curriculum-designer

name curriculum-designer description Design customized curricula for PODs with REAL resource links. Staged implementation with checkpointing and fallback logic. Use when user says 'Design curriculum', 'Create curriculum for POD', or 'Build learning plan'. Curriculum Designer Design customized curricula for Apni Pathshala PODs with real YouTube video links . FEATURES: ✅ Staged execution with checkpointing (recovery from failures) ✅ YouTube link verification with fallback logic (no blank URLs) ✅ Context capping per lesson (reduced token usage) ✅ Every topic gets a valid video OR search query fallback ⚡ Quick Start How This Skill Works When invoked, the agent follows a 5-stage workflow with checkpointing: Stage What Happens Checkpoint File 1 Gather requirements requirements.json 2 Research YouTube videos research-results.json 3 Verify videos + fallback logic validated-resources.json 4 Design curriculum (one lesson at a time) curriculum-structure.json 5 Create Google Sheet final-sheet-url.txt Checkpoint Behavior Each stage saves its output to a checkpoint file If checkpoint exists, stage loads it and skips processing If checkpoint doesn't exist, stage runs from scratch Re-running resumes from first incomplete stage Trigger User message contains: "Design curriculum" → Start curriculum creation "Create curriculum for [POD name]" → Start with POD context "Build learning plan" → Start curriculum creation "Curriculum for [subject/topic]" → Start with topic context Target User This skill is designed for Madhur (Academic Associate) who designs curricula for PODs. Configuration API Keys: Stored locally in ~/.openclaw/workspace/skills/curriculum-designer/.env (NOT in git) Output Folder: 1upJQu-IVmZRJQsNGmJNRzq9IwL67MVL9 (Curriculum Designer) Checkpoint Directory: ~/.openclaw/workspace/curriculum-designer-checkpoints/ YouTube API Key: YOUTUBE_API_KEY=your_key_here Get from: https://console.cloud.google.com/apis/credentials Agent Workflow Instructions Model Allocation for Stages Action: Use different LLM models for different stages to optimize cost and performance. Stage-Specific Model Assignment Stage Recommended Model Reason Stage 1: Requirements Collection glm-4.7 Quick reasoning, sufficient for form filling Stage 2: YouTube Research glm-5 Fast research, needs latest web knowledge Stage 3: Video Validation glm-4.7 Pattern matching, simple logic Stage 4: Curriculum Design glm-4.7 Structured generation, cost-effective for lessons Stage 5: Sheet Creation glm-4.7 JSON formatting, simple transformations How to Set Models Option 1: Specify model when calling agent # Use glm-5 for research stage agent.chat --model glm-5 --message "Research YouTube videos for..." # Use glm-4.7 for design stage agent.chat --model glm-4.7 --message "Generate lesson structure..." Option 2: Configure in SKILL.md Each stage should include model recommendation in its instructions: ### Stage 2: Research YouTube Resources **Action:** Search YouTube for videos based on requirements **Recommended Model:** glm-5 (fast research, latest web knowledge) **Why:** Research needs up-to-date information and fast processing. Agent Workflow Instructions Stage 1: Gather Requirements Action: Ask the user these questions (from SOP): Basic Information POD Name - Which POD is this curriculum for? Target Audience - Grade level or age group of students? Subject Areas - What subjects/topics should be covered? Duration - How long is the program? (e.g., 1 month, 3 months, 6 months) Frequency - How many classes per week? Daily Lab Hours - How many hours will the lab operate? Previous Exposure - Have students done digital learning before? Teacher Context Teacher Capability - Can teachers operate computers independently? Teacher Training Needed - Do teachers need any training? Learning Outcomes Learning Area Focus - Which area(s) to prioritize? Digital Literacy Academic Empowerment Skill Development Employment Readiness Specific Skills - What specific skills should students acquire? Assessment Method - How will learning be measured? Output: Save to checkpoint as JSON: { "pod_name" : "Example POD" , "target_audience" : "Grade 8-10" , "subject_areas" : [ "Digital Literacy" , "Computer Basics" ] , "duration" : "1 month" , "frequency" : "3 days/week" , "daily_lab_hours" : 2 , "previous_exposure" : "None" , "teacher_capability" : "Basic" , "teacher_training_needed" : true , "learning_area_focus" : [ "Digital Literacy" ] , "specific_skills" : [ "Basic computer operations" , "Internet safety" ] , "assessment_method" : "Practical exercises and quizzes" } Checkpoint: ~/.openclaw/workspace/curriculum-designer-checkpoints/<timestamp>-<session-id>/requirements.json Stage 2: Research YouTube Resources Action: Search YouTube for videos based on requirements API: Use YouTube Data API v3 with key from .env Search Queries (Default): search_queries = [ "computer basics tutorial hindi beginners" , "typing practice hindi tutorial" , "internet browser basics hindi" , "gmail email tutorial hindi beginners" , "google docs tutorial hindi" , "google sheets tutorial hindi" , "chatgpt tutorial hindi beginners 2024" , "ai tools for students hindi" ] Search Parameters: part=snippet q=<query> type=video maxResults=5 videoDuration=medium (5-10 minutes preferred) relevanceLanguage=hi (Hindi