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Get Started Free →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
.claude/skills/giuseppe-trisciuoglio-gemini/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 182% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 136% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 249% | 0% |
| case-15 | ✓→✗ | ▼ Worse | 79% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 964% | 0% |
Delegate specific tasks to the gemini CLI when the user explicitly requests Gemini, especially for large-context analysis workflows.
This skill provides a safe and consistent workflow to:
gemini in non-interactive mode for deterministic outputsThis skill complements existing capabilities by delegating specific tasks to Gemini when requested.
Use this skill when:
Typical trigger phrases:
Verify tool availability before delegation:
bashgemini --version
If unavailable, inform the user and stop execution until Gemini CLI is installed.
references/cli-command-reference.md-p for reproducible runs.Before running Gemini:
If scope is ambiguous, ask for clarification first.
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:
gemini-3-flash for quick iterations and prototypinggemini-3-pro for production-quality analysis and complex reasoninggemini-3-pro for tasks where accuracy and depth are prioritized over speedgemini-3-flash for faster feedback cycles and iterate to gemini-3-pro if neededBuild a precise English prompt from the user request.
Prompt quality checklist:
Example transformation:
Preferred baseline command:
bashgemini -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:
--approval-mode default unless user asks otherwise--approval-mode plan for read-only analysis--yolo only with explicit user consentRun the selected command via Bash and capture stdout/stderr.
Examples:
bash# 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."
When reporting Gemini output:
Use this structure when returning delegated results:
markdown## 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
bashgemini -p "Analyze this repository for security vulnerabilities. Report only high-confidence issues with file paths, severity, and patch recommendations." -m gemini-3-flash --approval-mode plan
bashgemini -p "Read the available documentation and produce a concise architecture summary with component responsibilities and integration points." -m gemini-3-pro --approval-mode plan
bashgemini -p "Return a JSON list of top 10 refactoring opportunities with fields: title, file, impact, effort." -m gemini-3-flash --output-format json
bashgemini -p "Generate a minimal Express.js REST endpoint for POST /items with input validation and a unit test. Keep the implementation concise and ready to paste." -m gemini-3-flash
bashgemini -p "Summarize this CSV file's key statistics: row count, missing-value counts, and top 5 columns by variance. Provide a 6-line bullet summary suitable for quick triage." -m gemini-3-flash --output-format json --approval-mode plan
bashgemini -p "Provide 3 short alternative microcopy options (<=20 words each) for an onboarding tooltip that explains account recovery. Include a one-line A/B test metric proposal for each option." -m gemini-3-flash
bashgemini -p "Analyze the current system architecture and propose a detailed migration strategy to a microservices architecture. Include component boundaries, communication patterns, data ownership, and estimated risks for each migration step." -m gemini-3-pro --approval-mode plan
bashgemini -p "Perform a thorough security audit of the authentication and authorization layer. Identify potential vulnerabilities, rate-limiting gaps, token handling weaknesses, and injection vectors. Provide severity ratings and specific remediation steps for each finding." -m gemini-3-pro --approval-mode plan
bashgemini -p "Generate a production-ready TypeScript module for paginated API responses. Include input validation, error handling, retry logic with exponential backoff, and comprehensive unit tests with mocks. Follow best practices for error types, logging, and type safety." -m gemini-3-pro --approval-mode auto_edit
plan mode for analysis-only tasks| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,132 | 6,654 | -53% | 1 | 1 | 0% | 2,437 | 2,265 | -7% | 0 | 0 | — |
case-02 | fail→fail | 13,245 | 12,172 | -8% | 1 | 1 | 0% | 1,536 | 3,001 | +95% | 0 | 0 | — |
case-03 | fail→fail | 12,723 | 5,833 | -54% | 1 | 1 | 0% | 2,142 | 2,168 | +1% | 0 | 0 | — |
case-04 | fail→fail | 15,215 | 7,057 | -54% | 1 | 1 | 0% | 2,542 | 2,290 | -10% | 0 | 0 | — |
case-05 | fail→fail | 8,923 | 6,115 | -31% | 1 | 1 | 0% | 796 | 2,110 | +165% | 0 | 0 | — |
case-06 | fail→pass | 6,380 | 7,703 | +21% | 1 | 1 | 0% | 953 | 2,689 | +182% | 0 | 0 | — |
case-07 | fail→fail | 5,058 | 6,158 | +22% | 1 | 1 | 0% | 858 | 2,190 | +155% | 0 | 0 | — |
case-08 | fail→fail | 5,002 | 5,509 | +10% | 1 | 1 | 0% | 730 | 2,244 | +207% | 0 | 0 | — |
case-09 | pass→pass | 6,836 | 9,302 | +36% | 1 | 1 | 0% | 1,038 | 2,859 | +175% | 0 | 0 | — |
case-10 | fail→fail | 8,205 | 6,522 | -21% | 1 | 1 | 0% | 1,447 | 2,276 | +57% | 0 | 0 | — |
case-11 | fail→fail | 8,001 | 5,204 | -35% | 1 | 1 | 0% | 1,305 | 2,224 | +70% | 0 | 0 | — |
case-12 | fail→fail | 12,960 | 5,402 | -58% | 1 | 1 | 0% | 2,769 | 2,093 | -24% | 0 | 0 | — |
case-13 | pass→pass | 9,323 | 4,627 | -50% | 1 | 1 | 0% | 1,630 | 2,667 | +64% | 0 | 0 | — |
case-14 | fail→pass | 7,806 | 7,717 | -1% | 1 | 1 | 0% | 1,134 | 2,677 | +136% | 0 | 0 | — |
case-15 | pass→fail | 8,216 | 7,626 | -7% | 1 | 1 | 0% | 1,300 | 2,326 | +79% | 0 | 0 | — |
case-16 | fail→fail | 12,766 | 6,299 | -51% | 1 | 1 | 0% | 1,929 | 2,242 | +16% | 0 | 0 | — |
case-17 | fail→fail | 7,162 | 8,436 | +18% | 1 | 1 | 0% | 1,126 | 2,254 | +100% | 0 | 0 | — |
case-18 | fail→pass | 4,477 | 6,501 | +45% | 1 | 1 | 0% | 686 | 2,394 | +249% | 0 | 0 | — |
case-19 | pass→pass | 5,294 | 8,436 | +59% | 1 | 1 | 0% | 844 | 3,289 | +290% | 0 | 0 | — |
case-20 | pass→fail | 10,799 | 17,174 | +59% | 1 | 1 | 0% | 231 | 2,457 | +964% | 0 | 0 | — |
case-21 | pass→fail | 6,808 | 10,585 | +55% | 1 | 1 | 0% | 982 | 2,679 | +173% | 0 | 0 | — |
case-22 | pass→fail | 2,325 | 6,960 | +199% | 1 | 1 | 0% | 389 | 2,324 | +497% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 7 counted toward the lift figure. The other 15 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -5 percentage points is the difference between those two pass rates over the 7 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.