tuning AI Agent Skills
Browse 492 skills related to tuning
spark-optimization
wshobsonOptimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
vector-index-tuning
wshobsonOptimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
llava
davila7Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
grpo-rl-training
davila7Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
transformers
davila7This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
scikit-learn
davila7Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
peft-fine-tuning
davila7Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train less than 1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
senior-ml-engineer
davila7World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
senior-backend
davila7Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.
llama-factory
davila7Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
gptq
davila7Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
unsloth
davila7Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
weights-and-biases
davila7Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
clip
davila7OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
algolia-search
davila7Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantsearch, search api, search functionality.
implementing-llms-litgpt
davila7Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when needing clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
fine-tuning-with-trl
davila7Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
axolotl
davila7Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
vector-index-tuning
sickn33Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
sql-pro
sickn33Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.
database-optimizer
sickn33Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures.
spark-optimization
sickn33Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
application-performance-performance-optimization
sickn33Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
postgresql
2025EmmaPostgreSQL database documentation - SQL queries, database design, administration, performance tuning, and advanced features. Use when working with PostgreSQL databases, writing SQL, or managing database systems.
scikit-learn
K-Dense-AIMachine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
transformers
K-Dense-AIThis skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
database-optimizer
JeffallanUse when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.
fine-tuning-expert
JeffallanUse when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.
postgres-pro
JeffallanUse when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.
llm-tuning-patterns
parcadeiLLM Tuning Patterns
gpt
openclawOpenAI GPT integration. Chat completions, image generation, embeddings, and fine-tuning via OpenAI API.
sql-assistant
openclawComprehensive SQL query assistant for database operations, optimization, and troubleshooting. Use when Codex needs to write, debug, optimize, or explain SQL queries; analyze database schemas; or help with SQL-related tasks including joins, subqueries, aggregations, and performance tuning. Supports MySQL, PostgreSQL, SQLite, and other SQL dialects.
feishu-bridge
openclawConnect a Feishu (Lark) bot to Clawdbot via WebSocket long-connection. No public server, domain, or ngrok required. Use when setting up Feishu/Lark as a messaging channel, troubleshooting the Feishu bridge, or managing the bridge service (start/stop/logs). Covers bot creation on Feishu Open Platform, credential setup, bridge startup, macOS launchd auto-restart, and group chat behavior tuning.
sql-pro
openclawUse when optimizing SQL queries, designing database schemas, or tuning database performance. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis.
airfrance-afkl
openclawTrack Air France flights using the Air France–KLM Open Data APIs (Flight Status). Use when the user gives a flight number/date (e.g., AF007 on 2026-01-29) and wants monitoring, alerts (delay/gate/aircraft changes), or analysis (previous-flight chain, aircraft tail number → cabin recency / Wi‑Fi). Also use when setting up or tuning polling schedules within API rate limits.
spark-engineer
openclawUse when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.
pgvector-semantic-search
timescaleUse this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
sentry-performance-tuning
Optimize Sentry performance monitoring configuration. Use when tuning sample rates, reducing overhead, or improving performance data quality. Trigger with phrases like "sentry performance optimize", "tune sentry tracing", "sentry overhead", "improve sentry performance".
optimizing-database-connection-pooling
jeremylongshoreThis skill optimizes database connection pooling for enhanced performance and resource management. It is activated when the user requests assistance with connection pooling, database performance tuning, or connection lifecycle management. Use this skill to implement and configure connection pools in various programming languages, identify optimal pool settings, and troubleshoot common connection-related issues. The skill is triggered by phrases like "connection pooling," "optimize database connections," or "improve database performance with connection pool."
replit-performance-tuning
Optimize Replit API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Replit integrations. Trigger with phrases like "replit performance", "optimize replit", "replit latency", "replit caching", "replit slow", "replit batch".
vercel-performance-tuning
jeremylongshoreOptimize Vercel API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Vercel integrations. Trigger with phrases like "vercel performance", "optimize vercel", "vercel latency", "vercel caching", "vercel slow", "vercel batch".
linear-cost-tuning
Optimize Linear API usage and manage costs effectively. Use when reducing API calls, managing rate limits efficiently, or optimizing integration costs. Trigger with phrases like "linear cost", "reduce linear API calls", "linear efficiency", "linear API usage", "optimize linear costs".
maintainx-cost-tuning
Optimize MaintainX API usage for cost efficiency. Use when managing API costs, optimizing request volume, or implementing cost-effective integration patterns with MaintainX. Trigger with phrases like "maintainx cost", "maintainx billing", "reduce maintainx usage", "maintainx api costs", "maintainx optimization".
firecrawl-performance-tuning
Optimize FireCrawl API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for FireCrawl integrations. Trigger with phrases like "firecrawl performance", "optimize firecrawl", "firecrawl latency", "firecrawl caching", "firecrawl slow", "firecrawl batch".
linear-performance-tuning
Optimize Linear API queries and caching for better performance. Use when improving response times, reducing API calls, or implementing caching strategies. Trigger with phrases like "linear performance", "optimize linear", "linear caching", "linear slow queries", "speed up linear".
perplexity-performance-tuning
Optimize Perplexity API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Perplexity integrations. Trigger with phrases like "perplexity performance", "optimize perplexity", "perplexity latency", "perplexity caching", "perplexity slow", "perplexity batch".
supabase-performance-tuning
Optimize Supabase API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Supabase integrations. Trigger with phrases like "supabase performance", "optimize supabase", "supabase latency", "supabase caching", "supabase slow", "supabase batch".
obsidian-performance-tuning
Optimize Obsidian plugin performance for smooth operation. Use when experiencing lag, memory issues, or slow startup, or when optimizing plugin code for large vaults. Trigger with phrases like "obsidian performance", "obsidian slow", "optimize obsidian plugin", "obsidian memory usage".
