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Topic: skills

17,247 skills in this topic.

loom-serialization
cosmix/loom 36
loom-technical-writing
cosmix/loom 36
loom-terraform
cosmix/loom 36
loom-test-strategy
cosmix/loom 36
loom-testing Comprehensive test implementation across all domains including unit, integration, e2e, security, infrastructure, data pipelines, and ML models. Covers TDD/BDD workflows, test architecture, flaky test debugging, and coverage analysis.
cosmix/loom 36
loom-threat-model
cosmix/loom 36
loom-typescript
cosmix/loom 36
loom-usage Meta-orchestration skill for Claude driving loom. Covers the full loom lifecycle from plan initialization through execution, monitoring, debugging, and recovery. Use this when Claude itself needs to operate loom — running plans, interpreting status, recovering from failures, managing state, and coordinating multi-stage execution. USE WHEN: Claude needs to run loom commands, debug failed stages, interpret loom status output, recover from crashes, manage worktrees, or orchestrate multi-stage execution. DO NOT USE: For writing loom plans (use /loom-plan-writer), for designing wiring checks (use /loom-wiring-test), for before/after verification pairs (use /loom-before-after), or for dead code detection config (use /loom-dead-code-check).
cosmix/loom 36
loom-wiring-test Generates wiring verification YAML for loom plans. Helps agents prove that features are properly integrated — commands registered, endpoints mounted, modules exported, components rendered. Use when writing truths/artifacts/wiring fields for loom plan stages.
cosmix/loom 36
context-engineering Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
itsmostafa/llm-engineering-skills 17
lora Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
itsmostafa/llm-engineering-skills 17
mlx Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
itsmostafa/llm-engineering-skills 17
pytorch Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
itsmostafa/llm-engineering-skills 17
agents Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
itsmostafa/llm-engineering-skills 17
prompt-engineering Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses.
itsmostafa/llm-engineering-skills 17
qlora Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
itsmostafa/llm-engineering-skills 17
rlhf Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
itsmostafa/llm-engineering-skills 17
transformers Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
itsmostafa/llm-engineering-skills 17
cursor-agent-supervisor Offloading tasks with a well-defined scope to sub-agents, for instance to use a sub-agent to implement a set of specs. Use this skill whenever a task should not need a broad knowledge of the whole project
YPares/agent-skills 23
github-pr-workflow Working with GitHub Pull Requests using the gh CLI. Use for fetching PR details, review comments, CI status, and understanding the difference between PR-level comments vs inline code review comments.
YPares/agent-skills 23
jj-todo-workflow Structured TODO commit workflow using JJ (Jujutsu). Use to plan tasks as empty commits with [task:*] flags, track progress through status transitions, manage parallel task DAGs with dependency checking. Enforces completion discipline. Enables to divide work between Planners and Workers. **Requires the working-with-jj skill**
YPares/agent-skills 23
nix-profile-manager Expert guidance for agents to manage local Nix profiles for installing tools and dependencies. Covers flakes, profile management, package searching, and registry configuration.
YPares/agent-skills 23
nushell-plugin-builder Guide for creating Nushell plugins in Rust using nu_plugin and nu_protocol crates. Use when users want to build custom Nushell commands, extend Nushell with new functionality, create data transformations, or integrate external tools/APIs into Nushell. Covers project setup, command implementation, streaming data, custom values, and testing.
YPares/agent-skills 23
nushell-usage Essential patterns, idioms, and gotchas for writing Nushell code. Use when writing Nushell scripts, functions, or working with Nushell's type system, pipelines, and data structures. Complements plugin development knowledge with practical usage patterns.
YPares/agent-skills 23