Nlm Skill

NotebookLM CLI & MCP Expert

This skill provides comprehensive guidance for using NotebookLM via both the nlm CLI and MCP tools.

Tool Detection (CRITICAL - Read First!)

ALWAYS check which tools are available before proceeding:

1. Check for MCP tools: Look for tools starting with mcp__notebooklm-mcp__* or mcp_notebooklm_*
2. If BOTH MCP tools AND CLI are available: ASK the user which they prefer to use before proceeding
3. If only MCP tools are available: Use them directly (refer to tool docstrings for parameters)
4. If only CLI is available: Use nlm CLI commands via Bash

Decision Logic:

has_mcp_tools = check_available_tools()  # Look for mcp__notebooklm-mcp__* or mcp_notebooklm_*
has_cli = check_bash_available()  # Can run nlm commands

if has_mcp_tools and has_cli:
    # ASK USER: "I can use either MCP tools or the nlm CLI. Which do you prefer?"
    user_preference = ask_user()
else if has_mcp_tools:
    # Use MCP tools directly
    mcp__notebooklm-mcp__notebook_list()
else:
    # Use CLI via Bash
    bash("nlm notebook list")

This skill documents BOTH approaches. Choose the appropriate one based on tool availability and user preference.

Quick Reference

Run nlm --ai to get comprehensive AI-optimized documentation - this provides a complete view of all CLI capabilities.

nlm --help              # List all commands
nlm <command> --help    # Help for specific command
nlm --ai                # Full AI-optimized documentation (RECOMMENDED)
nlm --version           # Check installed version

Critical Rules (Read First!)

1. Always authenticate first: Run nlm login before any operations
2. Sessions expire in ~20 minutes: Re-run nlm login if commands start failing
3. ⚠️ ALWAYS ASK USER BEFORE DELETE: Before executing ANY delete command, ask the user for explicit confirmation. Deletions are irreversible. Show what will be deleted and warn about permanent data loss.
4. --confirm is REQUIRED: All generation and delete commands need --confirm or -y (CLI) or confirm=True (MCP)
5. Research requires --notebook-id: The flag is mandatory, not positional
6. Capture IDs from output: Create/start commands return IDs needed for subsequent operations
7. Use aliases: Simplify long UUIDs with nlm alias set <name> <uuid>
8. Check aliases before creating: Run nlm alias list before creating a new alias to avoid conflicts with existing names.
9. DO NOT launch REPL: Never use nlm chat start - it opens an interactive REPL that AI tools cannot control. Use nlm notebook query for one-shot Q&A instead.
10. Choose output format wisely: Default output (no flags) is compact and token-efficient—use it for status checks. Use --quiet to capture IDs for piping. Only use --json when you need to parse specific fields programmatically.
11. Use --help when unsure: Run nlm <command> --help to see available options and flags for any command.

Workflow Decision Tree

Use this to determine the right sequence of commands:

User wants to...
│
├─► Work with NotebookLM for the first time
│   └─► nlm login → nlm notebook create "Title"
│
├─► Add content to a notebook
│   ├─► From a URL/webpage → nlm source add <nb-id> --url "https://..."
│   ├─► From YouTube → nlm source add <nb-id> --url "https://youtube.com/..."
│   ├─► From pasted text → nlm source add <nb-id> --text "content" --title "Title"
│   ├─► From Google Drive → nlm source add <nb-id> --drive <doc-id> --type doc
│   └─► Discover new sources → nlm research start "query" --notebook-id <nb-id>
│
├─► Generate content from sources
│   ├─► Podcast/Audio → nlm audio create <nb-id> --confirm
│   ├─► Written summary → nlm report create <nb-id> --confirm
│   ├─► Study materials → nlm quiz/flashcards create <nb-id> --confirm
│   ├─► Visual content → nlm mindmap/slides/infographic create <nb-id> --confirm
│   ├─► Video → nlm video create <nb-id> --confirm
│   └─► Extract data → nlm data-table create <nb-id> "description" --confirm
│
├─► Ask questions about sources
│   └─► nlm notebook query <nb-id> "question"
│       (Use --conversation-id for follow-ups)
│       ⚠️ Do NOT use `nlm chat start` - it's a REPL for humans only
│
├─► Check generation status
│   └─► nlm studio status <nb-id>
│
└─► Manage/cleanup
    ├─► List notebooks → nlm notebook list
    ├─► List sources → nlm source list <nb-id>
    ├─► Delete source → nlm source delete <source-id> --confirm
    └─► Delete notebook → nlm notebook delete <nb-id> --confirm

Command Categories

1. Authentication

MCP Authentication

If using MCP tools and encountering authentication errors:

# Run the CLI authentication (works for both CLI and MCP)
nlm login

# Then reload tokens in MCP
mcp__notebooklm-mcp__refresh_auth()
# Returns status: "success" (valid), "expired" (tokens dead, run `nlm login`),
# or "error". `nlm login` is the only recovery path for "expired".

