AI Toolkit & Copilots

IntaGrin is designed to pair well with your IDE's AI agents — inta copilot generates rule/skill files that teach your assistant the framework's conventions instead of relying on it to infer them from scratch.

1. Zero-Code Scaffolding (inta copilot)

If you use Cursor, GitHub Copilot, or Claude Code, do not write ai.yaml by hand.

Run the following command in your project root:

inta copilot

The CLI will generate rule files (e.g., .cursor/rules/intagrin-agent.mdc) that describe IntaGrin's YAML schema and conventions to your IDE's LLM — review the generated rules before relying on them.

2. The intagrin-compile Skill (Bidirectional Sync)

When you run inta copilot, it installs the intagrin-compile skill into your selected IDE integration.

How to use it:

1. Create a plain-english file called blueprint.md in your project.
2. Describe your product vision: "I need a triage agent that talks to a billing agent."
3. Open your IDE's AI chat and say: "Compile my architecture."

The IDE Agent will read your blueprint.md, read your existing ai.yaml, and intelligently prompt you if they are out of sync. It acts as an interactive software architect, automatically writing your ai.yaml and scaffolding your .jinja2 prompt files for you — and, per the skill's own instructions, always finishes by running inta verify and fixing anything it reports before calling the task done, rather than assuming the config it wrote is correct.

3. The CLI Compiler

If you prefer CI/CD or terminal workflows, you can bypass the IDE agent and use the native CLI compiler. inta compile writes ai.yaml itself, so — unlike inta dev/inta run, which read the key from a project's already-written ai.yaml/.env — it needs a model key available before that file exists: either export it in your shell (e.g. GEMINI_API_KEY=...), or drop it in a .env file next to blueprint.md. Running without one fails fast with a clear IG-CLI-008 error rather than a raw stack trace.

inta compile blueprint.md

This performs the same bidirectional diff-merge between your Markdown spec and your existing ai.yaml, preserving manual API keys and config it wasn't asked to change. Unlike free-form generation, the compiled result is never written to disk unchecked:

• Validated, not hoped. Every compile is checked against the real AppConfig schema and against the router-condition grammar (routers[].condition only supports bare state-key names and comparisons — a generated state.get(...) condition is caught here, not discovered later as a router that silently never fires). A config that doesn't validate is self-healed by feeding the exact error back to the model, up to 2 retries; if it still doesn't validate, ai.yaml is not written and the command exits with an error — a blocked compile beats a broken one.
• Scaffolds what it references. Any system_prompt_file or local tool the compiled config points at gets a minimal placeholder if it doesn't already exist — never overwriting a file you've already edited by hand.
• Verifies in the same flow. On success, inta compile automatically runs inta verify against the result, so cycle/cost/condition-syntax feedback shows up immediately instead of requiring a separate command you have to remember to run. If you're updating an existing, already-running ai.yaml, it also points you at inta simulate --config ai.yaml to check what the change would actually do against real session history before you rely on it — see Production Deployment.