# Analyze Kids Content > Sub-skill: analyze-kids-content - Skill: `weizhengzhou/analyze-kids-content` (Agent Skill) - Install (CLI): `npx skillmds@latest add weizhengzhou/analyze-kids-content` - Raw SKILL.md: https://api.skillmd.com/api/skills/weizhengzhou/analyze-kids-content/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: WeizhengZhou (https://skillmd.com/u/weizhengzhou) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/weizhengzhou/analyze-kids-content --- # Sub-skill: analyze-kids-content Part of `/youtube-kids-audit`. Reads a scraped watch history, filters adult noise, and generates a personalized educational curriculum analysis for a specific child. **Model requirement:** Use `claude-opus-4-6` (Pro/Opus). This analysis requires expert-level educational judgment — do NOT use Haiku or any flash model. ## Usage ``` /youtube-kids-audit analyze-kids-content [--profile ] [--history ] ``` | Argument | Default | Description | |----------|---------|-------------| | `--profile` | prompts to choose | Child profile in `user_docs/kid_profile/.md` | | `--history` | latest `watch_history_*.json` | Scraped history from `/youtube-kids-audit` | | `--output` | `report` | `report` = markdown table + sections | --- ## Step 0: Load or create kid profile Check for existing profiles: ```bash ls user_docs/kid_profile/*.md 2>/dev/null | grep -v '_template\|README' ``` **If none exist**, run the intake interview — ask these one at a time: 1. **"What is your child's name and age?"** 2. **"Is this a dedicated YouTube account, or shared with an adult?"** - If shared: "Describe the adult content I should ignore (e.g. finance podcasts, political commentary, foreign-language drama)" 3. **"What's their current math level?"** (e.g. grade, curriculum name, specific topics) 4. **"What's their reading level?"** (e.g. reads independently vs with help, example books) 5. **"What are their top 3 interests?"** 6. **"What do they dislike or find boring?"** 7. **"What do they currently watch or listen to most?"** 8. **"Any context I should know — learning style, emotional sensitivities, goals?"** Write answers to `user_docs/kid_profile/.md` using `_template.md` schema. --- ## Step 1: Load watch history ```python import glob, json history_files = sorted(glob.glob("user_docs/watch_history_*.json"), reverse=True) if not history_files: raise FileNotFoundError( "No watch_history_*.json found. Run '/youtube-kids-audit' first to scrape." ) videos = json.load(open(history_files[0], encoding="utf-8")) print(f"Loaded {len(videos)} videos from {history_files[0]}") ``` --- ## Step 2: Format history as plain-text table (no URLs) ```python def format_history_table(videos: list[dict]) -> str: lines = ["| # | Title | Channel | Duration |", "|---|-------|---------|----------|"] for i, v in enumerate(videos, 1): lines.append( f"| {i} | {v.get('title','')[:70]} | {v.get('channel','')[:40]} | {v.get('durationRaw','')} |" ) return "\n".join(lines) ``` Do not include URLs or thumbnails — the model doesn't need them and it reduces token cost. --- ## Step 3: Build the analysis prompt Construct from the kid's profile. The prompt follows the structure of an Educational Consultant analyzing a personalized digital learning curriculum. ```python def build_prompt(profile: dict, history_table: str) -> str: name = profile["name"] age = profile["age"] math_level = profile.get("math_level", "grade-appropriate") reading_level = profile.get("reading_level", "") interests = "\n".join(f"- {i}" for i in profile.get("interests", [])) dislikes = "\n".join(f"- {d}" for d in profile.get("dislikes", [])) ignore = "\n".join(f"- {p}" for p in profile.get("ignore_patterns", [])) current_media = profile.get("current_books_shows", "") notes = profile.get("parent_notes", "") shared = bool(profile.get("ignore_patterns")) return f""" You are an expert Educational Consultant specializing in Gifted & Talented Elementary Education and personalized digital learning curricula. ## Child Profile - Name: {name}, Age: {age} - Math level: {math_level} - Reading level: {reading_level} - Interests (HIGH value): {interests} - Dislikes / avoid: {dislikes} - Currently consuming: {current_media} - Parent notes: {notes} ## Five Core Educational Goals When classifying and recommending content, evaluate it against these five goals: 1. **Expand Knowledge Breadth & Depth** — Move beyond surface facts into history, biography, global concepts, and cultural literacy. 