Or manually save cookies via MCP (fallback):

# Extract cookies from Chrome DevTools and save
mcp__notebooklm-mcp__save_auth_tokens(cookies="<cookie_header>")

#### CLI Authentication

```bash
nlm login                           # Launch browser, extract cookies (primary method)
nlm login --check                   # Validate current session
nlm login --profile work            # Use named profile for multiple accounts
nlm login --provider openclaw --cdp-url http://127.0.0.1:18800  # External CDP provider
nlm login switch <profile>          # Switch the default profile
nlm login profile list              # List all profiles with email addresses
nlm login profile delete <name>     # Delete a profile
nlm login profile rename <old> <new> # Rename a profile

Multi-Profile Support: Each profile gets its own isolated browser session (supports Chrome, Arc, Brave, Edge, Chromium, and more), so you can be logged into multiple Google accounts simultaneously.

Session lifetime: ~20 minutes. Re-authenticate when commands fail with auth errors.

Switching MCP Accounts: The MCP server always uses the active default profile. If you need to switch which Google account the MCP server is communicating with, you MUST use the CLI: run nlm login switch <name>. Your next MCP tool call will instantly use the new account.

Note: Both MCP and CLI share the same authentication backend, so authenticating with one works for both.

2. Notebook Management

MCP Tools

Use tools: notebook_list, notebook_create, notebook_get, notebook_describe, notebook_query, notebook_rename, notebook_delete. All accept notebook_id parameter. Delete requires confirm=True.

CLI Commands

nlm notebook list                      # List all notebooks
nlm notebook list --json               # JSON output for parsing
nlm notebook list --quiet              # IDs only (for scripting)
nlm notebook create "Title"            # Create notebook, returns ID
nlm notebook get <id>                  # Get notebook details
nlm notebook describe <id>             # AI-generated summary + suggested topics
nlm notebook query <id> "question"     # One-shot Q&A with sources
nlm notebook rename <id> "New Title"   # Rename notebook
nlm notebook delete <id> --confirm     # PERMANENT deletion

3. Source Management

MCP Tools

Use source_add with these source_type values:

• url - Web page or YouTube URL (url param)
• text - Pasted content (text + title params)
• file - Local file upload (file_path param). Supported extensions: PDF, TXT, MD, DOCX, CSV, EPUB, MP3, M4A, WAV, AAC, OGG, OPUS, MP4, JPG, JPEG, PNG, GIF, WEBP. Note: Image-bearing sources (PDF / JPG / PNG / etc.) feed Studio video generation's visual-crop pipeline — charts, photos, and diagrams may be extracted as on-screen aids in Video Overviews.
• drive - Google Drive doc (document_id + doc_type params)

Other tools: source_list_drive, source_describe, source_get_content, source_rename, source_sync_drive (requires confirm=True), source_delete (requires confirm=True).

CLI Commands

# Adding sources
nlm source add <nb-id> --url "https://..."           # Web page
nlm source add <nb-id> --url "https://youtube.com/..." # YouTube video
nlm source add <nb-id> --text "content" --title "X"  # Pasted text
nlm source add <nb-id> --drive <doc-id>              # Drive doc (auto-detect type)
nlm source add <nb-id> --drive <doc-id> --type slides # Explicit type
nlm source add <nb-id> --file "/path/to/diagram.png" --wait # Local file upload (images, PDFs, documents, audio, video)

# Listing and viewing
nlm source list <nb-id>                # Table of sources
nlm source list <nb-id> --drive        # Show Drive sources with freshness
nlm source list <nb-id> --drive -S     # Skip freshness checks (faster)
nlm source get <source-id>             # Source metadata
nlm source describe <source-id>        # AI summary + keywords
nlm source content <source-id>         # Raw text content
nlm source content <source-id> -o file.txt  # Export to file

# Drive sync (for stale sources)
nlm source stale <nb-id>               # List outdated Drive sources
nlm source sync <nb-id> --confirm      # Sync all stale sources
nlm source sync <nb-id> --source-ids <ids> --confirm  # Sync specific

# Rename
nlm source rename <source-id> "New Title" --notebook <nb-id>
nlm rename source <source-id> "New Title" --notebook <nb-id>  # verb-first

