Results for “radixattention”

Preserve React 18 concurrent patterns and adopt React 19 APIs (useTransition, useDeferredValue, Suspense, use(), useOptimistic, Actions) during migration.

Reclaim DRAM on NVIDIA Jetson devices by disabling unused display, camera, and DMA subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB layers for headless or no-camera deployments.

Radix UI unstyled accessible React components. Headless primitives. USE WHEN: user mentions "Radix", "Radix UI", "headless components", "unstyled primitives", asks about "accessible components", "Radix primitives" DO NOT USE FOR: shadcn/ui (use shadcn-ui skill), styled component libraries (Material-UI, Chakra), Vue/Svelte (use Headless UI)

Econometrics skill for Regression Discontinuity Design (RDD). Activates when the user asks about: "regression discontinuity", "RDD", "RD design", "sharp RDD", "fuzzy RDD", "running variable", "forcing variable", "cutoff", "bandwidth selection", "local linear regression", "McCrary test", "density test", "RDROBUST", "continuity assumption", "donut hole RDD", "geographic RDD", "断点回归", "回归不连续", "运行变量", "截断值", "带宽选择", "精确断点", "模糊断点", "密度检验", "局部线性回归"

Redis caching patterns, pub/sub, sessions, rate limiting, and data structures.

Caching strategies across the RAG stack. Semantic caching with GPTCache and LangChain, Redis-based embedding-similarity cache, cache key design, TTL/invalidation, partial caching (cache retrieval only), provider-native prompt caching (Anthropic, OpenAI), and hierarchical L1/L2 caches. USE WHEN: user mentions "semantic cache", "GPTCache", "LLM cache", "prompt caching", "Redis vector cache", "cache invalidation for RAG", "reduce LLM cost", "latency reduction LLM" DO NOT USE FOR: retrieval accuracy - use `rag-patterns`; groundedness checks - use `rag-guardrails`; incremental indexing - use `rag-production`

Systematically investigate all persistence mechanisms on Windows and Linux systems to identify how malware survives reboots and maintains access.

Quick reference for Redis best practices across common backend use cases, including caching strategies, rate limiting, distributed locks, pub/sub, streams, and connection management.

Save information to persistent memory for cross-session recall. Stores preferences, conventions, decisions, and context.

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

Redis in-memory data store. Covers data structures, caching, and pub/sub. Use for caching and real-time features. USE WHEN: user mentions "redis", "caching", "session storage", "rate limiting", "pub/sub", "sorted sets", "in-memory database", "cache invalidation" DO NOT USE FOR: relational data - use `postgresql` or `mysql` instead, document storage - use `mongodb` instead, full-text search - use `elasticsearch` instead

Keeps long AI sessions on track by tracking decisions, detecting scope creep, and flagging drift before it costs work.

Designs and implements data retention schedules compliant with GDPR Article 5(1)(e) storage limitation principle. Maps data categories to retention periods with legal basis justification, regulatory minimum holding periods, and automated review triggers for schedule maintenance. Activate for retention policy, storage limitation, data lifecycle, retention period queries.

Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.

Implements financial records retention requirements across EU directives (5-7 years), SOX Section 802 (7 years), MiFID II (5-7 years), tax records, payment data, and AML obligations under AMLD. Maps financial data categories to statutory retention periods with cross-jurisdictional reconciliation. Activate for financial retention, SOX records, MiFID retention, AML retention, tax record keeping queries.

Store and inspect source-indexed proof material extracted from papers, notes, PDFs, or LaTeX sources. Use when Codex needs stable material IDs, source locations, reusable abstractions, proof skeletons, tags, and public attribution before any proof ideas are cooked into proof-usage.

Wydruki i raporty w Soneta (enova365, Triva) — pliki .repx (DevExpress XtraReports, serializowany XML). Sięgaj przy KAŻDYM zadaniu z wydrukiem/raportem: faktura, dokument, deklaracja, lista, zestawienie; utworzenie, przeróbka lub poprawa .repx (też z załącznika); pytanie „jak w wydruku zrobić X”. Typowe X: podpięcie danych (lista, dokument z pozycjami, master-detail, podraport), podział stron, kolumna Lp, tabele, grupowanie i sumy, formatowanie warunkowe, kwoty, nakładanie wartości na skan formularza, nagłówek/stopka/podpisy, arkusz stylów (.repss), rejestracja wydruku. Wyzwalają też pojęcia: XtraReportsLayoutSerializer, StyleSheetSerializer, pasma (DetailBand/DetailReportBand/GroupHeaderBand/SubBand), kontrolki (XRLabel/XRTable/AmountLabel), BusinessDataSource/DataKind, ReportContext, ExpressionBindings/Summary/CalculatedFields/FilterString, [assembly: DxReport], ReportSnippet/[DxBind]. Wystarczy „wydruk”, „raport” lub „.repx” w kontekście Soneta. NIE dla: business.xml, form.xml, SQL, Excel, Word .dotx.

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users drop off", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", "user activation", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users stop after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.

Recommends careful DDAVP and fluid intake titration including frequent weighing and serum sodium level monitoring for adipsic diabetes insipidus. Use when managing adipsic DI requiring fluid balance management.

Capture durable project memory from work, debates, debugging discoveries, learned conventions, deferred options, and session outcomes. Load when the user says remember this, save this learning, record what happened, update project memory, or preserve context for future agents.

Investigate retention patterns, cohorts, drivers, warning signs, and intervention options.

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications.

Upserts a validated MDX book corpus into Supabase via Drizzle, hydrating books, chapters, sections, and chunks tables while preserving stable bookmark anchors and only re-embedding changed content.

Save the current diagnosis state to disk for cross-session recall, including conclusions, rejected directions, and next steps.

Use when converting completed work into durable knowledge. This skill provides a systematic process for extracting lessons learned, patterns, and insights from completed work and storing them in memory systems (PARA, Gigabrain, OpenStinger) for future reuse.

Manage Convex table retention policies, TTL fields, cleanup crons, and schema classification for ClawHub.

Pre-ingestion verification for epistemic quality in RAG systems with 9-point verification and Two-Round HITL workflow

Scaffold, install, and compose shadcn-vue components built on Reka UI v2, including Vue variants, form patterns with vee-validate/Zod, and adaptation of existing components.

Use to review how call recordings are notified, stored, accessed, redacted and deleted, including the parts that differ from text conversations. Trigger for "review our call recording practices", "do we need to tell customers we're recording", "who can listen to our calls", recording retention, card details spoken on a call, or a request for a customer's own recording.

Train reinforcement learning agents using PPO, SAC, DQN, TD3, DDPG, and A2C algorithms with a scikit-learn-like API. Supports custom Gymnasium environments, vectorized environments, callbacks, and model persistence.

Local RAG indexing with 97% storage reduction via anchor-based lazy recomputation. Graph-based selective embedding storage for memory-efficient semantic code search.

Check post-launch traction for a shipped VARD app and recommend iterate, graduate, or archive

Enforces identity-based network access with 802.1X, RADIUS authentication, dynamic VLAN assignment, and endpoint posture assessment using PacketFence.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.