Gemini Deep Research Browser

@dp — Gemini Deep Research Skill

Alias: @dp [topic]

Crafts and launches a Gemini Deep Research session using a custom Deep Research Architect Gem that you own.

Setup: Create Your Deep Research Gem (One Time)

1. Go to https://gemini.google.com/gems/create
2. Name it: Deep Research Architect
3. Paste this system prompt:

You are a Deep Research Query Architect. Your job is to transform vague research topics into high-precision Gemini Deep Research queries.

When a user gives you a topic:
1. Identify the core question and 2-3 critical sub-questions
2. Add year scope (current year), domain constraints, and format requests (tables, code examples, comparisons)
3. Recommend source type:
   - Technical/general → No sources needed (web-only)
   - User-provided docs → Google Drive attachment
   - Prior research notes → NotebookLM attachment
4. Output:
   a. The paste-ready research query (3-5 sentences, specific)
   b. Source recommendation with reasoning
   c. A 3-item plan checklist of what good research will cover

Keep queries tight. Vague queries produce vague reports.

4. Save the Gem. Copy its URL — that's your @dp endpoint.

Workflow (3 Steps)

Step 1 — Craft the Query (Agent)

Expand the user's topic:

• Add year scope (e.g., "2026"), domain constraints
• Request comparison tables or code examples if technical
• Decide source type:
  • Project docs / specs → Google Drive
  • Prior research notebook → NotebookLM
  • General / technical research → None

Open your Gem URL and drop the topic in. It returns a paste-ready research query + plan checklist.

Step 2 — Launch Deep Research (User)

1. Open a new Gemini chat: https://gemini.google.com/app
2. Click + button left of input → Tools → Deep research
3. Paste the query from your Gem
4. If sources needed: attach via the Sources button before submitting
5. Submit → review the auto-generated plan → click Start research

Step 3 — Retrieve Results (User)

When research completes:

• Export as Google Doc → Share → Export
• Download as .docx
• Convert to Markdown: python -m markitdown report.docx > report.md
• Store in your project's research directory

Confirmed UI Notes (May 2026)

Curated @dp Prompt Examples

ADK / Model Architecture

@dp Gemini 3.1 model cascading: Flash-Lite drafter → Flash linter → Pro critic — ADK LoopAgent cost and quality tradeoffs 2026
@dp thinking_level None/Low/Medium/High in Gemini 3.1 — token cost, latency, quality per level for multi-agent pipelines
@dp Gemini Batch Mode 50% discount — async batch processing in ADK for non-realtime generation
@dp ARC-AGI-2 benchmark results — what long-horizon agentic tasks Gemini 3.1 Pro handles vs fails at

RAG / Embeddings

@dp sqlite-vec vs pgvector for local vector memory at 10k documents — embedding strategies 2026
@dp Gemini Embedding 2 task prefix patterns vs legacy task_type — migration guide and pitfalls
@dp MRL truncation quality vs storage tradeoffs — 256/768/1536/3072 dims benchmarks 2026

Infrastructure

@dp Jules GitHub agent autonomous PR workflows — deduplication, branch hygiene, CI/CD best practices 2026
@dp Docker dynamic MCP server pattern — runtime secret injection from GCP Secret Manager
@dp ADK DatabaseSessionService SQLite session persistence — call replay and failure logging patterns

Market / Business Research

@dp [your market] competitive landscape 2026 — pricing, features, differentiators comparison table
@dp [your tech stack] security audit patterns — OWASP Top 10 coverage and remediation 2026

Tips

• Be specific upfront. "AI agents" → bad. "ADK 2.0 LoopAgent vs LangGraph cycles — Python, 2026, cost benchmarks" → good.
• Use year scope always. Deep Research's web index is current; anchor it with "2026" to avoid outdated results.
• Review the plan. Before clicking "Start research", Deep Research shows you its research plan. Edit it if a key angle is missing.
• Export immediately. Reports expire from the chat. Export to Docs right after completion.