gemini

gemini is a skill for Claude Code, Codex from conectlens/lenserfight. It costs 12 tokens per session (91 once invoked), scanned A, original, MIT.

A collection of templates for using Google Gemini models or comparing Gemini with other AI models.

In plain words
What is it for?
It is for comparing AI outputs and evaluating Gemini alongside other models.
Why use it?
It gives you ready-made starting points for model comparisons and Gemini-focused tasks.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for comparing AI outputs and evaluating Gemini alongside other models.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/conectlens/lenserfight/gemini
Install

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.

Any agent
npx skills add conectlens/lenserfight --skill gemini
Clone the repo
git clone --depth 1 https://github.com/conectlens/lenserfight

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for gemini

README.md
[![agentmods](https://agentmods.dev/badge/skills/conectlens/lenserfight/gemini/github.svg)](https://agentmods.dev/skills/conectlens/lenserfight/gemini)
Your own site
<a href="https://agentmods.dev/skills/conectlens/lenserfight/gemini"><img src="https://agentmods.dev/badge/skills/conectlens/lenserfight/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.

agentmods 80×15 button for gemini

Your own site · 80×15
<a href="https://agentmods.dev/skills/conectlens/lenserfight/gemini"><img src="https://agentmods.dev/badge/skills/conectlens/lenserfight/gemini.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 91 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00012 $0.00091
Opus 5 $0.00006 $0.00046
Sonnet 5 $0.00002 $0.00018
Haiku 4.5 $0.00001 $0.00009

Measured 9d ago against content hash 096edcfcffe1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 9d 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.

.lenserfight/rays/gemini/SKILL.md · 17 lines

What it actually says

#gemini

Templates that target Google Gemini as the primary model or include Gemini in a head-to-head comparison.

Public lenses

Public battles

Changes

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.

  1. 9d ago First seen · 17 lines · 12 tokens per session scan A 096edcfcffe1

Subscribe to this mod's changes

gemini is a skill published in the GitHub repository conectlens/lenserfight (18 stars, last pushed yesterday), licensed MIT. It adds 12 tokens to every session and 91 once invoked, about $0.0001 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 2026-09-03.

Related

Other skills, from other repositories

langchain_patterns

Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.

vuralserhat86/antigravity-agentic-skills · 57 tokens

langchain_patterns

Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.

DonggangChen/antigravity-agentic-skills · 57 tokens

ai-ml-development

AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.

travisjneuman/.claude · 43 tokens

ai-policy-generator

AI governance policy creation for nonprofits and enterprises with frameworks, risk assessment, ethical guidelines, and compliance templates. Use when drafting AI usage policies, responsible AI frameworks, or organizational AI governance documents.

travisjneuman/.claude · 42 tokens

data-engineering

ETL/ELT pipelines, data warehousing (BigQuery, Snowflake, Redshift), stream processing (Kafka, Spark Streaming), orchestration (Airflow, Dagster, Prefect), dbt transformations, and data lake architecture. Use when building data pipelines, designing warehouse schemas, or implementing real-time data processing.

travisjneuman/.claude · 70 tokens

mechanism-skills

Routing entry point for eleven families of mechanistic-interpretability methods that localize which internal object (layer, attention head, neuron, SAE feature, weight, or input feature) drives a model's behavior, how influential it is, and what changes when it is intervened on. Use this skill whenever the question is…

zjunlp/Mechanist · 214 tokens