve the llms.txt index
+- You identify relevant pages like "image_transformations.md" or "transformation_reference.md"
+- You fetch the specific documentation
+- You provide transformation syntax and examples
+
+**Example 3: SDK question**
+- User asks: "What's the Node.js SDK for Cloudinary?"
+- You retrieve the llms.txt index
+- You identify SDK-related pages
+- You provide installation instructions and usage examples