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gemini skill

/gemini

This skill helps you perform large-context code analysis and repository-scale reviews using Gemini CLI, enabling multimodal evaluation and strategic insights.

npx playbooks add skill zpankz/mcp-skillset --skill gemini

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SKILL.md
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---
name: gemini
description: |
  Execute Google Gemini CLI for large-context code analysis, multimodal reasoning, and repository-scale reviews.
  Also use for delegating tasks requiring 1M token context windows or Gemini-specific capabilities.
tools:
  - Bash
  - Read
  - Grep
  - Glob
  - WebFetch
disallowedTools:
  - Write
  - Edit
  - TodoWrite
context: fork
model: opus
---

# Gemini Skill Guide

## When to Use This Skill

**Primary Use Cases**:

1. User explicitly requests `gemini` CLI execution (Mode 1: Direct CLI)
2. Large-context analysis requiring 1M token window (Mode 2: Gemini Delegation)
3. Repository-scale code reviews and architectural analysis
4. Multimodal analysis (code + images/diagrams)
5. Cross-module dependency tracking

**Do NOT use for**:

- Simple code explanations (use Claude directly)
- Small context tasks (<100K tokens)
- Tasks not requiring Gemini-specific capabilities

## Execution Modes

### Mode 1: Direct CLI Execution

**When**: User explicitly asks to run gemini CLI

**Workflow**:

1. Gather configuration using structured questions:
   - Model selection (gemini-3-pro-preview vs gemini-3-flash-preview)
   - Approval mode (default, auto_edit, yolo)
   - Output format (text, json, stream-json)

2. Execute:
   ```bash
   gemini "<PROMPT>" -m <MODEL> --approval-mode <MODE> --output-format <FORMAT>
   ```

3. Inform user: "You can resume with 'gemini --resume latest' anytime"

**Resuming**:
```bash
gemini "<CONTINUATION PROMPT>" --resume latest
```

### Mode 2: Gemini Delegation for Large Context

**When**: Task requires large context window (>100K tokens), multimodal input,
or repository-scale analysis

**Workflow**:

1. Analyze context and identify what Gemini analysis would help
2. Formulate comprehensive query with all relevant context:
   - Problem statement
   - Current findings
   - Code snippets or file references
   - Specific questions
3. Leverage Gemini's 1M token context for comprehensive analysis
4. Execute: `gemini "<DETAILED CONTEXT>" --output-format json`
5. Synthesize Gemini response into actionable insights
6. Report findings with:
   - Clear summary of Gemini's analysis
   - Specific recommendations or solutions
   - Additional considerations or caveats
   - Next steps if applicable

### Error Handling

- Verify gemini binary exists before execution: `which gemini`
- Stop immediately on non-zero exit codes and report to user
- Request direction before retrying failed commands
- Before using high-autonomy flags, confirm with user:
  - `--yolo` (auto-approve all actions)
  - `--approval-mode yolo`

### Context Management

- Gemini excels at large context (up to 1M tokens)
- For massive contexts (>500K tokens), consider module-by-module summaries
- Use `--include-directories` to add additional workspace paths
- Leverage session resume for iterative refinement

## Configuration Gathering Patterns

### Initial Configuration (Mode 1, Step 1)

At the start of Mode 1 execution, gather model, approval mode, and output format:

**Standard configuration question:**

```text
Select gemini configuration (model, approval mode, and output format):

1. "gemini-3-pro-preview / default / json (Recommended)" - High quality, safe mode
2. "gemini-3-pro-preview / auto_edit / json" - High quality, auto-approve edits
3. "gemini-3-flash-preview / default / json" - Fast, safe mode
4. "gemini-3-flash-preview / auto_edit / json" - Fast, auto-approve edits
5. "gemini-3-pro-preview / yolo / json" - High quality, fully autonomous
6. "gemini-3-flash-preview / yolo / json" - Fast, fully autonomous
7. "Custom" - User will specify model, approval, and format separately
```