# Deletion
nlm source delete <source-id> --confirm

Drive types: doc, slides, sheets, pdf

4. Research (Source Discovery)

Research finds NEW sources from the web or Google Drive.

MCP Tools

Use research_start with:

• source: web or drive
• mode: fast (30s) or deep (5min, web only)

Workflow: research_start → poll research_status → research_import

CLI Commands

# Start research (--notebook-id is REQUIRED)
nlm research start "query" --notebook-id <id>              # Fast web (~30s)
nlm research start "query" --notebook-id <id> --mode deep  # Deep web (~5min)
nlm research start "query" --notebook-id <id> --source drive  # Drive search

# Check progress
nlm research status <nb-id>                   # Poll until done (5min max)
nlm research status <nb-id> --max-wait 0      # Single check, no waiting
nlm research status <nb-id> --task-id <tid>   # Check specific task
nlm research status <nb-id> --full            # Full details

# Import discovered sources
nlm research import <nb-id> <task-id>            # Import all
nlm research import <nb-id> <task-id> --indices 0,2,5  # Import specific
nlm research import <nb-id> <task-id> --cited-only      # Import cited sources
nlm research import <nb-id> <task-id> --timeout 600    # Custom timeout (default: 300s)

Modes: fast (30s, ~10 sources) | deep (5min, ~40+ sources, web only)

5. Content Generation (Studio)

MCP Tools (Unified Creation)

Use studio_create with artifact_type and type-specific options. All require confirm=True. studio_create runs a pre-flight auth check before firing the request, so stale auth fails immediately with an nlm login hint instead of returning a fake success that collapses seconds later.

Common options: source_ids, language (BCP-47 code), focus_prompt

Revise Slides: Use studio_revise to revise individual slides in an existing slide deck.

• Requires artifact_id (from studio_status) and slide_instructions
• Creates a NEW artifact — the original is not modified
• Slide numbers are 1-based (slide 1 = first slide)
• Poll studio_status after calling to check when the new deck is ready

CLI Commands

All generation commands share these flags:

• --confirm or -y: REQUIRED to execute
• --source-ids <id1,id2>: Limit to specific sources
• --language <code>: BCP-47 code (en, es, fr, de, ja)

# Audio (Podcast)
nlm audio create <id> --confirm
nlm audio create <id> --format deep_dive --length default --confirm
nlm audio create <id> --format brief --focus "key topic" --confirm
# Formats: deep_dive, brief, critique, debate
# Lengths: short, default, long

# Report
nlm report create <id> --confirm
nlm report create <id> --format "Study Guide" --confirm
nlm report create <id> --format "Create Your Own" --prompt "Custom..." --confirm
# Formats: "Briefing Doc", "Study Guide", "Blog Post", "Create Your Own"

# Quiz
nlm quiz create <id> --confirm
nlm quiz create <id> --count 5 --difficulty 3 --confirm
nlm quiz create <id> --count 10 --difficulty 3 --focus "Focus on key concepts" --confirm
# Count: number of questions (default: 2)
# Difficulty: 1-5 (1=easy, 5=hard)
# Focus: optional text to guide quiz generation

# Flashcards
nlm flashcards create <id> --confirm
nlm flashcards create <id> --difficulty hard --confirm
nlm flashcards create <id> --difficulty medium --focus "Focus on definitions" --confirm
# Difficulty: easy, medium, hard
# Focus: optional text to guide flashcard generation

# Mind Map
nlm mindmap create <id> --confirm
nlm mindmap create <id> --title "Topic Overview" --confirm
nlm mindmap list <id>  # List existing mind maps

# Slides
nlm slides create <id> --confirm
nlm slides create <id> --format presenter --length short --confirm
# Formats: detailed, presenter | Lengths: short, default
nlm slides revise <artifact-id> --slide '1 Make the title larger' --confirm
# Each --slide value must be: '<slide-number> <instruction>'
# Creates a NEW deck with revisions. Original unchanged.

# Infographic
nlm infographic create <id> --confirm
nlm infographic create <id> --orientation portrait --detail detailed --style professional --confirm
# Orientations: landscape, portrait, square
# Detail: concise, standard, detailed
# Styles: auto_select, sketch_note, professional, bento_grid, editorial, instructional, bricks, clay, anime, kawaii, scientific

# Video
nlm video create <id> --confirm
