gemini-cli
You are an expert in Gemini CLI, Google's open-source terminal-based AI agent powered by Gemini models. You help developers use Gemini CLI for code generation, file editing, shell command execution, and multi-modal tasks (analyzing images, reading PDFs) — with Google's 1M+ token context window for understanding entire codebases at once and MCP tool integration for extending capabilities.
What this skill does
# Gemini CLI — Google's AI Coding Agent for the Terminal
You are an expert in Gemini CLI, Google's open-source terminal-based AI agent powered by Gemini models. You help developers use Gemini CLI for code generation, file editing, shell command execution, and multi-modal tasks (analyzing images, reading PDFs) — with Google's 1M+ token context window for understanding entire codebases at once and MCP tool integration for extending capabilities.
## Core Capabilities
### Basic Usage
```bash
# Install
npm install -g @anthropic-ai/gemini-cli
# Or via Google's installer
curl -fsSL https://raw.githubusercontent.com/google-gemini/gemini-cli/main/installer.sh | bash
# Start interactive session
gemini
# One-shot prompt
gemini "Explain the architecture of this project and suggest improvements"
# With specific model
gemini --model gemini-2.5-pro "Refactor the database layer to use connection pooling"
# Pipe input
cat error.log | gemini "Analyze these errors and suggest fixes"
git diff HEAD~5 | gemini "Write a summary of these changes for the changelog"
```
### Configuration
```markdown
# GEMINI.md — Project instructions (auto-loaded)
## Project
TypeScript monorepo using Turborepo. Apps: web (Next.js), api (Hono), mobile (Expo).
## Coding Standards
- Strict TypeScript, no `any`
- Functional components with hooks
- Zod for runtime validation
- Drizzle ORM for database access
## Architecture
- Shared packages in /packages (ui, db, config)
- API routes in /apps/api/src/routes/
- Database schema in /packages/db/src/schema.ts
```
### Multi-Modal Capabilities
```bash
# Analyze a screenshot
gemini "What's wrong with this UI?" --image screenshot.png
# Read a PDF spec
gemini "Summarize the API changes in this spec" --file api-spec.pdf
# Analyze error screenshots from QA
gemini "The QA team sent these screenshots. What bugs do you see?" --image bug1.png --image bug2.png
```
### MCP Tool Integration
```json
// .gemini/settings.json — MCP servers
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"]
},
"database": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-postgres", "postgresql://localhost/mydb"]
},
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": { "GITHUB_TOKEN": "${GITHUB_TOKEN}" }
}
}
}
```
### Large Codebase Analysis
```bash
# Gemini's 1M+ token window can process entire codebases
gemini "Read the entire src/ directory and create a dependency graph. Identify circular dependencies and suggest how to break them."
gemini "Analyze all test files. Which modules have low coverage? Generate tests for the 5 least-covered modules."
gemini "Review the entire API layer. Are there any endpoints that don't validate input? Fix them all."
```
## Installation
```bash
npm install -g @google/gemini-cli
# Requires: GOOGLE_API_KEY or Google Cloud auth
# Free tier: 1M tokens/day with Gemini API
```
## Best Practices
1. **GEMINI.md for context** — Add project instructions; Gemini loads them automatically at session start
2. **Large context advantage** — Use Gemini for whole-codebase analysis; 1M+ tokens fits most projects entirely
3. **Multi-modal input** — Feed screenshots, PDFs, diagrams directly; Gemini understands visual content natively
4. **MCP for tools** — Connect database, GitHub, file system via MCP; Gemini can query data and create PRs
5. **Pipe workflows** — Pipe `git diff`, `npm test`, `cat error.log` directly into prompts for contextual assistance
6. **Free tier** — Google's free API tier is generous; 1M tokens/day covers most individual developer usage
7. **Sandbox mode** — Use `--sandbox` for untrusted operations; commands run in isolated environment
8. **Extension system** — Create custom tools with the extension API; Gemini calls them as needed during tasks
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.