### High-Impact Flags Confirmation

Before executing with --yolo or autonomous flags:

```text
Ready to execute with these flags: [LIST FLAGS]. Proceed?

1. "Execute now" - Run as configured
2. "Modify configuration" - Change settings
3. "Cancel" - Abort
```

### Post-Execution Follow-up

After gemini command completes:

```text
Gemini completed. [SUMMARY]. Next steps?

1. "Resume with additional prompt" - Continue session
2. "Analyze results" - Review output
3. "Complete" - Finished
4. "Retry with different config" - Adjust settings
```

### Error Recovery

When command fails or has warnings:

```text
Error: [SPECIFIC ERROR]. How to proceed?

1. "Resume with adjustments" - Fix and continue
2. "Retry with different config" - Change model/approval/format
3. "Accept partial results" - Use what worked
4. "Invoke heal-skill" - Fix outdated SKILL.md
```

## Running a Task

1. Gather configuration using the patterns above
2. Assemble the command with the appropriate options:
   - `-m, --model <MODEL>` (gemini-3-pro-preview or gemini-3-flash-preview)
   - `--approval-mode <MODE>` (default, auto_edit, yolo)
   - `-o, --output-format <FORMAT>` (text, json, stream-json)
   - `--include-directories <DIRS>` (additional workspace paths)
   - `-r, --resume <SESSION>` (latest or session index)
3. Run the command, capture output, and summarize for the user
4. **After Gemini completes**, inform the user: "You can resume this Gemini
   session at any time by saying 'gemini resume' or asking me to continue."

### Quick Reference

| Use case | Approval mode | Key flags |
| --- | --- | --- |
| Safe analysis | `default` | `--approval-mode default --output-format json` |
| Auto-approve edits | `auto_edit` | `--approval-mode auto_edit --output-format json` |
| Fully autonomous | `yolo` | `--yolo --output-format json` |
| Resume recent session | Inherited | `--resume latest` |
| Add workspace directories | Match task | `--include-directories <DIRS>` |
| Interactive continuation | default | `-i "<PROMPT>"` (stays interactive after) |

## Model Selection

| Model | Use Case | Context | Speed |
|-------|----------|---------|-------|
| `gemini-3-pro-preview` | Complex reasoning, architecture review | 1M tokens | Slower |
| `gemini-3-flash-preview` | Quick analysis, simple tasks | 1M tokens | Faster |

## Session Management

```bash
# List available sessions
gemini --list-sessions

# Resume most recent session
gemini --resume latest

# Resume specific session by index
gemini --resume 5

# Delete a session
gemini --delete-session 3
```

## MCP Server Integration

```bash
# List configured MCP servers
gemini mcp list

# Add an MCP server
gemini mcp add <name> <command> [args...]

# Remove an MCP server
gemini mcp remove <name>

# Limit to specific MCP servers
gemini "<PROMPT>" --allowed-mcp-server-names mcp-skillset
```

## Extension System

```bash
# List installed extensions
gemini extensions list

# Install extension from git or path
gemini extensions install <source> [--auto-update] [--pre-release]

# Update extensions
gemini extensions update [--all]

# Disable/enable extension
gemini extensions disable <name>
gemini extensions enable <name>
```

## Following Up

- After every `gemini` command, offer follow-up options
- When resuming, use: `gemini "<new prompt>" --resume latest`
- The resumed session automatically uses the same context from the original
- Restate the chosen model and approval mode when proposing follow-up actions

## Error Handling Guidelines

- Stop and report failures whenever `gemini --version` or a `gemini` command
  exits non-zero; request direction before retrying
- Confirm high-impact flags before execution
- When output includes warnings or partial results, offer error recovery options