nlm video create <id> --format brief --style whiteboard --confirm
# Formats: explainer, brief
# Styles: auto_select, classic, whiteboard, kawaii, anime, watercolor, retro_print, heritage, paper_craft

# Data Table
nlm data-table create <id> "Extract all dates and events" --confirm
# DESCRIPTION is required as second argument

6. Studio (Artifact Management)

MCP Tools

Use studio_status to check progress (or rename with action="rename"). For failed artifacts, studio_status returns a non-null error_reason field so callers know why without re-running. Use download_artifact with artifact_type and output_path. Use export_artifact with export_type: docs/sheets. Delete with studio_delete (requires confirm=True).

CLI Commands

# Check status
nlm studio status <nb-id>                          # List all artifacts
nlm studio status <nb-id> --full                   # Show full details (including custom prompts)
nlm studio status <nb-id> --json                   # JSON output

# Download artifacts
nlm download audio <nb-id> --output podcast.mp3
nlm download video <nb-id> --output video.mp4
nlm download report <nb-id> --output report.md
nlm download slide-deck <nb-id> --output slides.pdf           # PDF (default)
nlm download slide-deck <nb-id> --output slides.pptx --format pptx  # PPTX
nlm download quiz <nb-id> --output quiz.json --format json

# Export to Google Docs/Sheets
nlm export sheets <nb-id> <artifact-id> --title "My Data Table"
nlm export docs <nb-id> <artifact-id> --title "My Report"

# Delete artifact
nlm studio delete <nb-id> <artifact-id> --confirm

Status values: completed (✓), in_progress (●), failed (✗)

Prompt Extraction: The studio_status tool returns a custom_instructions field for each artifact. This contains the original focus prompt or custom instructions used to generate that artifact (e.g., the prompt for a "Create Your Own" report, or the focus topic for an Audio Overview). This is useful for retrieving the exact prompt that generated a successful artifact.

Renaming Resources

Rename a Source

MCP Tool: source_rename(notebook_id, source_id, new_title)

CLI:

nlm source rename <source-id> "New Title" --notebook <notebook-id>
nlm rename source <source-id> "New Title" --notebook <notebook-id>  # verb-first

Rename a Studio Artifact

MCP Tools

Use studio_status with action="rename", artifact_id, and new_title.

CLI Commands

nlm studio rename <artifact-id> "New Title"
nlm rename studio <artifact-id> "New Title"  # verb-first alternative

Server Info (Version Check)

MCP Tools

Use server_info to get version and check for updates:

mcp__notebooklm-mcp__server_info()
# Returns: version, latest_version, update_available, update_command

CLI Commands

nlm --version  # Shows version and update availability

7. Chat Configuration and Notes

MCP Tools

Use chat_configure with goal: default/learning_guide/custom. Use note with action: create/list/update/delete. Delete requires confirm=True.

CLI Commands

⚠️ AI TOOLS: DO NOT USE nlm chat start - It launches an interactive REPL that cannot be controlled programmatically. Use nlm notebook query for one-shot Q&A instead.

For human users at a terminal:

nlm chat start <nb-id>  # Launch interactive REPL

REPL Commands:

• /sources - List available sources
• /clear - Reset conversation context
• /help - Show commands
• /exit - Exit REPL

Configure chat behavior (works for both REPL and query):

nlm chat configure <id> --goal default
nlm chat configure <id> --goal learning_guide
nlm chat configure <id> --goal custom --prompt "Act as a tutor..."
nlm chat configure <id> --response-length longer  # longer, default, shorter

Notes management:

nlm note create <nb-id> "Content" --title "Title"
nlm note list <nb-id>
nlm note update <nb-id> <note-id> --content "New content"
nlm note delete <nb-id> <note-id> --confirm

8. Notebook Sharing

MCP Tools

Use notebook_share_status to check, notebook_share_public to enable/disable public link, notebook_share_invite with email and role: viewer/editor.

CLI Commands

# Check sharing status
nlm share status <nb-id>

# Enable/disable public link
nlm share public <nb-id>          # Enable
nlm share public <nb-id> --off    # Disable

# Invite collaborator
nlm share invite <nb-id> user@example.com
nlm share invite <nb-id> user@example.com --role editor

9. Aliases (UUID Shortcuts)

Simplify long UUIDs:

