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* feat: add initial documentation for Plano Agent Skills * feat: readme with examples * feat: add detailed skills documentation and examples for Plano --------- Co-authored-by: Adil Hafeez <adil.hafeez@gmail.com>
66 lines
2.5 KiB
Markdown
66 lines
2.5 KiB
Markdown
---
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title: Use `planoai init` Templates to Bootstrap New Projects Correctly
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impact: MEDIUM
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impactDescription: Starting from a blank config.yaml leads to missing required fields and common structural mistakes — templates provide validated, idiomatic starting points
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tags: cli, init, templates, getting-started, project-setup
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---
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## Use `planoai init` Templates to Bootstrap New Projects Correctly
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`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.
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**Available templates:**
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| Template ID | What It Demonstrates | Best For |
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| `sub_agent_orchestration` | Multi-agent routing with specialized sub-agents | Building agentic applications |
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| `coding_agent_routing` | Routing preferences + model aliases for coding workflows | Claude Code and coding assistants |
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| `preference_aware_routing` | Automatic LLM routing based on task type | Multi-model cost optimization |
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| `filter_chain_guardrails` | Input guards, query rewrite, context builder | RAG + safety pipelines |
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| `conversational_state_v1_responses` | Stateful conversations with memory | Chatbots, multi-turn assistants |
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**Usage:**
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```bash
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# Initialize with a template
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planoai init --template sub_agent_orchestration
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# Initialize coding agent routing setup
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planoai init --template coding_agent_routing
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# Initialize a RAG with guardrails project
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planoai init --template filter_chain_guardrails
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```
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**Typical project setup workflow:**
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```bash
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# 1. Create project directory
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mkdir my-plano-agent && cd my-plano-agent
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# 2. Bootstrap with the closest matching template
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planoai init --template preference_aware_routing
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# 3. Edit config.yaml to add your specific models, agents, and API keys
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# (keys are already using $VAR substitution — just set your env vars)
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# 4. Create .env file for local development
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cat > .env << EOF
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OPENAI_API_KEY=sk-proj-...
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ANTHROPIC_API_KEY=sk-ant-...
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EOF
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echo ".env" >> .gitignore
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# 5. Start Plano
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planoai up
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# 6. Test your configuration
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curl http://localhost:12000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}'
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```
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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.
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Reference: https://github.com/katanemo/archgw
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