## Comparison with Codex Skill

| Feature | Gemini | Codex |
|---------|--------|-------|
| Context window | 1M tokens | ~200K tokens |
| Primary models | gemini-3-pro/flash | gpt-5.2-codex |
| Approval modes | default/auto_edit/yolo | never/on-request/on-failure |
| Sandbox | Boolean (--sandbox) | read-only/workspace-write/danger-full-access |
| Resume | --resume latest/index | codex exec resume --last |
| Stderr handling | N/A | 2>/dev/null for thinking tokens |
| Output format | text/json/stream-json | json flag |

## Integration Patterns

### With Other CLI Agents

```bash
# Use gemini for large context, codex for GPT-specific
gemini "Analyze entire codebase architecture" --output-format json
codex exec "Implement specific feature based on analysis" --full-auto

# Chain with research CLI
research docs -t "framework API" --format json | gemini "Apply this to codebase"
```

### Multimodal Analysis

```bash
# Gemini can process images inline with code
gemini "Analyze this architecture diagram and compare with implementation" \
  --include-directories ./docs ./src
```

## Heal-Skill Integration

When gemini CLI API changes are detected (command failures, unexpected output formats, or deprecated flags):

1. **Detection**: Notice command failures or API mismatches during execution
2. **Trigger**: Flag skill for healing via `/heal-skill gemini`
3. **Analysis**: Healing agent analyzes current CLI with `gemini --help` and `gemini --version`
4. **Update**: Updates skill documentation to match current API
5. **Validation**: Re-validates agent configuration for compatibility
6. **Model Verification**: Ensures only gemini-3 models are referenced
7. **Context Window Verification**: Confirms 1M token context window specifications

**Common Changes to Monitor**:
- New or deprecated command flags
- Changes to approval modes or output formats
- Model availability updates (gemini-3 family)
- MCP server integration API changes
- Session management API modifications
- Extension system updates
- Context window specification changes

Overview

This skill executes the Google Gemini CLI to perform large-context code analysis, multimodal reasoning, and repository-scale reviews. It’s designed for tasks that require Gemini-specific capabilities or 1M token context windows. Use it to delegate heavy analysis, resume long sessions, or run Gemini with controlled approval modes.

How this skill works

The skill gathers essential configuration (model, approval mode, output format, include paths) and assembles a gemini CLI command to run locally or on a configured MCP server. For very large contexts it formulates a comprehensive delegation payload (problem statement, findings, file references) and runs Gemini with JSON output, then synthesizes results into actionable recommendations. It validates the gemini binary, stops on non-zero exits, and requests confirmation before high-autonomy flags like --yolo.

When to use it

  • User explicitly requests a gemini CLI run
  • Repository-scale code reviews or architecture analysis across many files
  • Tasks requiring >100K tokens up to 1M token context windows
  • Multimodal analysis combining code and images/diagrams
  • Cross-module dependency tracing and large refactor planning

Best practices

  • Confirm model choice: gemini-3-pro-preview for deep reasoning, gemini-3-flash-preview for faster runs
  • Always ask user to confirm high-impact flags (--yolo, approval-mode yolo) before execution
  • Validate gemini binary with which gemini and stop on non-zero exit codes
  • For contexts >500K tokens, create module-by-module summaries before full delegation
  • Use --include-directories to ensure Gemini has needed workspace paths and provide JSON output for easier parsing

Example use cases

  • Run gemini to analyze an entire repository architecture and propose refactor steps
  • Delegate a 1M-token audit combining source files and design diagrams to find security or consistency issues
  • Resume a long-running Gemini session to refine recommendations after user feedback
  • Perform multimodal reasoning: compare an architecture diagram image with implemented code paths
  • Chain with other CLIs: use research output as input to Gemini for applying external knowledge to codebase

FAQ

What should I do if gemini command exits non-zero?

Stop and report the error, show the exit output, and ask whether to retry with adjusted config, resume with fixes, or accept partial results.

When should I choose auto_edit or yolo approval modes?

Use auto_edit for trusted auto-approved edits in controlled repos; require explicit confirmation before using yolo because it grants full autonomy and potential destructive actions.