gemini-pro
Gemini Pro
Concept of the skill
What it is: A routing skill that decides when a task should go to Google's Gemini Pro frontier model — the current Gemini 3.1 Pro generation (moving to Gemini 3.5 Pro) — rather than Claude Opus or GPT-5. It is model-selection judgment, not a Gemini tutorial.
Mental model: Three signals push work toward Gemini Pro and one pushes it away. PUSH TOWARD: (1) the context is genuinely large — a corpus, codebase, or long document near or beyond 200K tokens, where Gemini Pro's 1M-token input avoids chunking-and-retrieval entirely; (2) the input is mixed-multimodal — audio + video + PDF + image in one call, which Gemini accepts natively; (3) the budget wants frontier-grade reasoning at the best capability-per-dollar. PUSH AWAY: the task is in a lane another frontier model owns (Claude Opus for the hardest agentic code modification; GPT-5 for best-in-class long-context retrieval quality and hard math).
Why it exists: Agents default to one model out of habit and pay for it — either too much (sending a 30K classification to the frontier) or in capability (chunking a 500K-token corpus through a 200K-window model when a 1M-window model would read it whole). This skill replaces "use my usual model" with an evidence-based per-lane decision, and corrects the most expensive misconception: that "has a 1M context window" means "best at using a 1M context window."
What it is NOT: It is not the cheap/fast tier decision (that's gemini-flash), not agent-system architecture (agent-engineering), not request-time dispatch among your local skills (skill-router), and not a Gemini API integration guide.
Adjacent concepts: gemini-flash (the cheap/fast Gemini tier and the escalation boundary up to Pro); agent-engineering (how to compose models into a system, one layer up from picking one model); skill-router (dispatches among skills, not among external models); autonomous-loop-patterns (the loop that decides which model each step in a pipeline uses).
One-line analogy: Gemini Pro is the wide-load freight truck of frontier models — when the cargo (context) is too big to fit any other vehicle in one trip, it wins by capacity, even though a sports car (Claude Opus / GPT-5) is faster on the specific stretch of road each owns.
Common misconception: That a 1M-token context window means Gemini Pro is the best at finding the needle in 1M tokens. Context capacity and long-context retrieval quality are different properties — GPT-5 leads on retrieval quality even where Gemini holds more tokens. Route to Gemini Pro to FIT the context cheaply; verify the retrieval-quality need separately.