Use when building Hugging Face smolagents, code-executing agents, Python-action agents, Hub-shared tools, or lightweight agent prototypes that need sandboxing and provider flexibility.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use code actions when an agent benefits from loops, variables, data transforms, and multi-call tool composition. Treat generated code as untrusted unless it runs in a real sandbox.
transformers, or Ollama.CodeAgent and ToolCallingAgent.CodeAgent when actions need Python control flow; choose ToolCallingAgent for simple structured tool calls.Never treat LocalPythonExecutor as a security boundary for untrusted code. Use it only for trusted local experiments.
Generate a starter safety scaffold:
python scripts/smolagent_safety_scaffold.py --name research_agent
Review sandboxing-checklist.md before running any agent that executes model-written code.
| Requirement | Choose |
|---|---|
| Agent writes Python actions | CodeAgent |
| Strict JSON-like tool calls | ToolCallingAgent |
| Untrusted tasks or web data | Sandboxed executor |
| Local model experiments | TransformersModel or Ollama-compatible provider |
| Hosted provider flexibility | InferenceClientModel, LiteLLMModel, or OpenAI-compatible model |
| Mistake | Fix |
|---|---|
| Running untrusted code locally | Use Docker or managed sandbox |
| Exposing broad filesystem access | Mount a temporary workspace only |
| Giving tools vague docstrings | Make inputs, outputs, and side effects explicit |
| Letting agents import anything | Whitelist imports per task |
| Shipping without trace logs | Persist steps, code snippets, tool outputs, and final answer |
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Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
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Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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