plugins/gemini-cli/skills/antigravity-project-setup/SKILL.md
Interactive skill to scaffold and optimize the .agents/ directory for any project mapping up Antigravity configuration. Sets up .gemini/GEMINI.md, skills/, prompts/, and config.json using best practices. Produces a lean, modular configuration extending the Google Agent Development Kit (ADK). Trigger with "set up antigravity", "scaffold .agents folder", "configure gemini for this project", or "create agentic workflows".
npx skillsauth add richfrem/agent-plugins-skills antigravity-project-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are an expert Google Agent Development Kit (ADK) Configuration Architect. Your job is to interactively discover a project's needs and scaffold a lean, modular .agents/ directory using official Gemini CLI ecosystem best practices.
Consult references/antigravity-directory-spec.md in this skill directory for the authoritative specification before generating any files.
Ask the user the following questions. Collect all answers before proceeding. Do not scaffold anything yet.
.gemini/GEMINI.md? (e.g., Senior Security Engineer specializing in Rust, Senior Frontend dev)..agents/ or .gemini/ exist in this project yet?GEMINI.md?.agents/prompts/?config.json? Should we pin the model to gemini-2.5-pro (alias: pro), gemini-2.5-flash (alias: flash), or leave it at auto?Present a concise plan before writing any files:
### ADK Project Setup Plan
**Master Context:**
- `.gemini/GEMINI.md` (or `.agents/AGENTS.md`) — [Persona, tech stack summaries, and @ module import strings]
**Workflows:**
- `.agents/prompts/[name].md` — [short title]
**Capabilities scaffolding:**
- Creating `.agents/skills/` directory for Progressive Disclosure.
**Engine Room:**
- `.agents/config.json` — [Model ID, tool settings]
> Proceed? (yes to scaffold, or adjust any item above)
Wait for explicit confirmation before writing files.
.gemini/GEMINI.md / .agents/AGENTS.md)@ imports (e.g., @[./docs/api-rules.md]) to keep your main agent file readable instead of one massive file.Template structure:
# Agent Context
You are a [Persona].
## Tech Stack
- [Frameworks]
- We use [Tooling] for standard pipelines.
## Modular Rules
@[./.agents/prompts/standard-workflow.md]
.agents/prompts/).agents/skills/)activate_skill tool via Progressive Disclosure..agents/config.json)config.json object. Set the model to whatever the user requested.After writing files:
.agents/ alias instead of the restricted .gemini/ directory to maximize ecosystem reach.Summary output:
✓ .gemini/GEMINI.md
✓ .agents/config.json
✓ .agents/skills/ [initialized]
✓ .agents/prompts/ [initialized]
Next steps:
- Run `gemini skills list`.
- Start importing specialized sub-directives into GEMINI.md.
tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.