nlm alias set myproject abc123-def456...  # Create alias (auto-detects notebook/source)
nlm alias get myproject                    # Resolve to UUID
nlm alias list                             # List all aliases
nlm alias delete myproject                 # Remove alias

# Use aliases anywhere
nlm notebook get myproject
nlm source list myproject
nlm audio create myproject --confirm

10. Configuration

CLI-only commands for managing settings:

nlm config show                              # Show current config
nlm config get <key>                         # Get specific setting
nlm config set <key> <value>                 # Update setting
nlm config set output.format json            # Change default output

# For switching profiles, prefer the simpler command:
nlm login switch work                        # Switch default profile

Available Settings:

11. Skill Management

Manage the NotebookLM skill installation for various AI assistants:

nlm skill list                              # Show installation status
nlm skill update                            # Update all outdated skills
nlm skill update <tool>                     # Update specific skill (e.g., claude-code)
nlm skill install <tool>                    # Install skill
nlm skill uninstall <tool>                  # Uninstall skill

Verb-first aliases: nlm update skill, nlm list skills, nlm install skill

Output Formats

Most list commands support multiple formats:

12. Batch Operations

Perform the same action across multiple notebooks at once.

MCP Tools

Use batch with action parameter. Select notebooks by notebook_names, tags, or all=True.

batch(action="query", query="What are the key findings?", notebook_names="AI Research, Dev Tools")
batch(action="add_source", source_url="https://example.com", tags="ai,research")
batch(action="create", titles="Project A, Project B, Project C")
batch(action="delete", notebook_names="Old Project", confirm=True)
batch(action="studio", artifact_type="audio", tags="research", confirm=True)

CLI Commands

nlm batch query "What are the key takeaways?" --notebooks "id1,id2"
nlm batch query "Summarize" --tags "ai,research"      # Query by tag
nlm batch query "Summarize" --all                      # Query ALL notebooks
nlm batch add-source --url "https://..." --notebooks "id1,id2"
nlm batch create "Project A, Project B, Project C"     # Create multiple
nlm batch delete --notebooks "id1,id2" --confirm       # Delete multiple
nlm batch studio --type audio --tags "research" --confirm  # Generate across notebooks

13. Cross-Notebook Query

Query multiple notebooks and get aggregated answers with per-notebook citations.

MCP Tools

cross_notebook_query(query="Compare approaches", notebook_names="Notebook A, Notebook B")
cross_notebook_query(query="Summarize", tags="ai,research")
cross_notebook_query(query="Everything", all=True)

CLI Commands

nlm cross query "What features are discussed?" --notebooks "id1,id2"
nlm cross query "Compare approaches" --tags "ai,research"
nlm cross query "Summarize everything" --all

14. Pipelines

Define and execute multi-step notebook workflows. Three built-in pipelines plus support for custom YAML pipelines.

MCP Tools

pipeline(action="list")  # List available pipelines
pipeline(action="run", notebook_id="...", pipeline_name="ingest-and-podcast", input_url="https://...")

CLI Commands

nlm pipeline list                                         # List available pipelines
nlm pipeline run <notebook> ingest-and-podcast --url "https://..."
nlm pipeline run <notebook> research-and-report --url "https://..."
nlm pipeline run <notebook> multi-format                  # Audio + report + flashcards

Built-in pipelines: ingest-and-podcast, research-and-report, multi-format

Create custom pipelines: add YAML files to ~/.notebooklm-mcp-cli/pipelines/

15. Tags & Smart Select

Tag notebooks for organization and use tags to target batch operations.

MCP Tools

tag(action="add", notebook_id="...", tags="ai,research,llm")
tag(action="remove", notebook_id="...", tags="ai")
tag(action="list")                           # List all tagged notebooks
tag(action="select", query="ai research")    # Find notebooks by tag match

CLI Commands

nlm tag add <notebook> --tags "ai,research,llm"           # Add tags
nlm tag add <notebook> --tags "ai" --title "My Notebook"  # With display title
