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:

```bash

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

```

Parse the notebook id from JSON output.

1. 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

1. Add sources (use -n for all subsequent commands):

```bash

notebooklm source add "https://..." -n --json

notebooklm source add ./local-file.pdf -n --json

```

Capture each source_id.

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

```bash

notebooklm source wait -n --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.

1. Query NotebookLM against all indexed sources:

```bash

notebooklm ask "{extraction_prompt}" -n --json

```

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

```bash

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

```

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

1. 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:

```bash

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

```

1. Write the SKILL.md:

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

1. 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."

1. 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.

1. Collect failures:
• Did the skill trigger correctly?
• Did it produce correct output?
• Any hallucinations or gaps?

1. If issues found, iterate:
• Feed the failure description back to NotebookLM:

```bash

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 --json

```

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

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

Error Recovery

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