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ML System Evaluation Methodology Guide

mlsys-experiments

brycewang-stanford/Awesome-Journal-Skills

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

On-Device Result Provenance and Reproducibility

mobisys-reproducibility

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for researchers detailing how to establish rigorous provenance for on-device performance measurements (e.g., energy, latency, accuracy). It mandates recording all necessary context—including device model, SoC, OS build, framework versions, power instrument setups, and thermal conditions—to ensure results are verifiable and reproducible across different hardware or environments for academic publication.

WACV Artifact Evaluation Guidelines

wacv-artifact-evaluation

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for creating the two required artifacts for a WACV paper: an anonymous package for double-blind review, and a public release post-acceptance. It emphasizes reproducing deployed system claims (latency, power, robustness) and maintaining consistency across revision rounds.

Ensuring Model Reproducibility for WACV

wacv-reproducibility

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for strengthening the reproducibility of academic papers, especially those involving systems claims (e.g., power usage, latency). It details maintaining a 'recipe ledger' for all reported metrics, ensuring constraint-aware measurement of environmental variables, and managing data/artifact consistency across the entire two-round revision process.

Caveman Evidence Review

caveman-evidence-review

JuliusBrussee/caveman

Read-only review of Caveman Cloud evidence covering cost, Cave Score, workflows, traces, latency, errors, routing, and savings. Use when asked what Caveman found or where LLM spend goes. Conclusions are based on current data and cite trace IDs.

AI Agent Evaluation Reporting

agent-evaluation-reporting

sickn33/agentic-awesome-skills

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

AI Agent Observability

agent-observability

sickn33/agentic-awesome-skills

Instrument AI agents with tracing, token metrics, latency, cost visibility, and PII redaction for reliability, debugging, and SLO monitoring.

AI Agent Performance Optimization

agent-orchestration-multi-agent-optimize

sickn33/agentic-awesome-skills

This skill optimizes multi-agent systems through coordinated profiling, workload distribution, and cost-aware orchestration. It helps improve agent performance, throughput, and reliability by identifying bottlenecks, managing context windows, and reducing latency. Suitable for complex workflows requiring intelligent coordination and efficiency tracking across multiple AI agents.

Context Optimization Techniques

context-optimization

sickn33/agentic-awesome-skills

Context optimization extends effective capacity of limited context windows via compression, masking, caching, and partitioning. It aims to better utilize available capacity rather than increasing window size. Techniques include summarizing context, masking verbose outputs, reusing KV-cache, and partitioning work across sub-agents to reduce costs and latency in AI agent systems.

Real-Time Multimodal AI Agent Orchestration

sickn33/agentic-awesome-skills

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

LLM Prompt Caching

sickn33/agentic-awesome-skills

Caching strategies for LLM prompts, including Anthropic prompt caching, response caching, cache invalidation, and Cache Augmented Generation (CAG) to reduce latency and cost.

RouterBase Model Gateway For LLM Routing

routerbase-model-gateway

sickn33/agentic-awesome-skills

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

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