Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/hackermanishackerman/claude-skills-vault/gemini-clinpx skills add hackermanishackerman/claude-skills-vault --skill gemini-cligit clone --depth 1 https://github.com/hackermanishackerman/claude-skills-vaultWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hackermanishackerman/claude-skills-vault/gemini-cli)<a href="https://agentmods.dev/skills/hackermanishackerman/claude-skills-vault/gemini-cli"><img src="https://agentmods.dev/badge/skills/hackermanishackerman/claude-skills-vault/gemini-cli.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00035 | $0.00398 |
| Opus 5 | $0.00017 | $0.00199 |
| Sonnet 5 | $0.00007 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
gemini-cli scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Gemini CLI
Interact w/ Google's Gemini CLI locally. Run queries, get responses, compare outputs.
Prerequisites
Gemini CLI must be installed & configured:
- Install: https://github.com/google-gemini/gemini-cli
- Auth: Run
gemini& sign in w/ Google account - Verify:
gemini --version
When to Use
- User asks to "run/ask/use gemini"
- Compare Claude vs Gemini responses
- Get second AI opinion
- Delegate task to Gemini
Usage
# One-shot query
gemini "Your prompt"
# Specific model
gemini -m gemini-3-pro-preview "prompt"
# JSON output
gemini -o json "prompt"
# YOLO mode (auto-approve)
gemini -y "prompt"
# File analysis
cat file.txt | gemini "Analyze this"
Comparison Workflow
- Provide Claude's response first
- Run same query via Gemini CLI
- Present both for comparison
CLI Options
| Flag | Desc |
|---|---|
-m |
Model (gemini-3-pro) |
-o |
Output: text/json/stream-json |
-y |
Auto-approve (YOLO) |
-d |
Debug mode |
-s |
Sandbox mode |
-r |
Resume session |
-i |
Interactive after prompt |
Best Practices
- Quote prompts w/ double quotes
- Use
-o jsonfor parsing - Pipe files for context
- Specify model for specific capabilities
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 67 lines · 35 tokens per session scan A 2c784d0bcd6f
gemini-cli is a skill published in the GitHub repository hackermanishackerman/claude-skills-vault (2 stars, last pushed 6d ago), licensed MIT. It adds 35 tokens to every session and 398 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
toon
Token-Oriented Object Notation is a compact, human-readable encoding of the JSON data model that minimizes tokens and makes structure easy for models to follow. It's intended for LLM input as a drop-in, lossless representation of your existing JSON.
create-art-prompt
Fabric pattern: createartprompt.
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
add-prompt
Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.
add-prompt
Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.
add-prompt
Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.