Skill: kidcvs/notebooklm-skill-factory (version 0.1.0)

--- 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: ```bash 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. 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): ```bash 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: ```bash 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](references/skill-extraction-prompt.md). Replace `{USER_INTENT}` with the user's original request. 2. **Query NotebookLM** against all indexed sources: ```bash notebooklm ask "{extraction_prompt}" -n <id> --json ``` 3. **Parse the output** into a clean SKILL.md: ```bash 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:** ```bash 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: ```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 <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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)