Deep Research (Gemini)

    emoji: "🔬"
    category: "research"
    primaryEnv: "GOOGLE_API_KEY"
    homepage: "https://github.com/24601/agent-deep-research"
    requires:
      bins:
        - "uv"
      env:
        - "GOOGLE_API_KEY"
        - "GEMINI_API_KEY"
        - "GEMINI_DEEP_RESEARCH_API_KEY"
        - "GEMINI_DEEP_RESEARCH_AGENT"
        - "GEMINI_DEEP_RESEARCH_MODEL"
        - "GEMINI_MODEL"
    install:
      - kind: "uv"
        label: "uv (Python package runner)"
        package: "uv"
    config:
      requiredEnv:
        - "GOOGLE_API_KEY"
        - "GEMINI_API_KEY"
        - "GEMINI_DEEP_RESEARCH_API_KEY"
      example: "export GOOGLE_API_KEY=your-key-from-aistudio.google.com"
---

# Deep Research Skill

Perform deep research powered by Google Gemini's deep research agent. Upload documents to file search stores for RAG-grounded answers. Manage research sessions with persistent workspace state.

## For AI Agents

Get a full capabilities manifest, decision trees, and output contracts:

```bash
uv run {baseDir}/scripts/onboard.py --agent
```

See [AGENTS.md]({baseDir}/AGENTS.md) for the complete structured briefing.

| Command | What It Does |
|---------|-------------|
| `uv run {baseDir}/scripts/research.py start "question"` | Launch deep research |
| `uv run {baseDir}/scripts/research.py start "question" --context ./path --dry-run` | Estimate cost |
| `uv run {baseDir}/scripts/research.py start "question" --context ./path --output report.md` | RAG-grounded research |
| `uv run {baseDir}/scripts/store.py query <name> "question"` | Quick Q&A against uploaded docs |

## Security & Transparency

**Credentials**: This skill requires a Google/Gemini API key (one of `GOOGLE_API_KEY`, `GEMINI_API_KEY`, or `GEMINI_DEEP_RESEARCH_API_KEY`). The key is read from environment variables and passed to the `google-genai` SDK. It is never logged, written to files, or transmitted anywhere other than the Google Gemini API.

**File uploads**: The `--context` flag uploads local files to Google's ephemeral file search stores for RAG grounding. Sensitive files are automatically excluded: `.env*`, `credentials.json`, `secrets.*`, private keys (`.pem`, `.key`), and auth tokens (`.npmrc`, `.pypirc`, `.netrc`). Binary files are rejected by MIME type filtering. Build directories (`node_modules`, `__pycache__`, `.git`, `dist`, `build`) are skipped. The ephemeral store is auto-deleted after research completes unless `--keep-context` is specified. Use `--dry-run` to preview what would be uploaded without sending anything. Only files you explicitly point `--context` at are uploaded -- no automatic scanning of parent directories or home folders.

**Non-interactive mode**: When stdin is not a TTY (agent/CI use), confirmation prompts are automatically skipped. This is by design for agent integration but means an autonomous agent with file system access could trigger uploads. Restrict the paths agents can access, or use `--dry-run` and `--max-cost` guards.

**No obfuscation**: All code is readable Python with PEP 723 inline metadata. No binary blobs, no minified scripts, no telemetry, no analytics. The full source is auditable at [github.com/24601/agent-deep-research](https://github.com/24601/agent-deep-research).

**Local state**: Research session state is written to `.gemini-research.json` in the working directory. This file contains interaction IDs, store mappings, and upload hashes -- no credentials or research content. Use `state.py gc` to clean up orphaned stores from crashed runs.

## Prerequisites

- A Google API key (`GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable)
- [uv](https://docs.astral.sh/uv/) installed (see [uv install docs](https://docs.astral.sh/uv/getting-started/installation/))

## Quick Start

```bash
# Run a deep research query
uv run {baseDir}/scripts/research.py "What are the latest advances in quantum computing?"

# Check research status
uv run {baseDir}/scripts/research.py status <interaction-id>

# Save a completed report
uv run {baseDir}/scripts/research.py report <interaction-id> --output report.md

# Research grounded in local files (auto-creates store, uploads, cleans up)
uv run {baseDir}/scripts/research.py start "How does auth work?" --context ./src --output report.md

# Export as HTML or PDF
uv run {baseDir}/scripts/research.py start "Analyze the API" --context ./src --format html --output report.html

# Auto-detect prompt template based on context files
uv run {baseDir}/scripts/research.py start "How does auth work?" --context ./src --prompt-template auto --output report.md
```

## Environment Variables

Set one of the following (checked in order of priority):

| Variable | Description |
|----------|-------------|
| `GEMINI_DEEP_RESEARCH_API_KEY` | Dedicated key for this skill (highest priority) |
| `GOOGLE_API_KEY` | Standard Google AI key |
| `GEMINI_API_KEY` | Gemini-specific key |