preference) Output Structure: { "resources" : [ { "topic" : "computer basics" , "videos" : [ { "title" : "Computer Basics for Beginners in Hindi" , "channel" : "TechGuruji" , "url" : "https://youtube.com/watch?v=ABC123" , "video_id" : "ABC123" } ] } ] } Research Summary (Before Validation) After completing all searches, summarize the research results before passing to validation stage. Why summarize? Reduces token usage when passing to Stage 3 (validation) Provides cleaner input for validation logic Allows easy review of what was researched Summary Structure: { "research_summary" : { "total_searches" : 8 , "topics_researched" : [ "computer basics" , "typing practice" , "internet browser basics" , "gmail email tutorial" , "google docs tutorial" , "google sheets tutorial" , "chatgpt tutorial" , "ai tools for students" ] , "total_videos_found" : 24 , "video_channels" : [ "TechGuruji" , "LearnWithMe" , "DigitalSkills" , "HindiTechTutorials" ] , "search_language" : "Hindi preference" , "video_duration_preference" : "5-10 minutes" , "notes" : "Most videos from 2023-2024. Good variety of channels. Some topics have fewer results, may need fallback search." } } Save summary: Append research_summary to research-results.json Validation stage uses summary for context, not raw results Checkpoint: ~/.openclaw/workspace/curriculum-designer-checkpoints/<timestamp>-<session-id>/research-results.json Stage 3: Verify Videos + Fallback Logic Action: Verify each video via YouTube oEmbed API. If invalid, retry with alternative search terms. Verification Method Use oEmbed endpoint (fast, lightweight): https://www.youtube.com/oembed?url=https://youtube.com/watch?v=VIDEO_ID HTTP 200 = Valid video HTTP 404 = Invalid/deleted video HTTP 4xx/5xx = Try again (rate limit or temporary error) Fallback Logic (CRITICAL) For each topic , follow this logic: For each video in topic: 1. Verify via oEmbed 2. If valid → Add to validated list, done with topic 3. If invalid → Try next video in topic If NO valid videos found for topic: 1. Retry search with alternative queries: - Original query + "part 2" - Original query + "for students" - Original query + "in english" (if Hindi failed) 2. Verify new results 3. If still no valid videos → ADD FALLBACK: - "search_query": "<original query> tutorial hindi beginners" - "fallback_reason": "No valid videos found, please search manually" Output Structure (With Fallbacks) { "resources" : [ { "topic" : "computer basics" , "video" : { "title" : "Computer Basics for Beginners in Hindi" , "channel" : "TechGuruji" , "url" : "https://youtube.com/watch?v=ABC123" , "video_id" : "ABC123" , "status" : "valid" } } , { "topic" : "advanced excel" , "fallback" : { "search_query" : "advanced excel tutorial hindi beginners" , "reason" : "No valid videos found after 3 retry attempts" } } ] } IMPORTANT: Every topic MUST have either: A valid video URL, OR A search query fallback Checkpoint: ~/.openclaw/workspace/curriculum-designer-checkpoints/<timestamp>-<session-id>/validated-resources.json Stage 4: Design Curriculum (Context Capping + Summarization) Action: Generate curriculum structure, processing one lesson at a time with summarization and context cleanup. How Context Capping + Summarization Works Instead of: Pass entire curriculum (all lessons) to LLM at once → High token usage Do this: For each lesson (1, 2, 3, ... N): 1. Load lesson N context only (this lesson's topic + resources) 2. Generate lesson content 3. SUMMARIZE lesson N context 4. Save lesson + summary to curriculum structure 5. WIPE lesson N context from memory 6. Continue to next lesson When all lessons complete: 1. Summarize entire curriculum 2. Save summary to curriculum structure 3. Save summary to Stage 2 checkpoint (research-results.json) Lesson-by-Lesson Process For lesson N: Load context: Lesson N topic Lesson N resources (from validated-resources.json) Previous lesson summary (if N > 1) Generate lesson: Daily learning objectives Daily assessment Module content YouTube link/fallback Summarize lesson: Create concise summary of lesson N Focus on: key skills, tools used, assessment type Save to curriculum: Full lesson details Lesson summary (for next lesson's context) Context cleanup: Remove lesson N's full context from memory Keep only lesson N's summary for N+1 Lesson Summary Template { "lesson_number" : 1 , "summary" : "Students learned basic computer components, mouse/keyboard operations, and system navigation. Introduced primary computer parts and basic troubleshooting. Assessment involved identifying components and practicing typing." , "key_skills" : [ "Identifying computer parts" , "Mouse and keyboard basics" , "System navigation" ] , "tools_used" : [ "Computer" , "Mouse" , "Keyboard" ] , "assessment_type" : "Practical exercise and observation" } Lesson Generation Template For each lesson, generate: Field Description Day Lesson number (1, 2, 3, ...) Subject Subject area / Learning area Module Module/Topic name Daily Learning Objectives What students learn that day Daily Assessment How to assess understanding YouTube Link Valid video URL OR search query fallback YouTube Title Video title (if applicable) Tools Used Required software/platforms Fallback Search Query Search query if no valid video (or blank) Lesson Summary Concise summary for next lesson's context