gemini-deep-research
Run Gemini Deep Research via browser automation. Persistent Chrome on CDP port 9222. Use when user asks to research a topic with.
What this skill does
# Gemini Deep Research
Run long-form research queries through Google's Gemini Deep Research via browser automation (Playwright CDP). Produces 40k+ char markdown reports with source citations.
> **Self-Evolving Skill**: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
## Prerequisites
1. **Chrome with debug port**: Must be running with `--remote-debugging-port=9222`
2. **Gemini Advanced subscription**: Logged into gemini.google.com in the debug Chrome
3. **playwright-core**: `bun add -g playwright-core` (or project-local)
4. **Runtime**: Use `npx tsx` (not `bun run`) — Bun's CDP connectOverCDP times out; Node.js connects in <1s
### Launch Chrome (if not running)
```bash
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \
--remote-debugging-port=9222 \
--user-data-dir="/tmp/gemini-research-profile" \
"https://gemini.google.com/app" &
```
Then log in manually with a Gemini Advanced account.
## Usage
### CLI (direct)
```bash
# Health check — verify Chrome CDP + Gemini login
npx tsx {{skill_dir}}/scripts/research.ts --health
# Basic research (runs preflight automatically)
npx tsx {{skill_dir}}/scripts/research.ts "your research query"
# Save to specific file
npx tsx {{skill_dir}}/scripts/research.ts \
--output /tmp/report.md \
--timeout 45 \
"comprehensive analysis of quantum computing error correction 2025-2026"
# Auto-save to directory (creates {date}-{slug}.md)
npx tsx {{skill_dir}}/scripts/research.ts \
--output-dir ~/.claude/automation/gemini-deep-research/output \
"your query"
# Without auto-confirming plan (lets you review first)
npx tsx {{skill_dir}}/scripts/research.ts --no-confirm "query"
```
### Programmatic (import)
```typescript
import { GeminiDeepResearchClient } from "{{skill_dir}}/scripts/client.js";
const client = new GeminiDeepResearchClient({
cdpUrl: "http://127.0.0.1:9222",
maxResearchTimeMs: 30 * 60 * 1000,
autoConfirm: true,
onProgress: (msg) => console.log(msg),
});
await client.init();
const result = await client.research("your query");
// result.report — full markdown report (40k+ chars)
// result.plan — research plan text
// result.completed — boolean
// result.durationMs — execution time
// result.shareLink — Gemini share URL (if Firecrawl enabled)
await client.close();
```
## Preflight
Every research run starts with an automatic preflight health check that verifies:
1. **Chrome CDP reachable** on configured port
2. **Browser connection** via WebSocket succeeds
3. **Gemini page open** at gemini.google.com
4. **Login state OK** (not showing sign-in wall)
If any check fails, research aborts with a clear error message. Use `--no-preflight` to skip.
## Automation Flow
```
Preflight (CDP + login check) → abort if unhealthy
↓
Chrome CDP:9222 → Navigate gemini.google.com/app
↓
Tools button → Deep Research drawer item → Active chip verification
↓
Type query (30ms/char) → Send button (or Enter fallback)
↓
Wait for research plan (~18-120s) → Extract plan text
↓
Auto-confirm "Start research" (or manual)
↓
Poll completion: mic button + text stability (5s intervals, 30min max)
↓
Extract report (longest .markdown element) → Optional share link + Firecrawl
```
## Debug Probes
When selectors break (Google updates Gemini UI), use the probe scripts:
```bash
# Check Chrome connectivity
bun run {{skill_dir}}/scripts/probes/dom-inspector.ts status
# Test all selectors against live DOM
bun run {{skill_dir}}/scripts/probes/dom-inspector.ts selectors
# Full DOM inspection
bun run {{skill_dir}}/scripts/probes/dom-inspector.ts probe
# Monitor active research execution
bun run {{skill_dir}}/scripts/probes/research-monitor.ts confirm-and-monitor
# Check research completion + extract share link
bun run {{skill_dir}}/scripts/probes/share-link.ts status
bun run {{skill_dir}}/scripts/probes/share-link.ts extract
```
## Selector Registry
All CSS selectors live in `scripts/selectors.ts`. When Google updates the Gemini UI:
1. Run `dom-inspector.ts selectors` to identify broken selectors
2. Run `dom-inspector.ts probe` to inspect current DOM
3. Update `selectors.ts` with new selectors
4. Re-test with `dom-inspector.ts selectors`
Selectors last verified: **2026-03-13** (Tools button: now `button.toolbox-drawer-button`, aria-label removed)
## Key Files
| File | Purpose |
| ------------------------------------ | ------------------------------------------------ |
| `scripts/research.ts` | Unified CLI entrypoint |
| `scripts/client.ts` | `GeminiDeepResearchClient` class |
| `scripts/selectors.ts` | CSS selector registry (13 groups with fallbacks) |
| `scripts/probes/dom-inspector.ts` | DOM probing (5 commands) |
| `scripts/probes/research-monitor.ts` | Research execution monitor |
| `scripts/probes/share-link.ts` | Share link extraction |
## Options Reference
| Option | Default | Description |
| ------------------- | ----------------------- | ------------------------------------------- |
| `cdpUrl` | `http://127.0.0.1:9222` | Chrome CDP endpoint |
| `maxResearchTimeMs` | `1800000` (30 min) | Max wait for research completion |
| `pollIntervalMs` | `5000` (5s) | How often to check for completion |
| `autoConfirm` | `true` | Auto-click "Start research" on plan |
| `enableFirecrawl` | `false` | Extract share link + scrape via Firecrawl |
| `firecrawlUrl` | `http://localhost:3002` | Self-hosted Firecrawl endpoint |
| `--no-preflight` | (preflight runs) | Skip automatic health check before research |
## Completion Detection
Research completion is detected via three signals:
1. **Mic button visible** — `button[data-node-type="speech_dictation_mic_button"]` reappears
2. **Report text > 500 chars** — longest `.markdown.markdown-main-panel` element
3. **Text stability** — 3 consecutive identical text lengths (15s total)
The spinner may remain visible as a stale artifact after completion — the mic button is the primary signal.
## Post-Execution Reflection
After this skill completes, check before closing:
1. **Did the command succeed?** — If not, fix the instruction or error table that caused the failure.
2. **Did parameters or output change?** — If the underlying tool's interface drifted, update Usage examples and Parameters table to match.
3. **Was a workaround needed?** — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround.
Only update if the issue is real and reproducible — not speculative.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.