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Verify RuView changes with Rust and Python tests, output hashes that detect processing changes, and firmware hash records. Packages test logs and audit evidence into a witness bundle that recipients can check with a single command.
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Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.
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Build and review numerical workflows for MATLAB or GNU Octave, including arrays, tables, graphics, tests, and MAT files. Helps plan release migrations, reproducible projects, and Python integration while checking runtime, compatibility, and license requirements.
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Build and operate bioinformatics workflows on Latch using Python, Nextflow, or Snakemake. Configure computing resources and input forms, move data, connect Registry records, and register, debug, launch, and monitor analysis runs.
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Check whether tests detect faults in changed Python code by deliberately modifying the changed lines. Add tests for missed behavior, confirm which tests fail against the previous code, and report measured evidence alongside any remaining gaps.
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Build Python code for Azure Cosmos DB's NoSQL API to create, read, update, delete, and query documents. Covers authentication, containers for storing data, partition keys for distributing records, throughput settings, asynchronous operations, and error handling.
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Create and organize Python tests with pytest. Use reusable setup, mock replacements for external services, controlled test time, and coverage reports to check normal behavior, failures, retries, and interactions between application components.
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Build a PowerPoint file with Python when no Office app is open. It uses python-pptx, applies a supplied firm template when available, and saves the deck locally with a path for collection by the surrounding workflow.
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Turn descriptions of 3D shapes and CAD tasks into FreeCAD Python scripts. Create models with adjustable dimensions, automate repeated operations with macros, convert meshes and solids, or build custom dialogs and workbench tools.
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Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.
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Generate concise Python examples for working with Microsoft Dataverse. Cover SDK installation, browser-based authentication, creating, reading, updating, and deleting records, plus bulk operations and retrieving results in pages using official example patterns.
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Generate Python code for Dataverse integrations with error handling, retries, logging, and shared connection management. Include type hints, usage examples, and queries that retrieve only the records and columns needed for the task.
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Turn a Python script into a beginner-friendly project by improving its structure and naming, adding comments that explain the reasoning, and creating a README.md tutorial covering setup, code logic, and example usage.
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Set up cognee and run a first Python example that stores information and retrieves it with a question. Covers virtual environments, API key configuration, optional packages, and basic troubleshooting.
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Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.
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Measure Python SDK test coverage, the code exercised by tests, and identify meaningful gaps in user-visible behavior. Report proposed scenarios or add authorized tests, then verify the changes and report remaining gaps alongside coverage results.
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Check Agent Skills for documentation structure and Python script behavior, then score quality and security posture. Get grades and improvement priorities, and use pass criteria in pre-commit checks or continuous integration, which runs checks automatically.
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Build AI applications in Python with Genkit, including conversational agents, generation workflows, streaming responses, and tool use. The skill also guides setup and debugging through traces, recorded details of model inputs, outputs, and actions.
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Guide shell, Python, and Node.js execution on the computer running a connected A0 CLI. It explains session reuse, interactive input, output retrieval, and recovery steps when execution permissions, timeouts, or disconnections interrupt the 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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Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.
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Use persistent virtual terminal sessions to keep directories and environment variables across commands, interact with Python or database consoles, and answer prompts. The skill explains sending input, reading responses, detecting prompts, and closing sessions when finished.
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Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.
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Resume Python tasks after execute_code_sandbox errors or timeouts by saving the attempted code as a script and running it through run_shell. The workflow includes dependency checks, execution timeouts, and verification of generated files such as Excel reports.
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