Rendiment

Anàlisi i optimització del rendiment

Skills: 409–432 de 2974
TerminalSkills

3dsmax-rendering

TerminalSkills

Configure and optimize rendering in 3ds Max — V-Ray and Corona render settings, render elements, light mix, batch rendering, network rendering, denoising, and post-production workflows. Use when tasks involve setting up production renders, optimizing render times, batch rendering multiple views, or configuring render farms for archviz and product visualization.

Automatització 179 fa 8 mesos
github

powerbi-modeling

github

'Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.'

Generació de codi 40,4 k fa 8 mesos
ChromeDevTools

chrome-devtools

ChromeDevTools

Uses Chrome DevTools via MCP for efficient debugging, troubleshooting and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to --slim mode (MCP configuration).

Processament de dades 54,1 k fa 8 mesos
Yeachan-Heo

learn-about-omc

Yeachan-Heo

Learn about your OMC usage patterns and get personalized recommendations

Agents 40,2 k fa 8 mesos
Yeachan-Heo

writer-memory

Yeachan-Heo

Agentic memory system for writers - track characters, relationships, scenes, and themes

Generació de codi 40,2 k fa 9 mesos
Yeachan-Heo

note

Yeachan-Heo

Save notes to notepad.md for compaction resilience

Autenticació 40,2 k fa 9 mesos
evolving-machines-lab

vercel-react-best-practices

evolving-machines-lab

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Rendiment 76 fa 9 mesos
aipoch

interview-mock-partner

aipoch

Simulates behavioral interview questions for medical professionals.

Operacions amb fitxers 2 k fa 8 mesos
aipoch

networking-email-drafter

aipoch

Draft professional follow-up emails to contacts made at conferences

Correu electrònic 2 k fa 8 mesos
aipoch

postdoc-fellowship-matcher

aipoch

Match postdoc applicants to eligible fellowships based on nationality

Eines de línia d’ordres 2 k fa 8 mesos
SamSaffron

memory

SamSaffron

"Read and update persistent memory. Use when: user says 'remember', asks about past context, preferences, or projects. Also use proactively to save anything reusable before a conversation ends."

Eines de línia d’ordres 125 fa 4 mesos
thienanblog

ask-questions-if-underspecified

thienanblog

Clarify requirements before implementing. Do not use automatically, only when invoked explicitly.

Anàlisi estàtica i format 69 fa 9 mesos
davila7

agent-memory-systems

davila7

"Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm"

Agents 32,9 k fa 9 mesos
davila7

peft-fine-tuning

davila7

Parameter-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 <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

MLOps 32,9 k fa 9 mesos
davila7

unsloth

davila7

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

Internacionalització 32,9 k fa 9 mesos
davila7

serving-llms-vllm

davila7

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

MLOps 32,9 k fa 9 mesos
davila7

context-window-management

davila7

"Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context."

Representacions vectorials 32,9 k fa 9 mesos
davila7

ai-agents-architect

davila7

"Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling."

Agents 32,9 k fa 9 mesos
davila7

conversation-memory

davila7

"Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history."

Bases de dades 32,9 k fa 9 mesos
davila7

agent-memory-mcp

davila7

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Agents 32,9 k fa 9 mesos
davila7

deepspeed

davila7

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

Processament de dades 32,9 k fa 9 mesos
davila7

training-llms-megatron

davila7

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Eines de línia d’ordres 32,9 k fa 9 mesos
OpenClaudia

icp-builder

OpenClaudia

Define ideal customer profiles and buyer personas with structured frameworks. Use when the user asks about ideal customer profile, ICP, buyer persona, target audience definition, customer segmentation, customer research, user interviews, or persona creation. Trigger phrases include "ideal customer profile", "ICP", "buyer persona", "who is my customer", "target audience", "customer profile", "persona", "customer segmentation", "user research", "customer interview", "who should I target".

Generació de codi 723 fa 8 mesos
OpenClaudia

page-cro

OpenClaudia

Audit and optimize landing pages for conversion rate. Use when the user asks about conversion rate optimization, CRO, landing page audits, improving sign-ups or sales, reducing bounce rate, A/B test ideas for pages, form optimization, or CTA optimization. Trigger phrases include "conversion rate", "CRO", "landing page audit", "why isn't my page converting", "improve conversions", "optimize my page", "reduce bounce rate", "CTA optimization", "form optimization", "above the fold".

Rendiment 723 fa 8 mesos