/detect-framework
Detect Python agent frameworks from code imports and map them to Omnigent executor types. Load when the user has existing agent code to integrate.
$ npx -y skills add omnigent-ai/omnigent --skill detect-framework --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/detect-framework
Context preview
The summary Claude sees to decide when to auto-load this skill.
Detect Python agent frameworks from code imports and map them to Omnigent executor types. Load when the user has existing agent code to integrate.
SKILL.md
detect-framework.SKILL.mdname: detect-framework
description: Detect Python agent frameworks from code imports and map them to Omnigent executor types. Load when the user has existing agent code to integrate.
Framework Detection
When the user has existing Python code they want to integrate into Omnigent, detect the framework from import statements and recommend the appropriate executor type.
Detection Procedure
1. Ask the user for the path to their agent code (or look for Python files in the current directory if filesystem access is enabled).
2. Scan Python files for import patterns. Check in this priority order:
| Import pattern | Framework | Executor type | |---------------|-----------|---------------| | `import anthropic` or `from anthropic` + agent patterns (e.g. `Agent`, `tool`, system prompt setup) | Claude SDK | `claude_sdk` | | `import openai` or `from openai` + agents patterns (e.g. `Agent`, `Runner`, `function_tool`) | OpenAI Agents SDK | `agents_sdk` | | `from langgraph` or `import langgraph` | LangGraph | Not natively supported yet | | `from deepagents` or `import deepagents` | DeepAgents | Not natively supported yet | | `from langchain` or `import langchain` | LangChain | Not natively supported yet | | `from crewai` or `import crewai` | CrewAI | Not natively supported yet | | `from autogen` or `import autogen` | AutoGen | Not natively supported yet | | None of the above | Unknown | Not natively supported yet |
3. Report what you found and recommend the executor type.
What to generate for each executor type
`llm` (default — no existing code)
Generate a standard agent directory:
executor:
type: llm # or omit entirely (llm is the default)
`claude_sdk`
The user's Claude SDK code runs directly. Generate config that points to their entry module:
executor:
type: claude_sdk
`agents_sdk`
The user's OpenAI Agents SDK code runs directly:
executor:
type: agents_sdk
Asking about unsupported frameworks
If the user's framework is not natively supported, let them know:
- Explain that Omnigent does not currently have a supported executor for that framework.
- Offer to show them a pre-filled GitHub issue URL requesting first-class
support for their framework.
- If they want to start fresh instead, recommend generating a standard `llm` agent.
- The issue URL format: `https://github.com/dbczumar/omnigent/issues/new?title=...&body=...`
Read more
name: detect-framework description: Detect Python agent frameworks from code imports and map them to Omnigent executor types. Load when the user has existing agent code to integrate.
Framework Detection
When the user has existing Python code they want to integrate into Omnigent, detect the framework from import statements and recommend the appropriate executor type.
Detection Procedure
1. Ask the user for the path to their agent code (or look for Python files in the current directory if filesystem access is enabled).
2. Scan Python files for import patterns. Check in this priority order:
| Import pattern | Framework | Executor type | |---------------|-----------|---------------| | `import anthropic` or `from anthropic` + agent patterns (e.g. `Agent`, `tool`, system prompt setup) | Claude SDK | `claude_sdk` | | `import openai` or `from openai` + agents patterns (e.g. `Agent`, `Runner`, `function_tool`) | OpenAI Agents SDK | `agents_sdk` | | `from langgraph` or `import langgraph` | LangGraph | Not natively supported yet | | `from deepagents` or `import deepagents` | DeepAgents | Not natively supported yet | | `from langchain` or `import langchain` | LangChain | Not natively supported yet | | `from crewai` or `import crewai` | CrewAI | Not natively supported yet | | `from autogen` or `import autogen` | AutoGen | Not natively supported yet | | None of the above | Unknown | Not natively supported yet |
3. Report what you found and recommend the executor type.
What to generate for each executor type
`llm` (default — no existing code)
Generate a standard agent directory:
executor: type: llm # or omit entirely (llm is the default)
`claude_sdk`
The user's Claude SDK code runs directly. Generate config that points to their entry module:
executor: type: claude_sdk
`agents_sdk`
The user's OpenAI Agents SDK code runs directly:
executor: type: agents_sdk
Asking about unsupported frameworks
If the user's framework is not natively supported, let them know:
- Explain that Omnigent does not currently have a supported executor for that framework.
- Offer to show them a pre-filled GitHub issue URL requesting first-class
support for their framework.
- If they want to start fresh instead, recommend generating a standard `llm` agent.
- The issue URL format: `https://github.com/dbczumar/omnigent/issues/new?title=...&body=...`
Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.
Repo: omnigent-ai/omnigent
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