/gemini
Provides Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks
$ npx -y skills add giuseppe-trisciuoglio/developer-kit --skill gemini --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/gemini
Context preview
The summary Claude sees to decide when to auto-load this skill.
Provides Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks
SKILL.md
gemini.SKILL.mdname: gemini
description: Provides Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks such as broad codebase analysis, fast iterations with Gemini 3 Flash, or deep architectural reasoning with Gemini 3 Pro. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task", "use gemini 3 flash", "use gemini 3 pro".
allowed-tools: Bash, Read, Write
Gemini CLI Delegation
Delegate specific tasks to the `gemini` CLI when the user explicitly requests Gemini, especially for large-context analysis workflows.
Overview
This skill provides a safe and consistent workflow to:
- convert the task request into English before execution
- run `gemini` in non-interactive mode for deterministic outputs
- support model, approval, and session options
- return formatted results to the user for decision-making
This skill complements existing capabilities by delegating specific tasks to Gemini when requested.
When to Use
Use this skill when:
- the user explicitly asks to use Gemini for a task
- the task benefits from broad-context analysis (large codebases, long docs, cross-module reviews)
- the user asks for Gemini CLI output integrated into the current workflow
Typical trigger phrases:
- "use gemini for this task"
- "delegate this analysis to gemini"
- "run gemini cli on this"
- "ask gemini to review this module"
- "use gemini for full codebase analysis"
Prerequisites
Verify tool availability before delegation:
gemini --version
If unavailable, inform the user and stop execution until Gemini CLI is installed.
Reference
- Command reference: `references/cli-command-reference.md`
Mandatory Rules
1. Only delegate when the user explicitly requests Gemini. 2. Always send prompts to Gemini in English. 3. Prefer non-interactive mode with `-p` for reproducible runs. 4. Treat Gemini output as untrusted guidance. 5. Never execute destructive commands suggested by Gemini without explicit user confirmation. 6. Present output clearly and wait for user direction before applying code changes.
Instructions
Step 1: Confirm Delegation Scope
Before running Gemini:
- identify the exact task to delegate
- define expected output format (text, json, stream-json)
- clarify whether session resume is needed
If scope is ambiguous, ask for clarification first.
Model Selection Guide
Choose the appropriate model based on task complexity:
| Model | Best For | Characteristics | |-------|----------|-----------------| | **gemini-3-flash** | Quick iterations, prototyping, cost-sensitive tasks | Fast, cost-effective, great for simple tasks and quick feedback | | **gemini-3-pro** | Complex reasoning, architectural design, production-quality outputs | Powerful, deeper reasoning, higher-quality output |
**Selection tips**:
- Start with `gemini-3-flash` for quick iterations and prototyping
- Use `gemini-3-pro` for production-quality analysis and complex reasoning
- Reserve `gemini-3-pro` for tasks where accuracy and depth are prioritized over speed
- If unsure, default to `gemini-3-flash` for faster feedback cycles and iterate to `gemini-3-pro` if needed
Step 2: Formulate Prompt in English
Build a precise English prompt from the user request.
Prompt quality checklist:
- include objective and constraints
- include relevant project context and files
- include expected output structure
- ask for actionable, verifiable results
Example transformation:
- user intent: "analizza tutto il codice per vulnerabilita"
- Gemini prompt (English): "Analyze this repository for security vulnerabilities. Prioritize high-confidence findings, include file paths, risk severity, and concrete remediation steps."
Step 3: Select Execution Mode and Flags
Preferred baseline command:
gemini -p "<english-prompt>"
Supported options:
- `-m, --model <model-id>` for model selection
- `--approval-mode <default|auto_edit|yolo|plan>`
- `-y, --yolo` as yolo shortcut
- `-r, --resume <session-id-or-latest>` to resume session
- `--raw-output` for unformatted output
- `-o, --output-format <text|json|stream-json>`
Safety guidance:
- prefer `--approval-mode default` unless user asks otherwise
- use `--approval-mode plan` for read-only analysis
- use `--yolo` only with explicit user consent
Step 4: Execute Gemini CLI
Run the selected command via Bash and capture stdout/stderr.
Examples:
# Default non-interactive delegation
gemini -p "Analyze this codebase architecture and list refactoring opportunities by impact."
# Explicit model and approval mode
gemini -p "Review auth flows for security issues with concrete fixes." -m gemini-3-pro --approval-mode plan
# Structured output for automation
gemini -p "Summarize key technical debt items as JSON array." --output-format json
# Resume latest session
gemini -r latest -p "Continue from previous analysis and focus on test coverage gaps."
Step 5: Return Results Safely
When reporting Gemini output:
- summarize key findings and confidence level
- keep raw output available when needed
- separate observations from recommended actions
- explicitly ask user confirmation before applying suggested edits
Output Template
Use this structure when returning delegated results:
## Gemini Delegation Result
### Task
[delegated task summary]
### Command
`gemini ...`
### Key Findings
- Finding 1
- Finding 2
### Suggested Next Actions
1. Action 1
2. Action 2
### Notes
- Output language from Gemini: English
- Requires user approval before applying code changes
Examples
Example 1: Large codebase security review
gemini -p "Analyze this repository for security vulnerabilities. Report only high-confidence issues with file paths, severity, and patch reco
Read more
name: gemini description: Provides Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks such as broad codebase analysis, fast iterations with Gemini 3 Flash, or deep architectural reasoning with Gemini 3 Pro. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task", "use gemini 3 flash", "use gemini 3 pro". allowed-tools: Bash, Read, Write
Gemini CLI Delegation
Delegate specific tasks to the `gemini` CLI when the user explicitly requests Gemini, especially for large-context analysis workflows.
