「code-execution」の検索結果

Guide safe use of code_execution_remote for shell-backed execution on the connected A0 CLI host. Use when the user asks for their computer, local terminal, CLI host, remote shell, not Docker, or host-side Python/Node/terminal commands.

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Fallback workflow for running code via file write and shell when sandbox execution fails

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Handle code execution failures with fallback strategies and anchored workspace paths

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Fallback workflow for executing Python code when execute_code_sandbox fails repeatedly

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Detect tool misuse and unexpected code execution via dialogue testing. Use when the agent exposes file, code-execution, or network tools.

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Detect command injection, eval/exec usage, remote execution, or arbitrary code loading.

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Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.

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Build, run, debug, and operate applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments that run inside a container with up to 8-hour lifetimes. Triggers on: Lambda MicroVMs, Firecracker isolation, snapshot-resumable compute, suspend/resume, sandboxed or untrusted code execution, AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, isolated security scanners, long-lived sessions, or port-listening servers (gRPC, WebSocket, custom TCP). For standard event-driven Lambda functions, use the aws-lambda skill instead.

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Execute Python code locally with marketplace API access for 90%+ token savings on bulk operations. Activates when user requests bulk operations (10+ files), complex multi-step workflows, iterative processing, or mentions efficiency/performance.

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Decision rubric for when an LM agent should write-and-run code (Program-of-Thought / code interpreter) versus reason in natural language: classify each step as deterministic- computable (emit + execute code, feed the result back) vs judgment (stay in prose). Use when designing or debugging an agent step that does arithmetic/parsing/data transforms, when prose reasoning hallucinates a computation (under-coding), or when a sandbox round- trip is wasted on a judgment task (over-coding). Search keywords: code interpreter, agent does math wrong, calculator hallucination, when to run code vs reason, program of thought, PoT, tool vs reasoning.

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Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

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E2B is a cloud platform that gives AI agents secure, isolated sandboxes — ephemeral Linux VMs with a filesystem, package managers, and internet access — to run AI-generated code safely. Use when a user wants an agent to execute Python or JavaScript, install packages, read/write files, or run a long process without risking the host machine, or wants to wire code execution into a LangChain, CrewAI, or custom agent loop.

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Use when an editor MCP server (unity, godot, unreal, blender) or the browser MCP shows up in your tool list - what each one can observe or change, how to check it is attached to YOUR task workspace, why the repo's tests still decide, the Blender code-execution caution, and the headless fallback when none is connected.

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Read, create, edit, and verify PDFs with local code-execution tools and rendered-page inspection. Use whenever PDF layout, forms, extraction, merging, or final visual quality matters.

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Create, inspect, and edit Word documents (`.docx`) with local code-execution tools. Use for document drafting, structured edits, tables, comments, and layout-sensitive Word deliverables.

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Senior {{engine_name}}/{{engine_code_language}} developer that performs direct, ad-hoc code tasks without a formal plan — and the code-execution engine other unikit skills and agents delegate to. Use it whenever the user asks to do something in code right now, e.g. "add this method to this class", "refactor this function", "write a script that does X", "change this code", "optimize this class". Phrases like "without a plan", "no plan", "just do it", or "directly" are strong signals to use this skill rather than planning. Covers writing, refactoring, optimizing, and discussing {{engine_code_language}} code and patterns (DI, ECS, MVC, state machines, event systems). For a larger multi-step feature, plan it with /unikit-plan and build it with /unikit-implement — this skill is for direct, unplanned code work.

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MS17-010 (EternalBlue) is a critical vulnerability in Microsoft's SMBv1 implementation that allows remote code execution. Originally discovered by the NSA and leaked by the Shadow Brokers in 2017, it

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