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Prepare a clean Doris package and run Docker regression tests, which check whether changes break existing behavior. Restore configuration and startup files from Git, build the test image, and investigate failures using configuration checks and runtime logs.
Create, migrate, refine, or debug CARLA vehicle assets with Blender and Unreal. Use real vehicle references to guide geometry and materials, configure wheels, lights, and physics, and deliver editable sources with evidence from runtime validation.
Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.
Build and maintain machine learning apps on Hugging Face Spaces. Choose a suitable app framework and computing hardware, load models, deploy files, and check logs and outputs to diagnose build or runtime problems.
Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.
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.
Check for GSD updates, preview the changelog, and install the selected release after confirmation. Additional options synchronize managed skills across runtime environments, restore local customizations through comparison, or select a release candidate for installation.
Tests a specific Convex feature with sample data and simulated users, without deployment. It checks expected results, rejects unauthorized callers, and verifies that queries return only data belonging to the current user.
Review 72 hours of Convex deployment health events and trace read limits or conflicting writes to the responsible code. Receive prioritized performance, cost, and reliability findings with runtime evidence, source locations, and concrete fixes.
Apply ClawHub-specific rules when developing with Convex, including deployment targeting, runtime checks, data migrations, search performance, and skill statistics. It also directs specialized work to the appropriate companion skills.
Plan how agents retain active work across conversation compaction, context loss, and restarts. Organize restoration packets that preserve purpose, progress, ownership, and constraints, distinguish resume from fork or rebuild, and check whether work actually resumes correctly.
Plan and verify OpenRig startup, shutdown, and recovery. Compare reported status with actual running sessions, distinguish recovery outcomes, and define reboot tests that expose partial failures instead of hiding them behind a fresh launch.
Guide changes to RDD reviewer integrations through a shared Go contract, a common set of processing rules. Define adapter boundaries, validate result handling, and check that runtime migrations preserve existing review outcomes.
Independently rerun an unchanged bug reproduction package before repair. Check whether compiler diagnostics or runtime assertions support the exact reported claim, then preserve execution evidence and record a confirmed, unconfirmed, or inconclusive judgment.
Prepare 3D models for browser games by cleaning source assets, exporting GLB or glTF, and optimizing geometry and textures. It also guides collision shapes, distance-based detail levels, and runtime checks for the chosen engine.
Add focused runtime logs to a macOS app and verify that expected events appear. Uses Apple’s Logger to trace window, sidebar, menu, and background activity while keeping logs concise and excluding sensitive data.
Diagnose macOS app launch and trust failures by inspecting code signatures, app permissions, and Gatekeeper checks. Identify the failure category and get a minimal validation or repair sequence that distinguishes local development issues from distribution requirements.
Investigate slow rendering, uneven scrolling, and excessive updates in SwiftUI apps by reviewing code first. When needed, use runtime measurements to assess likely causes, prioritize fixes, and compare performance before and after changes.
Check macOS apps before distribution by reviewing bundle structure, code signing, and notarization readiness. Identify missing prerequisites and distinguish packaging problems from trust-policy issues, with concrete validation or repair steps to follow.
Test iPolloWork's handling of corporate certificates in a Daytona Windows sandbox. Set up valid and broken HTTPS certificate chains, then verify that the desktop app and runtimes it starts use certificates trusted by Windows.
Build, integrate or audit rigged 3D game characters and monsters, including skeletons, animations, equipment and collision rules. Connect assets to gameplay and provide an interactive inspector to verify actions, equipment states and runtime behavior.
Start the introductory guidance for Ouroboros by sending `ooo` or asking to begin. This entry point passes control to the welcome skill, including before runtime-specific setup has been completed in the Marketplace plugin.
Diagnose failures in an application or while it is running. Use it to investigate an application problem or a runtime error, meaning an error that occurs during execution.
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.