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Topic: llm

10,059 skills in this topic.

issue-tree-builder McKinsey-style issue tree framework for breaking down complex problems into MECE (Mutually Exclusive, Collectively Exhaustive) components. Use when users need to decompose strategic questions, structure analysis, create work plans, or prepare for case interviews. Apply hypothesis-driven approach to problem-solving.
sruthir28/enterprise-ai-skills 31
prioritization
sruthir28/enterprise-ai-skills 31
scpr-framework SCPR (Situation-Complication-Problem-Recommendation) framework for structured problem solving and executive communication. Use when users need to structure strategic arguments, analyze business situations, create executive summaries, or develop clear problem statements using McKinsey-style communication. Apply when structuring recommendations, writing memos, or organizing strategic thinking.
sruthir28/enterprise-ai-skills 31
storyline-builder McKinsey-style storyline framework for building presentation decks. Use when users need to structure presentations, pitch decks, or strategic communications. Creates logical flow where each storyline becomes a slide title, progressing from problem to solution.
sruthir28/enterprise-ai-skills 31
local-skills-mcp-guide Repository implementation guide for the local-skills-mcp codebase. Use when asked: how src/index.ts and src/skill-loader.ts work together; where MCP tool handlers are defined and registered; how getAllSkillsDirectories priority and override behavior works; how local-skills-mcp discovers skills and merges metadata across directories; where validate_skill and evaluate_skill are implemented in this repository; or how integration tests are structured in this local-skills-mcp project.
kdpa-llc/local-skills-mcp 23
local-skills-mcp-usage Operational guide for using Local Skills MCP in day-to-day projects. Use when asked: where skills should live (~/.claude/skills, ./.claude/skills, ./skills, SKILLS_DIR); directory precedence and override rules; how to configure local-skills-mcp in Claude Code mcp.json; fastest way to create a new skill folder and make it discoverable; how to add project skills to git so the team gets them; or how Local Skills MCP hot-reloads skill edits without restarting.
kdpa-llc/local-skills-mcp 23
skill-creator Use this skill when building, writing, reviewing, or fixing a SKILL.md file for a new or existing skill. Covers choosing skill names, writing and correcting YAML frontmatter (name and description fields), crafting effective 'Use when' trigger phrases, structuring skill body instructions, improving an existing skill's routing quality with better trigger keywords, and interpreting or evaluating a skill description against an eval set using validate_skill or evaluate_skill.
kdpa-llc/local-skills-mcp 23
mcp-stata
tmonk/mcp-stata 46
mcp-stata
tmonk/mcp-stata 46
stata-mcp Run or debug Stata workflows through the local io.github.tmonk/mcp-stata server. Use when users mention Stata commands, .do files, r()/e() results, dataset inspection, Stata graph exports, or data browsing with sorting/filtering.
tmonk/mcp-stata 46
conference-scheduler Generate optimized conference schedules using Google OR-Tools CP-SAT solver. Use when the user needs to create conference schedules from CSV data with constraints like speaker conflicts, track distribution, room assignments, educational flow, and speaker availability. Supports both single-day and multi-day conferences. Handles inputs from Google Sheets CSV exports with talks (ID, title, summary, track, level, speakers, availability) and schedule slots (day, times, rooms). Outputs CSV and Markdown schedules.
stephanj/conference-scheduling-skill 16
conference-scheduler Generate optimized conference schedules using SolverForge (Timefold compatible). Use when the user needs to create conference schedules from CSV data with constraints like speaker conflicts, track distribution, room assignments, educational flow, and speaker availability. Supports both single-day and multi-day conferences. Python-native solution with no Java project setup required.
stephanj/conference-scheduling-skill 16
conference-scheduler Generate optimized conference schedules using TimeFold (formerly OptaPlanner). Use when the user needs to create conference schedules from CSV data with constraints like speaker conflicts, track distribution, room assignments, educational flow, and speaker availability. Supports both single-day and multi-day conferences. Handles inputs from Google Sheets CSV exports with talks (ID, title, summary, track, level, speakers, availability) and schedule slots (day, times, rooms). Outputs CSV and Markdown schedules.
stephanj/conference-scheduling-skill 16
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
example-data-processor Process CSV data files by cleaning, transforming, and analyzing them. Use this when users need to work with CSV files, clean data, or perform basic data analysis tasks.
fkesheh/skill-mcp 25
clarity-gate Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.
frmoretto/clarity-gate 25