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Import RoadRunner geometry and road data into a CARLA source build, then verify the map loads and supports driving. Handle tiled maps and generate and inspect pedestrian navigation areas for standard maps.
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Check whether a CARLA feature is usable in the current environment and has a verified procedure. Review known failures, inspect build settings, and identify differences between client, server, and source versions before planning work.
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Install Scenic, a language for describing scenarios with randomized conditions, into the Python environment used by CARLA. Check command-line tools and version compatibility, optionally download example scenarios and map assets, and save the installation paths.
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Set up ScenarioRunner, CARLA’s scenario engine, with a branch matched to your simulator version. Install dependencies, configure Python search paths, and verify imports to address setup failures before running scenarios.
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Evaluate a driving agent on CARLA Leaderboard routes after checking the required setup. Select routes and repetitions, save driving scores and infractions, record runs, and resume interrupted evaluations from a saved checkpoint.
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Downloads CARLA release packages, nightly builds, source code, or Docker images, then reports their locations and environment variables for follow-up setup. Supports extra maps and resumable downloads, helping prepare the files needed for subsequent installation or building.
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Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.
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Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.
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Design and implement command-line tools with subcommands, options, interactive prompts, and progress indicators. Get command structures, configuration handling, clear error messages, and shell completion scripts, with guidance for automated use and testing across operating systems.
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Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.
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Create, edit, repair, and convert spreadsheet files, including Excel workbooks and CSV data. Clean messy tables, add formulas and formatting, preserve existing template styles, and recalculate formulas to check for errors before delivery.
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Inspect and manage Tencent Cloud resources with tccli, its command line tool. Covers servers, networking, DNS, certificates, permissions, monitoring, and application deployment, with supporting scripts for login and identifying the current cloud instance.
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Create presentations from a plain-language brief using preset visual themes. Compare three template layouts and one custom design per slide, edit the HTML presentation in a browser, and export to PPTX or PDF.
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Guides a Worktrunk release from testing and change review to version updates, release notes, tagging, and publication checks. Includes reviews of potential data loss, documentation accuracy, and contributor credits before the release is published.
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Run OpenClaw tests and reproduce bugs on remote machines through Crabbox. Choose a suitable execution provider, verify realistic user workflows, and report the actual provider, environment ID, logs, and timing.
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Set up OpenRig to run a team of coding agents, including Claude Code and Codex. Open the team interface, pass project goals to its operator, reconnect to agents, and follow assigned work through to its results.
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Automate Chrome using an existing login session through CDP, Chrome’s remote control interface. Open pages, click elements, enter text, extract page content, run JavaScript, and inspect authentication tokens with agent-browser.
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Check PDF page counts, document details, and required text using command-line tools. This skill provides a verification workflow for environments without Python PDF libraries, including text extraction from selected pages and searches for expected sections.
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Extract PDF text when read_file returns unreadable binary content or little usable output. Try pdftotext with layout preservation, then use pdfplumber as an alternative when the command is unavailable or produces insufficient text.
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Diagnose Claude in Chrome connection failures on macOS by checking native messaging hosts, the processes linking Chrome to Claude, and their settings. It guides fixes for competing configurations, stale wrappers, and session conflicts, including restart steps.
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Guides fuzz testing for Ruby libraries and gems with Ruzzy, using varied inputs to find failures. Covers pure Ruby and C extensions, including test harnesses, memory error detection, environment setup, and crash reproduction.
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Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.
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Set up libFuzzer to test C/C++ code with varied inputs guided by which code paths run. Build test harnesses, manage sample inputs and dictionaries, enable error detectors, and reproduce crashes or investigate stalled coverage.
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Review native C/C++ applications and services for memory errors, integer overflows, and race conditions, where concurrent operations interfere. Coordinate multiple reviewers and produce findings with severity ratings, alongside reports of missing or unverified review coverage.
中文简介正在准备中。当前显示英语简介。