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 skills add ashish7802/awesome-api-skills --skill geminigit clone --depth 1 https://github.com/ashish7802/awesome-api-skillsWrote 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/ashish7802/awesome-api-skills/gemini)<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/gemini"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/gemini/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/gemini"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/gemini.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00000 | $0.00523 |
| Opus 5.5 | $0.00000 | $0.00209 |
| Sonnet 5.5 | $0.00000 | $0.00105 |
| Haiku 4.5 | $0.00000 | $0.00052 |
Grade A, and why
gemini 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Gemini API Skill
Overview
Google Gemini offers natively multimodal capabilities (text, image, audio, video). This skill focuses on @google/genai and the v1beta API surface.
Installation
npm install @google/genai
pip install google-genai
Authentication
Use a Google API key passed to the client initialization. For GCP environments, Vertex AI authentication via IAM is preferred.
Core Concepts
- Parts: The building blocks of a message. A part can be text or inline data (images).
- Gemini 1.5 Pro: Features a massive 1-million to 2-million token context window.
- System Instructions: Provided at the model initialization level.
Common Workflows
- Initialize
GoogleGenAI. - Call
models.generateContentwith a multimodal array of parts. - Extract the text from the response candidate.
Error Handling
Watch for FinishReason.SAFETY. If the model refuses to answer due to safety settings, the response will be empty but the finishReason will indicate why.
Security
Tune safety settings (HARM_CATEGORY_HATE_SPEECH, etc.) according to your application's risk tolerance.
Rate Limits
Free tier offers 15 RPM for Gemini 1.5 Flash. Paid tiers depend on GCP quotas.
Best Practices
Leverage the massive context window by uploading entire codebases or PDFs rather than aggressively chunking, as Gemini 1.5's recall is exceptionally high.
Troubleshooting
If multimodal requests fail, ensure inline data is base64 encoded and the correct MIME type (e.g., image/jpeg) is specified.
References
Why use this skill
Use this when your agent works with gemini — structured patterns beat pasted docs and prevent common hallucinations.
AI pitfalls
- Using deprecated model IDs or wrong API endpoints
- Confusing chat vs completions vs embeddings APIs
- Omitting rate-limit and token budget handling
Production checklist
- Secrets in environment variables, not source code
- Error handling and logging in place
- Rate limits and timeouts configured
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 62 lines · 0 tokens per session scan A 089eaca5b8f6
gemini is a skill published in the GitHub repository ashish7802/awesome-api-skills (13 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 523 tokens. 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 2026-08-30.
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