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Notebook First retrieval policy — query NotebookLM notebooks before web search or context dumping
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SOC 職業分類に基づく
Route complex requests to the right specialist agent or chain of agents. This skill acts as the central brain of an agent swarm — it analyses what the user needs, determines which specialist domain(s) are required, and coordinates parallel or sequential agent execution. Use this skill when a request spans multiple domains (e.g., "research competitors and create a pitch deck"), when you need to decide which specialist should handle an ambiguous request, or when a task requires a multi-step pipeline across different skills. Triggers on: multi-step requests, cross-domain tasks, "coordinate", "plan this out", "I need help with multiple things", or any complex request that touches more than one specialist area. Also triggers when the user seems unsure which tool or approach to use.
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Hybrid DAG execution primitive combining deterministic and agentic nodes with hard iteration caps
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Act as a brand ambassador — create authentic, platform-specific social media content that embodies a brand's identity and connects with audiences. Use this skill whenever the user asks to "create social media content for a brand", "act as a brand ambassador", "write ambassador posts", "promote [brand] on social media", "create influencer-style content", "write authentic brand content", "social media ambassador", or any request involving representing a brand through social content. Also triggers on "ambassador voice", "brand promotion posts", "influencer content", "authentic brand posts", "UGC-style content", or when someone wants social media content that sounds like a real person recommending a brand rather than corporate marketing copy. Even if the user just says "help me promote [brand]" or "I need content for [brand]'s social channels" — use this skill.
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| id | notebooklm-second-brain |
| name | notebooklm-second-brain |
| type | skill |
| version | 1.0.0 |
| created | 20/03/2026 |
| modified | 20/03/2026 |
| status | active |
| category | meta-orchestration |
| tags | ["retrieval","knowledge-management","notebooklm","second-brain"] |
| triggers | ["onboarding","debugging","architecture","security","post-build"] |
| description | Notebook First retrieval policy — query NotebookLM notebooks before web search or context dumping |
| context | fork |
Policy: Query the relevant NotebookLM notebook before web search or dumping full docs into context.
This skill enforces a "Notebook First" retrieval policy. Instead of loading entire markdown files or running web searches for architecture decisions, debugging patterns, or security knowledge, the agent queries purpose-built NotebookLM notebooks via the nlm CLI. This keeps the agent context window lean and retrieval fast.
| Scenario | Notebook | Why |
|---|---|---|
| "How does auth work?" | project_sot | Architecture decisions live here |
| "Why is this test failing?" | debug_kb | Error patterns and debugging playbooks |
| "Is this input sanitised?" | security_handbook | OWASP patterns and security reviews |
| "Explain the codebase" | repo_onboarding | Codebase atlas and mind maps |
| After successful build/verify | project_sot | Auto-sync implementation notes |
All notebook IDs are stored in .claude/notebooklm/notebooks.json. Never hard-code or fabricate IDs.
# Read current notebook config
cat .claude/notebooklm/notebooks.json
| Query Type | Target Notebook | Fallback |
|---|---|---|
| Architecture, feature specs, implementation history | project_sot | Read CLAUDE.md directly |
| Error patterns, test failures, debugging steps | debug_kb | Search codebase with grep |
| OWASP, auth patterns, security reviews | security_handbook | Web search security docs |
| "How does X work?", codebase overview, onboarding | repo_onboarding | Read source files directly |
# Authentication
nlm login # Browser cookie extraction (one-time)
nlm login --check # Verify auth status
# Notebook management
nlm notebook list # List all notebooks
nlm notebook create "Name" # Create new notebook
nlm notebook query <notebook_id> # Interactive query
# Source management
nlm source add <notebook_id> --file <path> # Add file source
nlm source add <notebook_id> --url <url> # Add URL source
nlm source add <notebook_id> --text "content" # Add text source
nlm source list <notebook_id> # List sources
# Notes and audio
nlm note create <notebook_id> "Title" # Create note
nlm audio create <notebook_id> --confirm # Generate audio overview
When setting up a new notebook:
nlm notebook create "Name"notebooks.jsonAfter successful pnpm run verify or pnpm build, the sync hook (.claude/hooks/scripts/notebooklm-sync.ps1) automatically creates an implementation note in project_sot with:
notebooks.json (populated by /notebooklm-bootstrap)| System | Integration |
|---|---|
| Beads | Sync completed Beads tasks as implementation notes |
| Council of Logic | Architecture queries route through project_sot first |
| Hooks | Post-verify sync hook adds notes automatically |
| Skills | Other skills can query notebooks for domain knowledge |
Run the bootstrap command to install, authenticate, and create all notebooks:
/notebooklm-bootstrap
This installs notebooklm-mcp-cli, authenticates via browser, creates the 4 notebooks, seeds them with project sources, and writes IDs to notebooks.json.
python scripts/validate-notebooks.py # Schema validation
python scripts/validate-notebooks.py --check-ids # Verify IDs populated
python scripts/validate-notebooks.py --dry-run-sync # Preview sync note