Results for “quaderno”

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

Diagnoses and reduces Qdrant memory usage by analyzing resident memory, page cache, and providing optimization techniques like quantization, on-disk storage, and async_scorer.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Diagnoses and resolves slow Qdrant indexing and data ingestion by optimizing batching, sharding, HNSW parameters, and payload indexing strategies.

Drafts a Motion for Judgment Notwithstanding the Verdict (JNOV) under FRCP 50(b) or state equivalents, with alternative new-trial request. Builds element-by-element evidentiary insufficiency arguments using transcript citations and preserves the appellate record. Use when drafting JNOV motions, post-trial motions, renewed judgment as a matter of law, or challenging jury verdicts for insufficient evidence.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Drafts Qualified Domestic Relations Orders (QDROs) compliant with ERISA §206(d)(3) and IRC §414(p) to divide retirement benefits in divorce. Covers defined benefit pensions, 401(k)s, and defined contribution plans with plan-specific division formulas and alternate payee protections. Use when drafting QDROs, dividing retirement assets post-judgment, or preparing domestic relations orders for plan administrator review.

Guides test-driven development for Quarkus 3.x services with JUnit 5, Mockito, REST Assured, Camel testing, and JaCoCo, targeting 80%+ coverage for event-driven architectures.

Qdrant vector database — collections, upsert, search, filtering, payloads, sparse vectors, BM25

Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)

Test-driven development for Quarkus 3.x LTS using JUnit 5, Mockito, REST Assured, Camel testing, and JaCoCo. Use when adding features, fixing bugs, or refactoring event-driven services.

Runs an evidence-first, read-only workflow over large codebases via an external JSONL REPL server, retrieving only minimal file slices and greps to produce cited architecture and audit reports.

Sistema multiagente de IA para assistência em pesquisa científica que automatiza fluxos de trabalho de pesquisa desde análise de dados até publicação. Esta skill deve ser usada ao gerar ideias de pesquisa a partir de conjuntos de dados, desenvolver metodologias de pesquisa, executar experimentos computacionais, realizar buscas de literatura ou gerar papers prontos para publicação em formato LaTeX. Suporta pipelines de pesquisa end-to-end com orquestração de agentes personalizável.

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting, then commit with git.

Applies four disciplined mindsets—audit, debt, help, review—to cut complexity, track deferrals, surface reference, and catch over-engineering in codebases.

当感到生活迷茫、停滞不前，需要理解当前所处阶段并规划下一步行动时

Implementa state management avanzado con BLoC y Cubit en Flutter, incluyendo persistencia con Hydrated BLoC, replay de estados, transformers para control de eventos y testing exhaustivo.

Build AI agents, multi-agent teams, and agentic workflows using the Agno framework, with reference files covering agents, teams, workflows, memory, knowledge, and deployment.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).