youtube-analyzer

name	youtube-analyzer
description	Analyze YouTube videos with content-type detection, multi-agent transcript chunking, optional GitHub repo exploration, and structured markdown output. Use when the user asks to "analyze a YouTube video", "summarize this video", "extract insights from a tutorial", or pastes a YouTube URL with intent to analyze.

YouTubeAnalyzer Skill

Purpose: Comprehensive YouTube video analysis with content-type detection, specialized format workflows (tutorial/finance/general/etc.), multi-agent orchestration for long videos, and optional GitHub repo cross-referencing for tutorial content.

Architecture: This file is the orchestration spine — 4 phases with blocking gates. Mechanical detail is progressively disclosed via references/*.md (loaded by phase) and content-analysis rules live in workflows/*.md (passed into sub-agents).

File	Read when
references/source-selection.md	Starting Phase 1
references/scaling-and-repo-explore.md	Starting Phase 3
references/output-paths.md	Phase 4 Step 4.5, only if mode == "document"
references/output-templates.md	Phase 4 Step 4.3, passed into synthesis agent
content-types.md	Phase 2 Q1, when detection confidence < 60%
workflows/<format>-workflow.md	Phase 4 Step 4.1, passed into chunk agents
package-database-schema.md	Phase 4 Step 4.4, tutorials only

---

Invocation Flags

Parse the user's invocation BEFORE Phase 1. Flags pre-set Phase 2 outputs and suppress the matching prompt.

Syntax accepted (any of these forms):

• --chat <url> — deliver inline, no file written
• --chat: <url> / --chat=<url>
• --document <url> (explicit; default behavior)

Flag	Effect	Pre-sets
--chat	Skip Q5; deliver analysis inline (no file written)	mode = "chat"
--document	Skip Q5; save to resolved output directory (default)	mode = "document"

Parsing rules:

1. Strip the flag token (and any trailing : or =value) before extracting the URL
2. If a flag was passed: set mode and SKIP Q5
3. Both flags present → last one wins
4. Unknown flags (--foo) → warn user inline, proceed without it

When a flag was detected, surface it: "Flag detected: --chat → mode preset to chat (Q5 skipped)."

---

Required External Tools

Verify these are installed before Phase 1. If missing, surface a single install message and stop:

• yt-dlp — pip install yt-dlp or brew install yt-dlp
• youtube_transcript_api — pip install youtube-transcript-api
• bun — curl -fsSL https://bun.sh/install | bash

command -v yt-dlp && command -v youtube_transcript_api && command -v bun

---

Capture invocation cwd

BEFORE Phase 1, capture the working directory the user invoked the skill from. Store as invocationCwd. Used by Step 4.7 (post-save copy prompt).

pwd

Persist that value through all phases. Sub-agents may run elsewhere — only the orchestrator's initial cwd counts.

---

Runtime Requirements

This skill runs from any agent context (the primary thread, or a delegated agent like general-purpose) provided the calling agent has: Agent/Task, AskUserQuestion, Write/Edit, Bash, Read, Grep. Agents lacking any of these (e.g. Explore) should defer — auto-detect via tool availability, not by name.

The terms "the orchestrator" and "the calling agent" both refer to whatever agent is executing this skill.

---

Orchestration Model

The orchestrator NEVER does analysis work directly. All content extraction, processing, and writing is delegated to specialized sub-agents via the Task tool. The orchestrator coordinates, routes, and writes files (in mode == "document") once results return.

EVERY phase has a BLOCKING GATE. Do not proceed until the gate checklist is satisfied.

---

PHASE 1: SOURCE SELECTION

BLOCKING GATE 1

PRE-CONDITIONS: External tools verified
MANDATORY OUTPUTS:
  - transcriptPath: string     # Path to loaded clean transcript file
  - transcriptSource: string   # "yt-dlp" | "youtube_transcript_api"
  - videoMetadata: object      # { title, channel, duration?, upload_date?, video_id?, topic? }
  - wordCount: number          # Estimated word count
  - transcriptQuality: string  # "HIGH" | "MEDIUM" | "NONE" | "UNAVAILABLE"

Mechanics: Read references/source-selection.md. It covers URL extraction, the 4-tier transcript fallback chain, and VTT cleanup.

Auto-detect: If the user already provided a YouTube URL, skip the URL prompt and go directly to metadata extraction.

Gate 1 checklist (verify ALL):

• transcriptPath exists and is readable
• transcriptSource is set
• videoMetadata.title and videoMetadata.channel are non-empty
• wordCount > 0
• transcriptQuality is set

"Phase 1 complete. {wordCount} words loaded from {transcriptSource}. Proceeding to config..."