Optional model configuration:

| Variable | Description | Default |
|----------|-------------|---------|
| `GEMINI_DEEP_RESEARCH_MODEL` | Model for file search queries | `gemini-3.1-pro-preview` |
| `GEMINI_MODEL` | Fallback model name | `gemini-3.1-pro-preview` |
| `GEMINI_DEEP_RESEARCH_AGENT` | Deep research agent identifier | `deep-research-pro-preview-12-2025` |

## Research Commands

### Start Research

```bash
uv run {baseDir}/scripts/research.py start "your research question"
```

| Flag | Description |
|------|-------------|
| `--report-format FORMAT` | Output structure: `executive_summary`, `detailed_report`, `comprehensive` |
| `--store STORE_NAME` | Ground research in a file search store (display name or resource ID) |
| `--no-thoughts` | Hide intermediate thinking steps |
| `--follow-up ID` | Continue a previous research session |
| `--output FILE` | Wait for completion and save report to a single file |
| `--output-dir DIR` | Wait for completion and save structured results to a directory (see below) |
| `--timeout SECONDS` | Maximum wait time when polling (default: 1800 = 30 minutes) |
| `--no-adaptive-poll` | Disable history-adaptive polling; use fixed interval curve instead |
| `--context PATH` | Auto-create ephemeral store from a file or directory for RAG-grounded research |
| `--context-extensions EXT` | Filter context uploads by extension (e.g. `py,md` or `.py .md`) |
| `--keep-context` | Keep the ephemeral context store after research completes (default: auto-delete) |
| `--dry-run` | Estimate costs without starting research (prints JSON cost estimate) |
| `--format {md,html,pdf}` | Output format for the report (default: md; pdf requires weasyprint) |
| `--prompt-template {typescript,python,general,auto}` | Domain-specific prompt prefix; auto detects from context file extensions |
| `--depth {quick,standard,deep}` | Research depth: quick (~2-5min), standard (~5-15min), deep (~15-45min) |
| `--max-cost USD` | Abort if estimated cost exceeds this limit (e.g. `--max-cost 3.00`) |
| `--input-file PATH` | Read the research query from a file instead of positional argument |
| `--no-cache` | Skip research cache and force a fresh run |

The `start` subcommand is the default, so `research.py "question"` and `research.py start "question"` are equivalent.

**Important**: When `--output` or `--output-dir` is used, the command blocks until research completes (2-10+ minutes). Do not background it with `&`. Use non-blocking mode (omit `--output`) to get an ID immediately, then poll with `status` and save with `report`.

### Check Status

```bash
uv run {baseDir}/scripts/research.py status <interaction-id>
```

Returns the current status (`in_progress`, `completed`, `failed`) and outputs if available.

### Save Report

```bash
uv run {baseDir}/scripts/research.py report <interaction-id>
```

| Flag | Description |
|------|-------------|
| `--output FILE` | Save report to a specific file path (default: `report-<id>.md`) |
| `--output-dir DIR` | Save structured results to a directory |

## Structured Output (`--output-dir`)

When `--output-dir` is used, results are saved to a structured directory:

```
<output-dir>/
  research-<id>/
    report.md          # Full final report
    metadata.json      # Timing, status, output count, sizes
    interaction.json   # Full interaction data (all outputs, thinking steps)
    sources.json       # Extracted source URLs/citations
```

A compact JSON summary (under 500 chars) is printed to stdout:

```json
{
  "id": "interaction-123",
  "status": "completed",
  "output_dir": "research-output/research-interaction-1/",
  "report_file": "research-output/research-interaction-1/report.md",
  "report_size_bytes": 45000,
  "duration_seconds": 154,
  "summary": "First 200 chars of the report..."
}
```

This is the recommended pattern for AI agent integration -- the agent receives a small JSON payload while the full report is written to disk.

## Adaptive Polling

When `--output` or `--output-dir` is used, the script polls the Gemini API until research completes. By default, it uses **history-adaptive polling** that learns from past research completion times:

- Completion times are recorded in `.gemini-research.json` under `researchHistory` (last 50 entries, separate curves for grounded vs non-grounded research).
- When 3+ matching data points exist, the poll interval is tuned to the historical distribution:
  - Before any research has ever completed: slow polling (30s)
  - In the likely completion window (p25-p75): aggressive polling (5s)
  - In the tail (past p75): moderate polling (15-30s)
  - Unusually long runs (past 1.5x the longest ever): slow polling (60s)
- All intervals are clamped to [2s, 120s] as a fail-safe.

When history is insufficient (<3 data points) or `--no-adaptive-poll` is passed, a fixed escalating curve is used: 5s (first 30s), 10s (30s-2min), 30s (2-10min), 60s (10min+).