2. **Improve English Holistic Skills** — Advance listening comprehension, complex narrative exposure, writing structure, and articulate speaking. Not phonics drills. 3. **Elevate Math Content** — CRITICAL: DO NOT recommend basic counting, arithmetic drills, or phonics-adjacent content. Content must match {name}'s actual level ({math_level}), focusing on logic, puzzles, and conceptual thinking. 4. **Broaden Science Horizons** — Spark curiosity across biology, physics, astronomy, engineering. Prefer inquiry-based ("How? Why?") over fact-listing. 5. **Reinforce Social-Emotional Learning (SEL)** — Support understanding of emotions, resilience, and growth mindset (especially important for advanced learners who rarely face challenge and may struggle when they finally do). ## Account Context {"This account is SHARED with an adult. You MUST IGNORE content matching these patterns:" if shared else "This is a dedicated child account."} {ignore} Focus your analysis on: children's educational content, audiobooks, cartoons, science/math enrichment, English language content, and fantasy/adventure stories. ## Watch History {history_table} --- ## Your Task Analyze the watch history above and produce a Strategic Educational Report in this exact structure: --- ### Part 1: Content Audit — What Is {name} Currently Exploring? Identify the dominant themes and "systems" {name} is consuming. For each theme, list 2-3 example titles and rate engagement (High/Medium/Low based on frequency, repeat views, or channel volume). **Level check:** Flag any content that appears to be *below* {name}'s level — content she has likely "graduated from" even though it may still appear in history. Examples: basic phonics channels for a fluent reader, simple counting for an advanced math learner. Label these **"Educational Junk Food"** — they feel productive but provide no new challenge. --- ### Part 2: Three-Way Content Classification Classify ALL relevant (non-adult) videos into one of these buckets: **✅ KEEP — The Foundation (already working)** Content aligned with the 5 goals that should remain core to her curriculum. For each item: channel name, why it fits, which goal(s) it serves. **🛑 REMOVE / GRADUATE FROM — Outdated or Misaligned** Content to deprecate. Two types: - *Too Easy / Mastered*: she has outgrown this (flag with "Educational Junk Food") - *Misaligned*: passive entertainment with low vocabulary/complexity for her level **🟢 ADD — The Growth Areas (gaps to fill)** This is the most important section. Recommend specific named YouTube channels or content types to fill gaps identified in the audit. Use these strategies: - **Elevate Math**: Logic puzzles, proof-based thinking, conceptual geometry — no drills. Must match her actual level. - **The "Trojan Horse"**: History / biography content framed as engineering, science, or systems — bypasses any dislike of "dates and names" history. - **Deepen English**: Fiction featuring competence, survival, and logic. Avoid whimsical/nonsense if she dislikes it. Audiobooks at native speed beat ESL-simplified videos for vocabulary growth. - **Science Breadth**: Inquiry-first channels (How? Why? What if?) over fact-listing channels. - **SEL for Gifted Learners**: Growth mindset content specifically addressing the "panic when things get hard" pattern common in advanced learners. --- ### Part 3: Curriculum Channel Table Produce a Markdown table of the recommended final channel lineup: | Channel / Playlist | Short Description | Why {name} Needs This | Goal Alignment | |-------------------|-------------------|-----------------------|----------------| | (keep + add items only, sorted by goal) | | | | Use these Goal labels: `Knowledge Breadth` | `English Skills` | `Math` | `Science` | `SEL` --- ### Part 4: Implementation Schedule Suggest a simple weekly rhythm (not a rigid schedule — just framing): | Time / Context | Content Type | Example | |----------------|-------------|---------| | Morning (fresh brain) | Active learning | Math puzzles, writing structure | | Relaxed / meal