---

PHASE 2: INTERACTIVE CONFIG

BLOCKING GATE 2

PRE-CONDITIONS:
  - transcriptPath exists and is readable
  - videoMetadata is populated (title + channel at minimum)
MANDATORY OUTPUTS:
  - category: string            # business, finance, technology, etc.
  - format: string              # tutorial | course | finance | interview | lecture | general
  - outputSelection: string[]   # Selected output types
  - depth: string               # "quick" | "standard" | "deep"
  - focusArea: string           # Format-specific focus (or "none")
  - repoUrl: string | null      # GitHub repo URL (tutorials only)
  - confidence: number          # Detection confidence percentage
  - mode: string                # "document" | "chat" — see Q5 (or pre-set by flag)

Q1: Content Type Confirmation

Detection: Run the content-type detector with metadata extracted in Phase 1:

bun run ${CLAUDE_PLUGIN_ROOT}/scripts/detect-content-type.ts --url "URL"
# OR pipe metadata JSON
echo '{"title":"...","description":"...","tags":[...]}' | bun run ${CLAUDE_PLUGIN_ROOT}/scripts/detect-content-type.ts --json

If confidence < 60%, present alternatives. See content-types.md for full keyword lists and scoring.

{
  "questions": [{
    "question": "I detected this as {category}/{format} ({X}% confidence). Is that correct?",
    "header": "Content type",
    "options": [
      {"label": "Yes, proceed", "description": "Use {category}/{format} as detected"},
      {"label": "Change category", "description": "Keep {format} format, pick a different category"},
      {"label": "Change format", "description": "Keep {category} category, pick a different format"},
      {"label": "Change both", "description": "Select both category and format manually"
    
     
  

Q2: Output Selection (multiSelect: true)

The available options depend on format:

tutorial / course: Detailed Summary · Production Checklist · Tool/Package Inventory · Key Quotes finance: Strategy Breakdown · Action Items · Risk Analysis · Key Quotes general / interview / lecture: Detailed Summary · Deep Analysis · Key Insights · Key Quotes

Default if user says "all" or doesn't specify: Detailed Summary + Key Quotes.

Q3: Output Depth

A) Quick (~500w) · B) Standard (~1500w, recommended) · C) Deep dive (~3000w+)

Q4: Format-Specific

• Tutorial / Course: "GitHub repo for this tutorial? Paste URL or skip." (stored as repoUrl)
• Finance: "Actionable takeaways or theoretical analysis?"
• General / Interview / Lecture: "Any specific angle to emphasize?"

Q5: Delivery Mode

SKIP this question entirely if mode was pre-set by an invocation flag. Otherwise:

{
  "questions": [{
    "question": "How should the analysis be delivered?",
    "header": "Delivery",
    "options": [
      {"label": "Save to disk", "description": "Default — write a permanent markdown file at the resolved output directory. Best when you want to reference this later."},
      {"label": "Discuss in chat", "description": "Run the full analysis but return the rendered markdown inline so we can talk through it. No file is created."}
    ],
    "multiSelect": false
  }]
}

Map: Save to disk → mode = "document"; Discuss in chat → mode = "chat". Default: mode = "document".

Gate 2 checklist (verify ALL):

• category is a valid category from content-types.md
• format is one of: tutorial, course, finance, interview, lecture, general
• outputSelection has ≥1 item
• depth is set
• focusArea is set (can be "none")
• repoUrl is a string or null
• mode is "document" or "chat"

"Phase 2 complete. {category}/{format} at {confidence}% confidence. Depth: {depth}. Outputs: {outputSelection}. Mode: {mode}. Proceeding to scaling..."

---

PHASE 3: MULTI-AGENT SCALING + REPO EXPLORATION

BLOCKING GATE 3

PRE-CONDITIONS:
  - category, format, outputSelection, depth all set
  - transcriptPath exists
MANDATORY OUTPUTS:
  - partitionStrategy: string         # "single" | "multi"
  - agentCount: number                # 1 for single, 2+ for multi
  - chunkPaths: string[]              # Chunk file paths
  - repoExploreResults: object | null # Mermaid diagrams from repo exploration

Mechanics: Read references/scaling-and-repo-explore.md. It covers token estimation, the scaling decision table (≤30K / 30–100K / >100K), partition execution, and the 3-explorer parallel repo exploration (StructureExplorer, DependencyExplorer, PatternExplorer — all subagent_type: Explore).

