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Agent skills
ai-engineering

Topic: ai-engineering

51 skills in this topic.

grace-setup-subagents Create GRACE subagent presets for the current agent shell. Use when you want GRACE worker and reviewer agent files scaffolded for Claude Code, OpenCode, Codex, or another shell.
osovv/grace-marketplace 120
grace-ask Answer a question about a GRACE project using full project context. Use when the user has a question about the codebase, architecture, modules, or implementation — loads all GRACE artifacts, navigates the knowledge graph, and provides a grounded answer with citations.
osovv/grace-marketplace 120
grace-verification Design and enforce testing, traces, and log-driven verification for a GRACE project. Use when modules need stronger automated tests, execution-trace checks, or a maintained verification-plan.xml that autonomous and multi-agent workflows can trust.
osovv/grace-marketplace 120
grace-status Show the current health status of a GRACE project. Use to get an overview of project artifacts, codebase metrics, knowledge graph health, verification coverage, and suggested next actions.
osovv/grace-marketplace 120
update-llm-pool Workflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding hand-curated entries to section files (use add-new-entry), updating RAG/Agent citation sections in best_practices.md (use update-cite-count).
kimtth/awesome-azure-openai-llm 398
update-cite-count Guidelines for updating citation counts for papers in the section files using the `update_citation_counts.py` tool. USE FOR: Updating citation counts for papers listed in the section files to keep information current. DO NOT USE FOR: 1) Adding new papers to the section files; 2) Classifying entries into sections.
kimtth/awesome-azure-openai-llm 398
update-app-count Workflow for updating the popular LLM applications pool (section/x_llm_apps.md) using get_app_list_by_github_star.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked from applications.md. DO NOT USE FOR: Hand-curating application entries inside applications.md or adding GitHub star badges to the generated file.
kimtth/awesome-azure-openai-llm 398
classify-temp-entries-to-section Classification guidelines for entries in temp_entries.md. Each entry have its own title with the markdown file name and section name in temp_entries.md. USE FOR: Classifying new entries in temp_entries.md into *.md in the section files. DO NOT USE FOR: 1) Adding new entries to temp_entries.md; 2) Moving entries between sections.
kimtth/awesome-azure-openai-llm 398
add-new-entry Workflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR: Adding new resources to the knowledge base. DO NOT USE FOR: Editing existing entries or restructuring sections.
kimtth/awesome-azure-openai-llm 398
spec
maxritter/pilot-shell 1,637
create-skill Create a well-structured skill — provide a topic to explore the codebase and build a skill interactively, or capture a workflow from the current session
maxritter/pilot-shell 1,637
prd Generate Product Requirements Documents with optional research — brainstorm, challenge assumptions, define scope
maxritter/pilot-shell 1,637
setup-rules Set up and audit project rules — reads codebase, generates modular rules, documents MCP servers
maxritter/pilot-shell 1,637
spec-bugfix-plan Bugfix spec planning phase - investigate root cause, design fix, get approval
maxritter/pilot-shell 1,637
spec-bugfix-verify Bugfix verification phase - tests, quality checks, fix confirmation
maxritter/pilot-shell 1,637
spec-implement Spec implementation phase - TDD loop for each task in the plan
maxritter/pilot-shell 1,637
spec-plan Spec planning phase - explore codebase, design plan, get approval
maxritter/pilot-shell 1,637
spec-verify Spec verification phase - tests, execution, rules audit, code review
maxritter/pilot-shell 1,637
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
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
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
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
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
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