## Cost Estimation (`--dry-run`)

Preview estimated costs before running research:

```bash
uv run {baseDir}/scripts/research.py start "Analyze security architecture" --context ./src --dry-run
```

Outputs a JSON cost estimate to stdout with context upload costs, research query costs, and a total. Estimates are heuristic-based (the Gemini API does not return token counts or billing data) and clearly labeled as such.

After research completes with `--output-dir`, the `metadata.json` file includes a `usage` key with post-run cost estimates based on actual output size and duration.

## File Search Store Commands

Manage file search stores for RAG-grounded research and Q&A.

### Create a Store

```bash
uv run {baseDir}/scripts/store.py create "My Project Docs"
```

### List Stores

```bash
uv run {baseDir}/scripts/store.py list
```

### Query a Store

```bash
uv run {baseDir}/scripts/store.py query <store-name> "What does the auth module do?"
```

| Flag | Description |
|------|-------------|
| `--output-dir DIR` | Save response and metadata to a directory |

### Delete a Store

```bash
uv run {baseDir}/scripts/store.py delete <store-name>
```

Use `--force` to skip the confirmation prompt. When stdin is not a TTY (e.g., called by an AI agent), the prompt is automatically skipped.

## File Upload

Upload files or entire directories to a file search store.

```bash
uv run {baseDir}/scripts/upload.py ./src fileSearchStores/abc123
```

| Flag | Description |
|------|-------------|
| `--smart-sync` | Skip files that haven't changed (hash comparison) |
| `--extensions EXT [EXT ...]` | File extensions to include (comma or space separated, e.g. `py,ts,md` or `.py .ts .md`) |

Hash caches are always saved on successful upload, so a subsequent `--smart-sync` run will correctly skip unchanged files even if the first upload did not use `--smart-sync`.

### MIME Type Support

36 file extensions are natively supported by the Gemini File Search API. Common programming files (JS, TS, JSON, CSS, YAML, etc.) are automatically uploaded as `text/plain` via a fallback mechanism. Binary files are rejected. See `references/file_search_guide.md` for the full list.

**File size limit**: 100 MB per file.

## Session Management

Research IDs and store mappings are cached in `.gemini-research.json` in the current working directory.

### Show Session State

```bash
uv run {baseDir}/scripts/state.py show
```

### Show Research Sessions Only

```bash
uv run {baseDir}/scripts/state.py research
```

### Show Stores Only

```bash
uv run {baseDir}/scripts/state.py stores
```

### JSON Output for Agents

Add `--json` to any state subcommand to output structured JSON to stdout:

```bash
uv run {baseDir}/scripts/state.py --json show
uv run {baseDir}/scripts/state.py --json research
uv run {baseDir}/scripts/state.py --json stores
```

### Clear Session State

```bash
uv run {baseDir}/scripts/state.py clear
```

Use `-y` to skip the confirmation prompt. When stdin is not a TTY (e.g., called by an AI agent), the prompt is automatically skipped.

## Non-Interactive Mode

All confirmation prompts (`store.py delete`, `state.py clear`) are automatically skipped when stdin is not a TTY. This allows AI agents and CI pipelines to call these commands without hanging on interactive prompts.

## Workflow Example

A typical grounded research workflow:

```bash
# 1. Create a file search store
STORE_JSON=$(uv run {baseDir}/scripts/store.py create "Project Codebase")
STORE_NAME=$(echo "$STORE_JSON" | python3 -c "import sys,json; print(json.load(sys.stdin)['name'])")

# 2. Upload your documents
uv run {baseDir}/scripts/upload.py ./docs "$STORE_NAME" --smart-sync

# 3. Query the store directly
uv run {baseDir}/scripts/store.py query "$STORE_NAME" "How is authentication handled?"

# 4. Start grounded deep research (blocking, saves to directory)
uv run {baseDir}/scripts/research.py start "Analyze the security architecture" \
  --store "$STORE_NAME" --output-dir ./research-output --timeout 3600

# 5. Or start non-blocking and check later
RESEARCH_JSON=$(uv run {baseDir}/scripts/research.py start "Analyze the security architecture" --store "$STORE_NAME")
RESEARCH_ID=$(echo "$RESEARCH_JSON" | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])")

# 6. Check progress
uv run {baseDir}/scripts/research.py status "$RESEARCH_ID"

# 7. Save the report when completed
uv run {baseDir}/scripts/research.py report "$RESEARCH_ID" --output-dir ./research-output
```

## Output Convention

All scripts follow a dual-output pattern:
- **stderr**: Rich-formatted human-readable output (tables, panels, progress bars)
- **stdout**: Machine-readable JSON for programmatic consumption

This means `2>/dev/null` hides the human output, and piping stdout gives clean JSON.