version: "1.0.0" name: gemini-cli description: | This skill should be used when the user asks to "run gemini", "ask gemini", "delegate to gemini", "use Gemini CLI", "gemini resume", or "continue with gemini". Runs a prompt through Gemini CLI headless on a Gemini API key, with the model the user names, session resume by id, and the token counts each run consumed.

Gemini CLI (headless)

scripts/gemini-run.sh, next to this file, takes one prompt file and prints the answer, the token counts the CLI reported, and the session id. -h carries the flags and the exit statuses.

Prerequisites

• Node.js on PATH (from https://nodejs.org or the system's package manager), then Gemini CLI:

npm install -g @google/gemini-cli.

• GEMINI_API_KEY set in the environment of the command that runs the script. When the

environment does not have it, the user's own instructions say where it comes from.

• The command needs network access to the Gemini API and write access to the CLI's state

directory (~/.gemini, or $GEMINI_CLI_HOME/.gemini when that is set), where sessions are stored; a sandboxed shell has to be granted both. Keep the same state directory across a session's runs.

• The script stops when a settings file selects an auth method other than the API key; read

references/auth.md then, and on any other authentication failure.

Running a task

1. Script path. Resolve scripts/gemini-run.sh against the directory holding this

SKILL.md and run that absolute path.

1. Model. The model is whatever the user named; -m has no default. When none was named,

list the models the key can reach and ask: curl -s https://generativelanguage.googleapis.com/v1beta/models -H "x-goog-api-key: $GEMINI_API_KEY".

1. Approval mode. plan (the default) runs read-only tools; web search is available in it.

Pass -a auto_edit or -a yolo only when the user asked for Gemini to change files.

1. Directory. Pass -C <dir> — the directory whose files the task is about, or a temporary

directory the host permits for a question with no files. Sessions are stored per directory, so record the directory with the session and pass the same -C on resume.

1. Prompt. Write it to a file named for this invocation in a temporary directory the host

permits. The script feeds the file to the CLI on stdin.

1. Run inside a subagent that has shell access, where the host offers one, so the CLI's

output stays out of the main context; otherwise run it directly. The return contract: the answer (or the error), the TOKENS lines verbatim, and the SESSION_ID: line verbatim.

1. Record each SESSION_ID against its purpose, model and directory; it is the only resume

handle, and only a successful run prints one.

1. Resume by writing a new prompt file and running the same command with -S <SESSION_ID>,

the same -m, and the same -C.

1. Report the outcome, the session id, and the token counts. The counts are per model:

input is the prompt minus what was served from cache, cached is the cached part, candidates and thoughts are output. Each run re-sends the CLI's own system prompt and tool definitions, and a resumed run re-sends the conversation so far, so input grows with the session. To turn counts into cost, read the model's current rates at https://ai.google.dev/gemini-api/docs/pricing.