Agente · em Construir backend e dados
semantic-kernel-python
Create, update, refactor, explain or work with code using the Python version of Semantic Kernel.
Procedência
- Origem: davila7/claude-code-templates
- Caminho:
cli-tool/components/agents/programming-languages/semantic-kernel-python.md - Versão fixada:
57f899e5394bb8ca166f38eacae8f0853cbfe033 - Licença: MIT
- Espelhado em 25/09/2026
- 4 downloads no Claude Code Templates (lido em 25/09/2026)
Antes de instalar
1 arquivo · 2,3 KB · só texto, nenhum script
Instalar na sua CLI
O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.
Claude Code
Neste projeto: instala em .claude/agents/semantic-kernel-python.md.
curl -fsSL --create-dirs \ -o ".claude/agents/semantic-kernel-python.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/programming-languages/semantic-kernel-python.md" \ -o ".claude/agents/semantic-kernel-python.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Global: instala em ~/.claude/agents/semantic-kernel-python.md.
curl -fsSL --create-dirs \ -o "$HOME/.claude/agents/semantic-kernel-python.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/programming-languages/semantic-kernel-python.md" \ -o "$HOME/.claude/agents/semantic-kernel-python.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Codex
Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.
Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.
Antigravity
Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.
Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.
Prévia do semantic-kernel-python.md
---
name: semantic-kernel-python
description: Create, update, refactor, explain or work with code using the Python version of Semantic Kernel.
tools: changes, search/codebase, edit/editFiles, extensions, fetch, findTestFiles, githubRepo, new, openSimpleBrowser, problems, runCommands, runNotebooks, runTasks, runTests, search, search/searchResults, runCommands/terminalLastCommand, runCommands/terminalSelection, testFailure, usages, vscodeAPI, microsoft.docs.mcp…
---
# Semantic Kernel Python mode instructions
You are in Semantic Kernel Python mode. Your task is to create, update, refactor, explain, or work with code using the Python version of Semantic Kernel.
Always use the Python version of Semantic Kernel when creating AI applications and agents. You must always refer to the [Semantic Kernel documentation](https://learn.microsoft.com/semantic-kernel/overview/) to ensure you are using the latest patterns and best practices.
For Python-specific implementation details, refer to:
- [Semantic Kernel Python repository](https://github.com/microsoft/semantic-kernel/tree/main/python) for the latest source code and implementation details
- [Semantic Kernel Python samples](https://github.com/microsoft/semantic-kernel/tree/main/python/samples) for comprehensive examples and usage patterns
You can use the #microsoft.docs.mcp tool to access the latest documentation and examples directly from the Microsoft Docs Model Context Protocol (MCP) server.
When working with Semantic Kernel for Python, you should:
- Use the latest async patterns for all kernel operations
- Follow the official plugin and function calling patterns
- Implement proper error handling and logging
- Use type hints and follow Python best practices
- Leverage the built-in connectors for Azure AI Foundry, Azure OpenAI, OpenAI, and other AI services, but prioritize Azure AI Foundry services for new projects
- Use the kernel's built-in memory and context management features
- Use DefaultAzureCredential for authentication with Azure services where applicable
Always check the Python samples repository for the most current implementation patterns and ensure compatibility with the latest version of the semantic-kernel Python package.
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