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Found 37 Skills
Use when reducing model size, improving inference speed, or deploying to edge devices - covers quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimizationUse when ", " mentioned.
Efficient AI techniques including model compression, quantization, pruning, knowledge distillation, and hardware-aware optimization for production systems.
This skill should be used when the user says "clean up branches", "delete merged branches", or "prune stale branches". Use whenever the user mentions branch cleanup, pruning, or stale branch deletion — even if they don't say "clean-branches" explicitly.
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
Use when integrating Foundation Models framework, implementing on-device AI with Apple Intelligence, building tool-calling AI features, working with guided generation schemas, converting models with Core ML and coremltools, or running open-source LLMs on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM).
Interactive macOS system cleanup for any dev machine. Frees disk space by pruning caches, package managers, unused apps, stale dev artifacts, and more. Discovers what's installed rather than assuming a specific setup. Always consults the user before deleting anything. Use when the user asks to: clean up their Mac, free disk space, remove unused apps, prune caches, clean developer artifacts, or any disk space maintenance task.
macOS disk cleanup, cache pruning, stale file detection, and Downloads triage. TRIGGERS - disk space, cleanup, disk usage, stale files, cache clean, brew cleanup, forgotten files, Downloads cleanup, free space, storage, dust, dua, gdu, ncdu.
Use this skill when managing persistent user memory in ~/.memory/ - a structured, hierarchical second brain for AI agents. Triggers on conversation start (auto-load relevant memories by matching context against tags), "remember this", "what do you know about X", "update my memory", completing complex tasks (auto-propose saving learnings), onboarding a new user, searching past learnings, or maintaining the memory graph - splitting large files, pruning stale entries, and updating cross-references.
Use when running a scheduled mechanical maintenance pass over a repo — dead code removal, useless-test pruning, layering-violation fixes, duplicate unification, logic bugfixing, logic simplification, or excess-abstraction flattening. Invoked as `/cadence:maintenance-routine <archetype>`, typically from a scheduler (cron, a CI schedule, or an agent workbench's time trigger). One archetype per invocation; opens PRs and never merges. NOT for reviewing a PR (use pr-review), NOT for ad-hoc refactoring the user asked for directly.
Native Arrow filesystem integration with PyArrow. Optimized for Parquet workflows, zero-copy data transfer, predicate pushdown, and column pruning. Covers S3, GCS, HDFS with PyArrow datasets.
Consolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality.
Switches NixOS/Home Manager configurations, cleans old generations, and performs system maintenance. Use when running os/home switch, pruning the Nix store, or managing system generations.