▸case-06 We need a standard base class in Python for agent tools that outputs valid dictionary definitions for LLM tool selection. Write a base Tool class and show how properties and execution interface are structured. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-14 Write a method for a context manager that traverses a local directory and collects files into the context buffer while preventing context window overload. | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-03 I need to implement a state persistence mechanism for our autonomous developer agent so users can pause, save, and restore long-running tasks. Please write a Python manager class that serializes the conversation history, workspace context, and version control status to local JSON files, along with restore functionality. Format the response as a runnable code snippet accompanied by a high-level explanation of the saved payload structure. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-22 We are creating a standard CRUD web backend for managing user accounts and billing subscriptions using FastAPI and SQLAlchemy. Should we structure the API endpoints as autonomous tool schemas for an LLM agent loop? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-01 I am building a coding assistant and need to design a file editing tool that safely replaces code snippets without unintended collateral edits. Could you write a Python class for a search-and-replace edit tool that validates matching occurrences before writing, along with a structured tool schema? Please provide the complete class code and a summary of how error handling and match verification are structured. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-04 We are building an agent loop in Python where an LLM can iteratively invoke local tools until a task is complete. Developers often default to recursive calls or loose function wrappers without tracking standard message structures. Write a Python class for executing this agent loop. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-10 Write an async Python tool class using Playwright for web browsing tasks. We need navigation and click methods that capture visual feedback for the agent. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-21 We want to train Llama-3 8B on custom Python codebase repositories using LoRA fine-tuning and Unsloth. Can you provide the training hyperparameter setup, dataset formatting, and PEFT configuration? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-12 Design an agent class in Python that uses vision models to locate UI components from screenshots when CSS selectors fail, and then interacts with those visual elements. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-07 Write a ReadFileTool implementation in Python that reads local files line by line. Ensure it cleanly catches missing file errors without throwing unhandled system exceptions. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-08 We want to construct a sandboxed command runner class in Python that executes developer commands inside a target workspace directory. We need validation logic to block path traversal attacks outside the designated workspace path. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-17 Create a permission configuration mapping in Python that assigns risk levels and approval requirements to typical agent tools ranging from file reading to formatting disks. | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-13 Implement a Python ContextManager class for an AI editor that accumulates attached files and formats them into structured context blocks for prompt injection. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-11 We need an async Python method for a Playwright browser tool that extracts clean page body text without script or style tags for context feeding. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-15 Write a Python agent method that dynamically generates and registers new Model Context Protocol (MCP) server capabilities at runtime when requested by the user. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-09 When executing terminal scripts from an autonomous agent inside Python's subprocess module, we need to prevent commands from writing files into the user's main home folder. Show how to configure the subprocess execution environment dictionary. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-05 When building an agent for complex software engineering tasks, using a single LLM model for every subtask can be slow and expensive. Developers often hardcode one model across the entire application. Provide a Python model selector class that routes planning, reasoning, and code tasks across specialized model tiers. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-02 We are implementing a security module for an autonomous AI agent that executes terminal commands and modifies workspace files. Can you construct a Python permission management system that enforces tiered authorization policy levels (like immediate execution, session-based user approval, or mandatory confirmation) and checks command safety? Please output the full Python implementation with error handling and risk assessment logic. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-18 Implement a permission checking method for an AI agent tool executor that supports 'ask once per session' approval caching for file writing tools. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-16 In an agent security module, implement a risk assessment helper function that evaluates incoming terminal commands before presenting approval dialogs to users. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-19 Write a file content verification step for an agent edit tool that verifies target search strings prior to applying text replacements. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-20 We are setting up a retrieval augmented generation (RAG) pipeline for document search using Qdrant and SentenceTransformers. Should we implement an autonomous agent loop with function calling tools and tool execution state machines to retrieve search results? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |