notebooklm-skill-factory

name

notebooklm-skill-factory

description

Orchestrate NotebookLM research into SKILL.md generation and Claude Code validation in a single automated pipeline. Use when user asks to create a new Claude Code skill from source materials (PDFs, articles, YouTube, URLs), batch-produce skills, or convert domain knowledge into reusable skills. Trigger phrases -- create a skill for X, make me a skill that does X, turn this into a skill, generate a skill from these sources, build a skill factory pipeline. NOT for manual skill editing (use skill-creator) or NotebookLM-only tasks (use notebooklm directly).

NotebookLM Skill Factory

Orchestrate the full pipeline: NotebookLM source ingestion → structured SKILL.md extraction → write to skills directory → validate → test → iterate.

Prerequisites Check

Before the pipeline, verify NotebookLM is authenticated:

notebooklm status || notebooklm login

If notebooklm login opens a browser, tell user to complete Google OAuth and press ENTER in their terminal.

Pipeline

Phase 1: Setup & Source Ingestion

1. Create a dedicated notebook for this skill:

   notebooklm create "Skill: {skill-name}" --json
   

   Parse the notebook id from JSON output.

2. Collect sources from user. Ask:

   "What sources should this skill be based on? Give me URLs, local file paths, YouTube links — anything high-quality and specific to this domain."

   If the user doesn't have sources ready, suggest:

   • Official documentation URLs
   • Well-known books/articles on the topic (user can provide PDFs)
   • YouTube tutorials/channels specializing in the area

3. Add sources (use -n <notebook_id> for all subsequent commands):

   notebooklm source add "https://..." -n <id> --json
   notebooklm source add ./local-file.pdf -n <id> --json
   

   Capture each source_id.

4. Wait for indexing — spawn a background agent to avoid blocking:

   notebooklm source wait <source_id> -n <id> --timeout 600
   

   For multiple sources, wait them all. If any source fails indexing (exit code 1), log a warning but continue with remaining sources.

Phase 2: Extract SKILL.md

1. Load the extraction prompt template: Read references/skill-extraction-prompt.md. Replace {USER_INTENT} with the user's original request.

2. Query NotebookLM against all indexed sources:

   notebooklm ask "{extraction_prompt}" -n <id> --json
   

3. Parse the output into a clean SKILL.md:

   echo '<json_output>' | python3 scripts/parse-skill-output.py > /tmp/skill-output.md
   

   Or save the JSON to a temp file first, then pipe.

4. Validate basic structure:

   • Starts with --- YAML frontmatter
   • Has name: and description: fields
   • Body is non-empty after frontmatter If validation fails, re-prompt NotebookLM with more specific instructions.

Phase 3: Install & Validate

1. Create the skill directory:

   mkdir -p ~/.claude/skills/{skill-name}
   

2. Write the SKILL.md: Move the parsed output to ~/.claude/skills/{skill-name}/SKILL.md

3. Run skill-creator validation (via Skill tool): Invoke skill-creator with: "Validate the skill at ~/.claude/skills/{skill-name}/ and fix any issues found. Run package_skill.py to check for errors."

4. Run skill-vetter security check (via Skill tool): Invoke skill-vetter with: "Review the skill at ~/.claude/skills/{skill-name}/ for security issues."

Phase 4: Test & Iterate

1. Test the skill by invoking it with a realistic prompt matching its intended use case.

2. Collect failures:

   • Did the skill trigger correctly?
   • Did it produce correct output?
   • Any hallucinations or gaps?

3. If issues found, iterate:

   • Feed the failure description back to NotebookLM:

     notebooklm ask "The SKILL.md generated earlier has this issue: {failure}. Based on the sources, rewrite it to fix this. Output complete corrected SKILL.md in a markdown code block." -n <id> --json
     

   • Parse again and overwrite SKILL.md
   • Re-test

4. Repeat until the skill passes a real usage test.

Error Recovery

Situation	Action
NotebookLM auth expired	Run notebooklm login, retry
Source indexing failed	Skip that source, warn user, continue
Extraction prompt returned empty	Check if sources are indexed (status=ready), re-query with --new flag
Generated SKILL.md fails validation	Send failure description back to NB for rewrite (Phase 4 iteration)
NB rate limited	Wait 5-10 min, retry once

Source Quality Guidelines

Remind users:

• One notebook per skill, one theme per notebook
• 3-10 high-quality sources work better than 20+ mixed ones
• Official docs > blog posts > YouTube transcripts
• If the skill covers multiple unrelated domains, create separate notebooks for each
• After generation, user should be the final human reviewer — check tone, logic, completeness

Completing the Pipeline

When the skill passes testing, report:

• Skill name and path (~/.claude/skills/{name}/)
• Number of sources used
• Number of iteration rounds
• Final test result summary