Trigger for repo exploration: repoUrl is non-null AND format is tutorial or course. Run in parallel with transcript partitioning.

Gate 3 checklist (verify ALL):

• partitionStrategy is set
• agentCount ≥ 1
• chunkPaths has ≥1 path; each path exists and is readable
• repoExploreResults is set (object or null)
• If repoUrl was provided, either repoExploreResults has content OR a skip reason is documented

"Phase 3 complete. {agentCount} agent(s) ready. {repoExploreResults ? 'Repo exploration complete with Mermaid diagrams.' : ''} Dispatching {format} workflow..."

---

PHASE 4: WORKFLOW DISPATCH + SYNTHESIS

BLOCKING GATE 4

PRE-CONDITIONS:
  - All Gate 3 outputs satisfied
  - category, format, outputSelection, depth, focusArea, mode all set
MANDATORY OUTPUTS:
  - workflowLoaded: string                # Workflow file that was loaded
  - outputPath: string | null             # Canonical output path (null when mode == "chat")
  - renderedMarkdown: string | null       # Rendered markdown (populated when mode == "chat")
  - copyPath: string | null               # Optional cwd copy path from Step 4.7 (null if declined or chat mode)

Final-phase reporting (branch on mode):

If mode == "document" AND copyPath is set: "Analysis complete. Saved to {outputPath}. Copy also at {copyPath}." If mode == "document" AND copyPath is null: "Analysis complete. Saved to {outputPath}." If mode == "chat": "Analysis complete — rendered below for discussion. No file written." Then print renderedMarkdown inline so it enters the conversation context.

---

Step 4.1: Dispatch

Dispatch table:

Format	Workflow File	Agent Description	Agent Type	Model
tutorial	workflows/tutorial-workflow.md	TutorialAgent	general-purpose	sonnet
course	workflows/tutorial-workflow.md	TutorialAgent	general-purpose	sonnet
finance	workflows/finance-workflow.md	FinanceAgent	general-purpose	sonnet
interview	workflows/general-workflow.md	GeneralAgent	general-purpose	sonnet
lecture	workflows/general-workflow.md	GeneralAgent	general-purpose	sonnet
general	workflows/general-workflow.md	GeneralAgent	general-purpose	sonnet

Why general-purpose on sonnet: Workflow agents follow precise file instructions to extract structured data and produce markdown. general-purpose executes workflow instructions directly without loading specialized agent personalities.

Pre-dispatch verification:

1. Format maps to a valid workflow file in the table above
2. Read the workflow file contents
3. Agent prompt includes the full workflow file
4. Agent receives: chunk path + user config + video metadata
5. subagent_type: "general-purpose" and model: "sonnet" set explicitly

Step 4.2: Launch Workflow Agents

Per chunk, launch a Task agent with subagent_type: "general-purpose", model: "sonnet". Each agent receives:

1. Its chunk file path (or full transcript if single agent)
2. User config: { category, format, outputSelection, depth, focusArea }
3. Video metadata: { title, channel, duration, upload_date, video_id }
4. Workflow instructions (full content of the appropriate workflows/*.md)
5. Instruction: "Analyze ONLY your assigned chunk. Do not read beyond your assigned content."

Parallel: if agentCount > 1, launch ALL chunk agents in parallel (single message, multiple Task calls).

Step 4.3: Synthesis

Launch ONE synthesis agent (general-purpose, sonnet) with a fresh context. Pass it references/output-templates.md (YAML frontmatter, Production Checklist section, Ground Truth Architecture section, Package Version table) so it can apply the templates without polluting the orchestrator context.

Synthesis agent receives:

1. Merged chunk analysis results
2. User config (including mode)
3. Video metadata
4. repoExploreResults if non-null (Mermaid diagrams)
5. Target output path — only if mode == "document"; pass null for chat
6. Contents of references/output-templates.md

Synthesis agent does:

1. Merge + deduplicate chunk analyses
2. Apply output template based on format + outputSelection
3. Generate YAML frontmatter
4. If tutorial + repoExploreResults: add Ground Truth Architecture section with Mermaid diagrams
5. Tutorials: extract package list for the package database (always — runs regardless of mode)
6. Branch on mode:
   • mode == "document" → write file to output path. Return { outputPath, renderedMarkdown: null, wordCount, packagesFound[] }
   • mode == "chat" → skip file write. Return { outputPath: null, renderedMarkdown: <full markdown including frontmatter>, wordCount, packagesFound[] }. Orchestrator prints renderedMarkdown inline.

