agent-management
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent",…
Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides,
$ npx -y skills add davila7/claude-code-templates --skill deep-research-notebooklm --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/deep-research-notebooklmContext preview
The summary Claude sees to decide when to auto-load this skill.
Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides,
name: deep-research-notebooklm description: "Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps)."
Research **$ARGUMENTS** deeply using the NotebookLM MCP server and deliver a structured research brief. Optionally generate studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps) from the research.
Determine the **research type** based on the user's request:
| Type | Focus | |------|-------| | **Market Research** | Industry trends, market sizing, opportunities, TAM/SAM/SOM | | **Competitive Intel** | Competitor analysis, positioning gaps, feature comparisons | | **Client/Prospect Research** | Company background, pain points, decision makers, recent news | | **Trend Analysis** | Technology trends, adoption patterns, forecasts, emerging players | | **Proposal Research** | Background for proposals, sector-specific data, case studies | | **Academic/Technical** | Papers, frameworks, methodologies, state of the art |
Tell the user what you plan to research and confirm the angle:
> "I'll research [topic]. My angle: [specific focus]. I'll investigate: [2-3 specific questions]. Sound right, or should I adjust?"
Wait for confirmation before proceeding.
Use `notebook_create` to create a notebook named: `Research: [Topic] - [YYYY-MM-DD]`
Use `source_add` to seed the notebook with relevant context:
Use `research_start` with a well-crafted query based on the topic and context.
**Mode selection:**
**Tip:** Run direct `WebSearch` calls in parallel with NotebookLM for faster initial data gathering while the research engine works.
Poll `research_status` until complete. Use the `query` parameter as fallback matching -- task IDs can change between `research_start` and `research_status` calls.
Use `research_import` to bring discovered sources into the notebook for deeper analysis.
Use `notebook_query` to ask 3-5 targeted questions based on the research type:
1. **Overview**: "What are the key findings about [topic]?" 2. **Opportunities**: "What opportunities or gaps exist in this space?" 3. **Actions**: "What are the most actionable insights from this research?" 4. **Risks**: "What are the main risks, challenges, or counterarguments?" 5. **Custom**: A question specific to the research type (e.g., "Who are the top 5 competitors and how do they differentiate?" for competitive intel)
Save the findings to a local file using the research brief template:
**File path:** `research/[topic-slug]-[YYYY-MM-DD].md`
Use the template from [research-brief-template.md](research-brief-template.md) to structure the output. Create the `research/` directory if it does not exist.
After saving, present the user with:
Ask the user: "Want me to generate any artifacts from this research? Options: slides, audio (podcast), video, infographic, report, mind map."
If yes, use `studio_create` with the notebook_id from Step 2.
**Available artifact types and recommended settings:**
| Type | Key params | Best for | |------|-----------|----------| | `slide_deck` | `slide_format`: `detailed_deck` or `presenter_slides`; `slide_length`: `short` or `default` | Executive presentations, client pitches | | `audio` | `audio_format`: `deep_dive`, `brief`, `critique`, or `debate`; `audio_length`: `short`, `default`, `long` | Podcast-style deep dives, learning on the go | | `video` | `video_format`: `explainer`, `brief`, `cinematic`; `visual_style`: `auto_select`, `classic`, `whiteboard`, etc. | Visual explainers, social media content | | `infographic` | `orientation`: `landscape`, `portrait`, `square`; `infographic_style`: `professional`, `bento_grid`, etc. | One-pagers, social sharing | | `report` | `report_format`: `Briefing Doc`, `Study Guide`, `Blog Post`, `Create Your Own` | Written deliverables, summaries | | `mind_map` | `title` | Visual knowledge mapping |
**Common params for all artifact types:**
**After creating an artifact:** 1. Poll `studio_status` until `completed` (audio/video: 5-15 min; slides/infographics: 2-5 min) 2. Use `download_artifact` to save locally if needed 3. Provide the notebook URL so the user can access artifacts directly
**Tips:**
Ready-to-use configurations for Anthropic's Claude Code. A comprehensive collection of AI agents, custom commands, settings, hooks, external integrations (MCPs), and project templates to enhance your development workflow.
Repo: davila7/claude-code-templates
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