time | High-quality entertainment | Engineering shows, narrative history | | Car / bedtime | Audio-first | Audiobooks, mythology podcasts | --- ### Part 5: 3-5 Immediate Next Steps A specific, actionable parent checklist: - [ ] **Subscribe to:** [channel] — [one-line reason] - [ ] **Remove or stop:** [channel] — [one-line reason] - [ ] **Buy / borrow:** [book or audiobook] — start with [specific entry point] - [ ] **Ask {name}:** "[A question that opens a conversation about a gap topic]" """.strip() ``` --- ## Step 4: Call Claude Opus for analysis ```python import anthropic def run_analysis(prompt: str) -> str: client = anthropic.Anthropic() msg = client.messages.create( model="claude-opus-4-6", # Pro model required — not haiku/flash max_tokens=4096, messages=[{"role": "user", "content": prompt}] ) return msg.content[0].text ``` **Why Opus and not Haiku:** This task requires the model to: - Detect *level mismatches* between a child's academic profile and what they're watching - Identify "educational junk food" vs genuinely enriching content - Generate curriculum-quality recommendations tailored to a specific learner profile - Cross-reference five goal dimensions simultaneously Haiku will produce generic results. Opus produces recommendations that feel like a real educational consultant reviewed the data. --- ## Step 5: Save and display report ```python import datetime, os today = datetime.date.today().isoformat() safe_name = profile["name"].lower().replace(" ", "_") out_path = f"user_docs/kid_profile/{safe_name}_analysis_{today}.md" header = f"""# YouTube Watch History Analysis — {profile['name']} **Date:** {today} **Videos analyzed:** {len(videos)} **Model:** claude-opus-4-6 --- """ with open(out_path, "w", encoding="utf-8") as f: f.write(header + analysis_text) print(f"Report saved → {out_path}") print("\n" + analysis_text[:3000]) if len(analysis_text) > 3000: print(f"\n... [full report in {out_path}]") ``` --- ## Key concepts baked into the prompt ### "Educational Junk Food" Content that *feels* productive (it's about numbers! it's educational!) but provides no new challenge because the child has already mastered it. Common examples: - Basic phonics channels for a child who reads chapter books independently - Simple counting/number cartoons for a child doing advanced math curriculum - Letter-recognition apps for a fluent reader These are more harmful than pure entertainment because they consume curriculum time without developmental return, and may actually *bore* advanced learners into disliking "educational" content. ### "Consumer → Creator" gap Advanced readers often have a large intake-output gap: they consume complex content but haven't been guided to produce at a matching level. The analysis flags this and recommends writing-structure content (not just "write a story") to close it. ### The "Trojan Horse" recommendation For children who dislike a specific subject (e.g. traditional history), recommend content in their *preferred* domain that secretly teaches the gap subject: - History of medicine → satisfies anatomy interest + teaches history - Engineering history (bridges, rockets) → satisfies STEM interest + teaches history - Biography of a scientist → satisfies science interest + teaches biography genre ### Level-matching (critical for gifted learners) The prompt explicitly tells the model the child's actual academic level and instructs it NOT to recommend content below that level. A child doing 3rd-grade-level math should never see a recommendation for basic counting videos, regardless of how many counting videos appear in the history. --- ## Output files | File | Contents | Committed? | |------|----------|------------| | `user_docs/kid_profile/.md` | Kid profile | Parent's choice | | `user_docs/kid_profile/_analysis_.md` | Full report | No (gitignored) | --- ## File map ``` skills/media/youtube-kids-audit/ ├── SKILL.md ← Step 1: scrape watch history via CDP └── analyze-kids-content/ └── SKILL.md ← Step 2: this file — analyze + report user_docs/ ├── watch_history_.json ← scraped history (gitignored) └── kid_profile/ ├── README.md ← index ├── _template.md ← blank profile schema ├── .md ← filled-in kid profile └── _analysis_.md ← generated report (gitignored) ```