Step 4.4: Package DB Integration (Tutorials Only)

For each package found:

bun run ${CLAUDE_PLUGIN_ROOT}/scripts/package-db.ts add \
  --name "{packageName}" --display-name "{displayName}" \
  --version-mentioned "{version}" --category "{packageCategory}" \
  --source "{videoUrl}"

The package database lives at ~/.config/youtube-analyzer/package-db.json (created on first use).

Version lookup (post-analysis, batches of 5; skip packages checked within 7 days):

• npm: WebFetch on https://registry.npmjs.org/{pkg}/latest
• PyPI: WebFetch on https://pypi.org/pypi/{pkg}/json
• Other: WebSearch

Update the package database with --latest-version. Status thresholds and the Package Version table format live in references/output-templates.md. Schema details: package-database-schema.md.

Step 4.5: Output Path Resolution

Skip this step entirely when mode == "chat" — set outputPath = null and continue to cleanup.

For mode == "document": read references/output-paths.md. The skill resolves the output directory from ${CLAUDE_PROJECT_DIR}/.claude/youtube-analyzer.local.md if present, otherwise prompts the user once via AskUserQuestion and persists the answer there for next time.

Step 4.6: Cleanup

rm -rf "{scratchpad}/repo-explore/"

Step 4.7: Optional Copy to Invocation Cwd

Skip when mode == "chat" — no file exists to copy. Set copyPath = null.

After the canonical file is written and cleanup is done, ask the user whether to also drop a copy in the cwd captured at skill invocation. Use AskUserQuestion:

{
  "questions": [{
    "question": "Also save a copy to the project you ran this from? Cwd: {invocationCwd}",
    "header": "Copy to repo",
    "options": [
      {"label": "No, just the configured output dir", "description": "File stays at {outputPath} only. No copy created."},
      {"label": "Yes, copy to cwd root", "description": "Drop a copy at {invocationCwd}/{filename}"},
      {"label": "Yes, custom subpath", "description": "I'll specify a subdirectory under {invocationCwd}"}
    ],
    "multiSelect": false
  }]
}

Branch on response:

• No → copyPath = null. Done.
• Yes, copy to cwd root → targetDir = invocationCwd. Proceed to copy.
• Yes, custom subpath → ask one follow-up free-text question: "Subpath under {invocationCwd} (e.g., docs/research, leave blank for cwd root):". Resolve targetDir = invocationCwd + (subpath || ""). Proceed to copy.

Copy execution:

mkdir -p "{targetDir}"
cp "{outputPath}" "{targetDir}/{filename}"

Set copyPath = "{targetDir}/{filename}".

Safety guards:

• If invocationCwd is unset (rare — only if cwd capture failed), skip the prompt and set copyPath = null.
• If invocationCwd equals the configured output directory, skip the prompt — copying onto itself is a no-op.
• If the destination file already exists, append -2, -3, etc. to the filename rather than overwriting.

---

Example

Input: "Analyze this video: https://youtube.com/watch?v=xyz123"

• Phase 1: Auto-detect URL → yt-dlp metadata → youtube_transcript_api transcript → 12K words.
• Phase 2: Detect finance/finance at 87%. User confirms. Selects: Strategy Breakdown + Action Items. Depth: standard. Focus: actionable takeaways.
• Phase 3: 12K tokens → single agent. No repo exploration.
• Phase 4: 1 FinanceAgent analyzes full transcript. Synthesis writes to {configured-output-dir}/2026-04-27-dividend-portfolio-strategy.md. Step 4.7 then asks whether to copy a duplicate into invocationCwd.

For --chat invocation, Phase 4 returns renderedMarkdown inline, skips Steps 4.5 and 4.7, and never writes a file.

---

Quick Reference

Scripts (in ${CLAUDE_PLUGIN_ROOT}/scripts/): clean-transcript.ts, detect-content-type.ts, partition-transcript.ts, package-db.ts · External: yt-dlp, youtube_transcript_api, bun

Agent types:

• Workflow agents: general-purpose + sonnet, given full workflow file content
• Repo explorers: Explore (Structure / Dependency / Pattern)
• Synthesizer: general-purpose + sonnet, given merged results + references/output-templates.md

Workflow files: workflows/{tutorial,finance,general,repo-exploration}-workflow.md

Sequence: verify external tools → Phase 1 (Gate 1) → Phase 2 (Gate 2) → Phase 3 (Gate 3) → Phase 4 (Gate 4) → report. No phase may be skipped.