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Found 33 Skills
Invoke parallel document-specialist agents for external web searches and documentation lookup
Use when batch-resolving approved todos, especially after code review or triage sessions
Use this skill when working with Mass Entity, MassEntity, Mass AI, MassProcessor, MassFragment, MassTag, MassObserver, MassSpawner, MassCrowd, Mass ECS, entity archetype, ForEachEntityChunk, FMassEntityQuery, FMassEntityManager, ISM crowd, or large-scale entity simulation in Unreal Engine. See references/mass-entity-patterns.md for processor and observer templates. See references/mass-fragment-reference.md for built-in fragment types.
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
Automatically fix ESLint errors by modifying code to comply with linting rules. For small codebases (≤20 errors), fixes directly. For larger codebases (>20 errors), spawns parallel agents per directory for efficient processing. Never disables rules or adds ignore comments.
Delegate tasks to the cost-effective opencode/glm-5 model. Use when you need inexpensive task execution, simple research, or delegating work that doesn't require the most powerful models.
Delegate tasks to parallel worktree agents using worktrunk (wt). Use when asked to "spawn agents", "run in parallel", "delegate to worktrees", or split work across multiple Claude/OpenCode sessions.
Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to Google Sheets (via Rube) or CSV. Supports calibration mode for prompt refinement.
Autonomous project gardening by a coordinated team of agents. Spawns a team of gardeners that each run the `garden` skill in parallel, coordinating via a shared task list to avoid duplicate work. Use when the user wants to tend multiple small issues in one pass. Invoke with /gardeners.