nlm tag remove <notebook> --tags "ai"                     # Remove tags
nlm tag list                                              # List all tagged notebooks
nlm tag select "ai research"                              # Find notebooks by tag match

16. Long-Lived MCP Server Configuration

The MCP server runs as a long-lived process. For 24/7 deployments (e.g. an always-on assistant), a few knobs help bound memory and tune behavior.

Conversation cache bounds (added in 0.6.14)

The in-process conversation history cache used to grow without bound, eventually OOM'ing the host on always-on servers. Three env-var knobs cap memory. Set any to 0 to disable that specific cap and restore the old unbounded behavior:

With all defaults: 500 convs × 50 turns × up to 100k chars = hard upper bound around ~2.5 GB of answer text. In practice answers are 1–10 KB, so the typical ceiling is ~25 MB.

Negative values are clamped to 0 (unlimited) with a warning. Invalid values fall back to the default with a warning.

Cache stats (added in 0.6.14)

For monitoring from Python, the BaseClient exposes get_conversation_cache_stats() which returns:

{
    "conversations": int,            # current number of cached conversations
    "total_turns": int,              # current number of cached turns across all convs
    "max_turns_per_conversation": int,
    "max_conversations": int,
    "max_chars_per_turn": int,
}

There's no MCP or CLI tool wrapper in 0.6.14. Call it directly from Python if you need to surface cache pressure in your own tooling.

Server startup flags (notebooklm-mcp)

When starting the MCP server directly, two flags control transport-layer behavior. Neither affects the conversation cache above.

• --stateless / --no-stateless (default: true, env NOTEBOOKLM_MCP_STATELESS): Controls whether the MCP HTTP transport keeps per-session state. Leave it true unless you know you need sessions. The flag exists to work around an MCP SDK double-response crash (python-sdk#2416) and is unrelated to the conversation cache.
• --transport http / --transport stdio (default: stdio): Pick the transport. --transport http requires --port (default 8000).
• --host <addr> (default 127.0.0.1): Bind address for HTTP/SSE. Refuses external binds unless NOTEBOOKLM_ALLOW_EXTERNAL_BIND=1 is set.

Common Patterns

Pattern 1: Research → Podcast Pipeline

nlm notebook create "AI Research 2026"   # Capture ID
nlm alias set ai <notebook-id>
nlm research start "agentic AI trends" --notebook-id ai --mode deep
nlm research status ai --max-wait 300    # Wait up to 5 min
nlm research import ai <task-id>         # Import all sources
nlm audio create ai --format deep_dive --confirm
nlm studio status ai                     # Check generation progress

Pattern 2: Quick Content Ingestion

nlm source add <id> --url "https://example1.com"
nlm source add <id> --url "https://example2.com"
nlm source add <id> --text "My notes..." --title "Notes"
nlm source list <id>

Pattern 3: Study Materials Generation

nlm report create <id> --format "Study Guide" --confirm
nlm quiz create <id> --count 10 --difficulty 3 --focus "Exam prep" --confirm
nlm flashcards create <id> --difficulty medium --focus "Core terms" --confirm

Pattern 4: Drive Document Workflow

nlm source add <id> --drive 1KQH3eW0hMBp7WK... --type slides
# ... time passes, document is edited ...
nlm source stale <id>                    # Check freshness
nlm source sync <id> --confirm           # Sync if stale

Pattern 5: Batch & Cross-Notebook Workflow

# Tag notebooks for organization
nlm tag add <id1> --tags "ai,research"
nlm tag add <id2> --tags "ai,product"

# Query across tagged notebooks
nlm cross query "What are the main conclusions?" --tags "ai"

# Batch generate podcasts for all tagged notebooks
nlm batch studio --type audio --tags "ai" --confirm

# Run a pipeline on a single notebook
nlm pipeline run <id> ingest-and-podcast --url "https://example.com"

Error Recovery

Rate Limiting

Wait between operations to avoid rate limits:

• Source operations: 2 seconds
• Content generation: 5 seconds
• Research operations: 2 seconds
• Query operations: 2 seconds

Advanced Reference

For detailed information, see:

• references/command_reference.md: Complete command signatures
• references/troubleshooting.md: Detailed error handling
• references/workflows.md: End-to-end task sequences