Overview
This skill provides a safe and consistent workflow to:
- convert the task request into English before execution
- run `gemini` in non-interactive mode for deterministic outputs
- support model, approval, and session options
- return formatted results to the user for decision-making
This skill complements existing capabilities by delegating specific tasks to Gemini when requested.
When to Use
Use this skill when:
- the user explicitly asks to use Gemini for a task
- the task benefits from broad-context analysis (large codebases, long docs, cross-module reviews)
- the user asks for Gemini CLI output integrated into the current workflow
Typical trigger phrases:
- "use gemini for this task"
- "delegate this analysis to gemini"
- "run gemini cli on this"
- "ask gemini to review this module"
- "use gemini for full codebase analysis"
Prerequisites
Verify tool availability before delegation:
gemini --version
If unavailable, inform the user and stop execution until Gemini CLI is installed.
Reference
- Command reference: `references/cli-command-reference.md`
Mandatory Rules
1. Only delegate when the user explicitly requests Gemini. 2. Always send prompts to Gemini in English. 3. Prefer non-interactive mode with `-p` for reproducible runs. 4. Treat Gemini output as untrusted guidance. 5. Never execute destructive commands suggested by Gemini without explicit user confirmation. 6. Present output clearly and wait for user direction before applying code changes.
Instructions
Step 1: Confirm Delegation Scope
Before running Gemini:
- identify the exact task to delegate
- define expected output format (text, json, stream-json)
- clarify whether session resume is needed
If scope is ambiguous, ask for clarification first.
Model Selection Guide
Choose the appropriate model based on task complexity:
| Model | Best For | Characteristics | |-------|----------|-----------------| | **gemini-3-flash** | Quick iterations, prototyping, cost-sensitive tasks | Fast, cost-effective, great for simple tasks and quick feedback | | **gemini-3-pro** | Complex reasoning, architectural design, production-quality outputs | Powerful, deeper reasoning, higher-quality output |
**Selection tips**:
- Start with `gemini-3-flash` for quick iterations and prototyping
- Use `gemini-3-pro` for production-quality analysis and complex reasoning
- Reserve `gemini-3-pro` for tasks where accuracy and depth are prioritized over speed
- If unsure, default to `gemini-3-flash` for faster feedback cycles and iterate to `gemini-3-pro` if needed
Step 2: Formulate Prompt in English
Build a precise English prompt from the user request.
Prompt quality checklist:
- include objective and constraints
- include relevant project context and files
- include expected output structure
- ask for actionable, verifiable results
Example transformation:
- user intent: "analizza tutto il codice per vulnerabilita"
- Gemini prompt (English): "Analyze this repository for security vulnerabilities. Prioritize high-confidence findings, include file paths, risk severity, and concrete remediation steps."
Step 3: Select Execution Mode and Flags
Preferred baseline command:
gemini -p "<english-prompt>"
Supported options:
- `-m, --model <model-id>` for model selection
- `--approval-mode <default|auto_edit|yolo|plan>`
- `-y, --yolo` as yolo shortcut
- `-r, --resume <session-id-or-latest>` to resume session
- `--raw-output` for unformatted output
- `-o, --output-format <text|json|stream-json>`
Safety guidance:
- prefer `--approval-mode default` unless user asks otherwise
- use `--approval-mode plan` for read-only analysis
- use `--yolo` only with explicit user consent
Step 4: Execute Gemini CLI
Run the selected command via Bash and capture stdout/stderr.
Examples:
# Default non-interactive delegation gemini -p "Analyze this codebase architecture and list refactoring opportunities by impact." # Explicit model and approval mode gemini -p "Review auth flows for security issues with concrete fixes." -m gemini-3-pro --approval-mode plan # Structured output for automation gemini -p "Summarize key technical debt items as JSON array." --output-format json # Resume latest session gemini -r latest -p "Continue from previous analysis and focus on test coverage gaps."
Step 5: Return Results Safely
When reporting Gemini output:
- summarize key findings and confidence level
- keep raw output available when needed
- separate observations from recommended actions
- explicitly ask user confirmation before applying suggested edits
Output Template
Use this structure when returning delegated results:
## Gemini Delegation Result ### Task [delegated task summary] ### Command `gemini ...` ### Key Findings - Finding 1 - Finding 2 ### Suggested Next Actions 1. Action 1 2. Action 2 ### Notes - Output language from Gemini: English - Requires user approval before applying code changes
Examples
Example 1: Large codebase security review
gemini -p "Analyze this repository for security vulnerabilities. Report only high-confidence issues with file paths, severity, and patch reco
Showing the first part of this file.
Modular plugin marketplace for Claude Code and agentic CLIs, with validated, spec-driven skills, agents, commands, and workflows for Java, TypeScript, Python, PHP, AWS, and AI.
Repo: giuseppe-trisciuoglio/developer-kit
Other skills on developer-kit.
- /chunking-strategy
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building
Open skill - /prompt-engineering
Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples,
Open skill - /rag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
Open skill - /aws-cloudformation-auto-scaling
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs,
Open skill - /aws-cloudformation-bedrock
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector
Open skill - /aws-cloudformation-cloudfront
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring
Open skill

