plano/skills/rules/cli-init.md

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Use `planoai init` Templates to Bootstrap New Projects Correctly MEDIUM Starting from a blank config.yaml leads to missing required fields and common structural mistakes — templates provide validated, idiomatic starting points cli, init, templates, getting-started, project-setup

Use planoai init Templates to Bootstrap New Projects Correctly

planoai init generates a valid config.yaml from built-in templates. Each template demonstrates a specific Plano capability with correct structure, realistic examples, and comments. Use this instead of writing config from scratch — it ensures you start with a valid, working configuration.

Available templates:

Template ID What It Demonstrates Best For
sub_agent_orchestration Multi-agent routing with specialized sub-agents Building agentic applications
coding_agent_routing Routing preferences + model aliases for coding workflows Claude Code and coding assistants
preference_aware_routing Automatic LLM routing based on task type Multi-model cost optimization
filter_chain_guardrails Input guards, query rewrite, context builder RAG + safety pipelines
conversational_state_v1_responses Stateful conversations with memory Chatbots, multi-turn assistants

Usage:

# Initialize with a template
planoai init --template sub_agent_orchestration

# Initialize coding agent routing setup
planoai init --template coding_agent_routing

# Initialize a RAG with guardrails project
planoai init --template filter_chain_guardrails

Typical project setup workflow:

# 1. Create project directory
mkdir my-plano-agent && cd my-plano-agent

# 2. Bootstrap with the closest matching template
planoai init --template preference_aware_routing

# 3. Edit config.yaml to add your specific models, agents, and API keys
#    (keys are already using $VAR substitution — just set your env vars)

# 4. Create .env file for local development
cat > .env << EOF
OPENAI_API_KEY=sk-proj-...
ANTHROPIC_API_KEY=sk-ant-...
EOF

echo ".env" >> .gitignore

# 5. Start Plano
planoai up

# 6. Test your configuration
curl http://localhost:12000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}'

Start with preference_aware_routing for most LLM gateway use cases and sub_agent_orchestration for multi-agent applications. Both can be combined after you understand each independently.

Reference: https://github.com/katanemo/archgw