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 commands/jinseo-jang/antigravity-plugin-cc/setupgit clone --depth 1 https://github.com/jinseo-jang/antigravity-plugin-ccWrote 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/commands/jinseo-jang/antigravity-plugin-cc/setup)<a href="https://agentmods.dev/commands/jinseo-jang/antigravity-plugin-cc/setup"><img src="https://agentmods.dev/badge/commands/jinseo-jang/antigravity-plugin-cc/setup.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 | $0.00019 | $0.00968 |
| Opus 5 | $0.00010 | $0.00484 |
| Sonnet 5 | $0.00004 | $0.00194 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade B, and why
setup scanned grade B with 1 finding 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- `--api-key` is never accepted or stored. Provide the key one of three ways, checked in this order: (1) **OS keychain** — `python -m keyring set cao gemini_api_key` (encrypted at rest, recommended); (2) **`GEMINI_API_KE What it actually says
Persist model/region defaults to $CAO_PLUGIN_DATA/defaults.json (or ~/.config/cao/defaults.json).
These are read fresh on every session start — no daemon restart needed.
Supported models:
| Model | Status | Notes |
|---|---|---|
gemini-3.7-flash |
GA | Default. |
gemini-3.6-flash |
GA | |
gemini-3.5-flash |
GA | |
gemini-3.5-flash-lite |
GA | |
gemini-3.1-pro-preview |
Public Preview | Narrower regional availability. |
Any other model string is rejected with -32602 before the worker starts. Region is your choice — global (the default) works for all models; pick any Vertex location where your model is available (Gemini's regional coverage keeps expanding).
Workflow:
- Use AskUserQuestion to ask the user which mode they want:
vertex(Vertex AI via ADC) orgemini_api_key(Gemini API key). - For
vertexmode, ask for--model(gemini-3.7-flash(default),gemini-3.6-flash,gemini-3.5-flash,gemini-3.5-flash-lite, orgemini-3.1-pro-preview) AND--location— recommendglobal(today the only region these Gemini-3 models are reliably served on); let the user type another Vertex region only if they know their model is available there (an unavailable region hangs until the worker-turn timeout, it does not fail fast). If they choose nothing,globalis used. Also ask for the GCP project id — tell the user they can leave it blank to let agy auto-detect it from ADC (quota_project_id) or the active gcloud config. Forgemini_api_keymode, ask for--modelonly (key stays inGEMINI_API_KEY; no location or project). - Run the companion, including
--project <id>only if the user gave one (omit it entirely to let auto-detect handle it):
!python "${CLAUDE_PLUGIN_ROOT}/scripts/cao-companion.py" --plugin-data "${CLAUDE_PLUGIN_DATA}" setup --mode [--model ] [--location ] [--project ]
- If the companion prints a rejection (unsupported model), relay the Options from the message via AskUserQuestion and retry with a corrected model.
- On success, confirm to the user that the defaults are saved and will apply to the next
/agy:implementinvocation.
Notes:
--api-keyis never accepted or stored. Provide the key one of three ways, checked in this order: (1) OS keychain —python -m keyring set cao gemini_api_key(encrypted at rest, recommended); (2)GEMINI_API_KEYenv var — export it in the shell that launches Claude Code, then restart it (Google's recommended location); (3) plaintext file~/.config/cao/gemini_api_key(chmod 600) — last resort, not encrypted.- If the companion warns that no key is found (gemini_api_key mode), point the user at the keychain command first, then the env-var or file fallbacks.
- Region is user-selectable.
globalis the default (works for all supported models) and is used when you omit--location; pick another Vertex region where your model is served. An unavailable model×region is not blocked at setup — it surfaces as a worker timeout, so choose a region where the model actually runs. gemini-3.1-pro-previewis the correct model code for Gemini 3.1 Pro (baregemini-3.1-prois a 404).- Defaults take effect on the next session (fresh-read, no daemon restart required).
- To clear defaults, delete
defaults.jsondirectly.
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 · 42 lines · 19 tokens per session scan B 01d14bc07e35
setup is a command published in the GitHub repository jinseo-jang/antigravity-plugin-cc (6 stars, last pushed 21d ago), licensed Apache-2.0. It adds 19 tokens to every session and 968 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.