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Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.
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Turn a research proposal into an experiment plan tied to specific paper claims. Define comparisons, evaluation measures, tests that remove individual components, run order, and compute estimates, with a written plan and tracker for execution.
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Run MNN checks and regression tests on a host computer or Android device, with stage summaries and failure logs. A separate workflow benchmarks LLM prompt processing and token generation on real iPhones and iPads, including branch comparisons.
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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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Convert language models to GGUF and reduce their size through quantization, which stores model weights at lower precision. Guides cover compression choices, local inference on CPUs and Apple Silicon, and integration with tools such as Ollama.
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Plan and review new Omnigent harness integrations, the adapters connecting agent runtimes to the platform. Compare SDK and subprocess approaches with native interface wrappers, using capability matrices and checklists for implementation and testing.
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Move an existing mod to a newer game version or another mod loader while preserving its behavior and values. Organize source comparisons, scripted data extraction, tests, visual checks, and handoff documents for a long migration.
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Manage agents on a host by listing their capabilities, reading source code, and downloading or deploying agents in the smith namespace. Check available language model connections and their roles without exposing API keys.
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Research key competitors and produce cited profiles, a comparison table, and implications for a product decision. Separate facts from inferences and assumptions, highlight evidence quality, and create a consistent baseline for later comparisons.
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Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.
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Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.
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Prepare single-cell or single-nucleus RNA-seq analysis by checking inputs and selecting workflows that turn FASTQ sequencing files into count matrices. Route quality checks, cell annotation, clustering, and UMAP visualization to the embedded downstream skill.
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Find the right workflow for next-generation sequencing data by inspecting files and clarifying only missing setup details. Receive a recommended analysis skill, checks of available tools, and plans for software installation and reference data.
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Plan and implement features with a workflow that adapts to their complexity. It connects testable requirements to tasks, tests, and small commits, then checks the finished work against the specification through an independent verification pass.
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Create fluid animations where rounded shapes merge as they approach and separate as they move apart. SVG filters combine blur with a cutoff effect to form soft, connected shapes for backgrounds, loaders, or cursor effects.
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Turn an agreed product direction into a technical specification before coding. Create architecture and flow diagrams, define failure handling and input validation, and map tests to the behavior engineers need to implement.
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Clarify responsibilities in a TeamHarness team, including planning, task execution, result acceptance, and runtime administration. Use the role boundaries to identify who owns each action and how participants should handle sensitive information.
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Manage an AI team's project plans and recorded progress, including task dependencies and repeated improvement cycles. Track accepted results, identify work ready to start, pause or resume projects, and prepare status reports for requesters.
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Manage AI agent Workers by creating roles, selecting runtimes, starting or stopping Workers, and updating their skills. It also covers runtime migration, direct mentions between Workers, and access to the QwenPaw console.
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Turn ready project tasks into clear instructions for AI workers, with defined ownership, deliverables, acceptance criteria, and reporting rules. Handle assignment notifications and check submitted results before passing them to the project management workflow for acceptance.
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Manage an assigned Worker task from acceptance through delivery using taskflow. Keep work and optional progress notes in the task directory, submit structured results with deliverable paths, and report completion or blockers to the coordinator.
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Delegate Git operations to a Manager when a worker lacks credentials. The skill explains how to prepare command requests, receive results, and synchronize shared files, covering repository downloads, branches, commits, pushes, and other Git tasks.
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Helps a QwenPaw Worker choose between direct work, short parallel checks, and temporary agents with custom roles. It guides dependent task execution, shared files, progress updates, result merging, and removal of temporary agents after the work ends.
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Organize research reports around what readers need to understand or decide. Choose structures for overviews, comparisons, background investigations, or event tracking, with specialized formats for academic, medical, legal, and policy reports that make evidence and uncertainty explicit.
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