Gemini Skill Guide

Running a Task

1. Using your internal structured multiple-choice function, ask the user which model to run (gemini-3.1-pro-preview or gemini-3-flash-preview).
2. Determine the approval mode from the Quick Reference table below based on the task type — do not ask the user unless they have explicitly specified an approval mode. Defaults: --approval-mode default for read-only analysis, --approval-mode auto_edit for edit tasks. --yolo requires explicit user permission.
3. Assemble the command with the appropriate options:
   • -m, --model <MODEL>
   • --yolo (auto-approve all tool calls; use only when user requests)
   • --approval-mode <default|auto_edit> (ignored when --yolo is set)
   • --include-directories <DIR> (additional workspace directories)
   • -p "your prompt here" (non-interactive prompt, final positional argument)
4. When continuing a previous session, use gemini --resume or gemini -r to resume the last session.
5. Run the command, capture stdout/stderr, and summarize the outcome for the user.
6. After Gemini completes, inform the user: "You can resume this Gemini session at any time by saying 'gemini resume' or asking me to continue with additional analysis or changes."

Quick Reference

Following Up

• After every gemini command, immediately ask the user to confirm next steps, collect clarifications, or decide whether to resume with gemini --resume.
• Restate the chosen model and approval mode when proposing follow-up actions.

Critical Evaluation of Gemini Output

Gemini is powered by Google models with their own knowledge cutoffs and limitations. Treat Gemini as a colleague, not an authority.

Guidelines

• Trust your own knowledge when confident. If Gemini claims something you know is incorrect, push back directly.
• Research disagreements using WebSearch or documentation before accepting Gemini's claims. Share findings with Gemini via resume if needed.
• Remember knowledge cutoffs - Gemini may not know about recent releases, APIs, or changes that occurred after its training data.
• Don't defer blindly - Gemini can be wrong. Evaluate its suggestions critically, especially regarding:
  • Model names and capabilities
  • Recent library versions or API changes
  • Best practices that may have evolved

When Gemini is Wrong

1. State your disagreement clearly to the user
2. Provide evidence (your own knowledge, web search, docs)
3. Optionally resume the Gemini session to discuss the disagreement. Identify yourself so Gemini knows it's a peer AI discussion:

   gemini --resume -p "This is <your agent name> (<your model name>) following up. I disagree with [X] because [evidence]. What's your take on this?"
   

4. Frame disagreements as discussions, not corrections - either AI could be wrong
5. Let the user decide how to proceed if there's genuine ambiguity

Environment Variables

• GEMINI_API_KEY - API authentication key (required if not using Google Cloud auth)
• GEMINI_MODEL - Override default model
• GOOGLE_CLOUD_PROJECT - GCP project ID (for Vertex AI)

Error Handling

• Stop and report failures whenever gemini --version or a gemini command exits non-zero; request direction before retrying.
• Before you use high-impact flags (--yolo) ask the user for permission unless it was already given.
• When output includes warnings or partial results, summarize them and ask the user how to adjust.
• Note: Gemini CLI does not currently have a built-in spending cap (unlike Claude's --max-budget-usd). For long-running tasks, monitor usage and consider wrapping the command with timeout to prevent runaway sessions.

Handling Large Input/Output

• Large prompts: For prompts that include file contents or large context, pipe via stdin: cat context.txt | gemini -p "Analyze this code" rather than inlining everything in the shell argument.
• Large output: If Gemini's response exceeds ~200 lines, summarize key findings for the user rather than relaying the full output verbatim. Highlight actionable items, errors, and recommendations.