Results for “ray-kroc”

Computes the Receiver Operating Characteristic (ROC) metric using torchmetrics, supporting binary, multiclass, and multilabel tasks.

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Scales machine learning training from single GPU to multi-node clusters with minimal code changes. Supports PyTorch, TensorFlow, and HuggingFace with built-in hyperparameter tuning, fault tolerance, and elastic scaling.

Launch, monitor, stop, and debug NeMo-RL recipes on a Kubernetes cluster using the nrl-k8s CLI, supporting ephemeral and long-lived RayCluster modes.

Framework for scaling Python applications from a laptop to a cluster. Includes Ray Core for distributed computing, Ray Serve for model serving, Ray Tune for hyperparameter optimization, and Ray Data for distributed data processing.

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

Lark/Feishu CLI router: match intent, then Read the Entry file under lark-*/ (e.g. lark-doc/lark-doc.md). Covers docs, markdown, sheets, base, calendar, im, mail, task, okr, drive, wiki, slides, whiteboard, apps, approval, attendance, contact, vc, minutes, note, event via lark-cli. Use when operating Feishu/Lark workspace resources, messaging, docs, calendars, tasks, OKRs, or Miaoda deploys.

Create and optimize popups, modals, overlays, slide-ins, and banners to increase conversions without harming user experience or brand trust.

Computes the AUROC metric using torchmetrics, handling binary, multiclass, and multilabel tasks with configurable thresholds and averaging.

Token budget analytics and ROI scoring for AI agent actions. Track token cost per fix, detect wasteful loops, auto-route to fast tier when burn rate is too high. Integrates with token-budget-guard.sh hook. Triggered by /cost-report, "how much did that cost", "token usage", "am I wasting tokens".

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Scales AI and Python applications across clusters with distributed computing primitives for ML workloads.

Agent-driven RAG patterns. Self-RAG, Corrective RAG (CRAG) with web fallback, Adaptive RAG with routing classifier, ReAct with retrieval tool, multi-hop retrieval, plan-and-execute, LangGraph state machines for RAG. USE WHEN: user mentions "agentic RAG", "Self-RAG", "Corrective RAG", "CRAG", "Adaptive RAG", "multi-hop retrieval", "LangGraph RAG", "ReAct RAG", "plan and execute RAG" DO NOT USE FOR: static retrieval pipelines - use `rag-architecture`; query rewriting only - use `query-transformations`; evaluation - use `rag-evaluation`

Tracks media progress, reviews, collections, analytics, and calendar in Ryot, with automation support.

Optimizes conversion across landing pages, signup flows, and onboarding. Use when the user requests 'CRO', 'conversion rate', 'landing page', 'signup flow', 'A/B test', 'form optimization', 'exit popup', or 'user activation'.

Create and optimize popups, modals, overlays, slide-ins, and banners to increase conversions without harming user experience or brand trust.

Regenerate the cross-repo roadmap aggregate in your Claude memory directory by walking every curated repo's `docs/specs/**` frontmatter. Run at session start for cross-repo orientation or after any `spec-new`/`spec-ship` in any repo.

Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.

Manages Ray clusters, jobs, Serve deployments, and distributed workloads via the Dashboard API and CLI, with discovery-first checks and safety guardrails.

Analyzes signup and onboarding flows to identify friction, motivation gaps, guardrails, and experiment opportunities for improving conversion.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

When the user wants to optimize, improve, or increase conversions on any marketing page — including homepage, landing pages, pricing pages, feature pages, or blog posts. Also use when the user says "CRO," "conversion rate optimization," "this page isn't converting," "improve conversions," or "why isn't this page working." For signup/registration flows, see signup-flow-cro. For post-signup activation, see onboarding-cro. For forms outside of signup, see form-cro. For popups/modals, see popup-cro.

Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.

Creates structured Jira tickets from incident root cause analysis findings, including severity classification and remediation steps.

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," or "overlay." For forms outside of popups, see form-cro. For general page conversion optimization, see page-cro.

Optimize non-signup forms such as lead capture, contact, demo request, application, survey, quote, and checkout forms to maximize completion while preserving data usefulness.

Detect and fix stale chunks (files that changed or were deleted since last indexing)

Incident Report (Blameless RCA)

Clerk authentication router. Use when user asks about adding authentication, setting up Clerk, custom sign-in flows, Swift or native iOS auth, native Android auth, Next.js patterns, React patterns, Vue patterns, Nuxt patterns, Astro patterns, TanStack Start patterns, Expo patterns, React Router patterns, Chrome Extension patterns, organizations, billing, subscriptions, payments, pricing, plans, seat-based pricing, feature entitlements, syncing users, or testing. Automatically routes to the specific skill based on their task.

Giskard RAGET (RAG Evaluation Toolkit): automatic testset generation (simple / complex / distracting / conversational), component-level scoring (retriever / generator / rewriter), hallucination and bias tests, CI integration. Compared to RAGAS and DeepEval. USE WHEN: user mentions "Giskard", "RAGET", "Giskard RAG toolkit", "automatic testset generation", "component-level RAG scoring", "hallucination test Giskard" DO NOT USE FOR: general RAGAS usage - use `rag-evaluation`; Stanford ARES - use `ares-framework`; CI/CD wiring - use `continuous-evaluation`

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Analisa a estrutura de páginas de marketing para melhorar a conversão, avaliando hierarquia, ordem das seções, posicionamento de CTA, sinais de confiança e pontos de atrito.

Optimizes non-signup forms such as lead capture, contact, demo request, application, survey, quote, and checkout forms to maximize completion while preserving data usefulness.

Optimize any form that is NOT signup or account registration — including lead capture, contact, demo request, application, survey, quote, and checkout forms. Use when the goal is to increase form completion rate, reduce friction, or improve lead quality without breaking compliance or downstream workflows.