「index」の検索結果

Calculate the Maryland Aggregate Pathology Index (MAPI) on a deceased donor procurement kidney biopsy to predict graft survival and decide single vs dual vs discard allocation. Trigger when a clinician asks "what's the MAPI score", "interpret donor kidney biopsy", "should I use this marginal kidney", "MAPI cutoff for transplant", "procurement biopsy scoring", or has histology results from a deceased donor kidney and needs an objective grading.

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Extract every PlantUML block from docs/specs/<slug>.md and render each to SVG under docs/specs/_rendered/<slug>/, with an index.md listing them in order. Run this before /approve-direction so the reviewer sees pictures instead of raw PlantUML.

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Find web sources with query-relevant page excerpts and optional full-page content, and discover workflows, data APIs, and indexes. Use for web research or finding structured records, listings, transcripts, and datasets. Supports semantic tool discovery, domain matching, and progressive catalogue browsing.

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Find a direct path to structured data through ready-made workflows, data APIs, and indexes. Follow the search skill to discover and inspect tools, then the scrape skill to execute them.

日本語の概要は準備中です。原文の説明を表示しています。

Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

日本語の概要は準備中です。原文の説明を表示しています。

Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning

日本語の概要は準備中です。原文の説明を表示しています。

Search Reddit posts by meaning via the reddapi.dev index.

日本語の概要は準備中です。原文の説明を表示しています。

Search, create, and manage notes in the Obsidian vault with wikilinks and index notes. Use when user wants to find, create, or organize notes in Obsidian.

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Interactive agent-flow setup. Scans the repo read-only, proposes AGENTS.md, DOCS_INDEX.md and CONTEXT_MANIFEST.json with a confidence marker per claim, asks the user for protected paths and risk boundaries, writes each file only after approval. Use when setting up agent-flow, when context files are missing, or when asked to write an AGENTS.md, onboard a repo for AI agents or set protected paths.

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Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

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Design relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.

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Use when you need blast-radius analysis, dependency graphs, cross-repo impact, breaking-change diff, or architectural overview of .NET workspaces. Keywords: blast radius, dependency graph, impact analysis, cross-repo, call graph, endpoint map, EF Core lineage, breaking change, daemon, reindex.

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Measure, root-cause, fix, verify and record performance contention on a developer machine: cloud-sync daemons (iCloud Drive, OneDrive, Dropbox, Google Drive), file indexers, AI-tool MCP server fleets, and automation browsers left behind by tests and browser skills. Trigger when the machine is slow, apps freeze, browser or MCP tool calls time out, load exceeds the core count, or before and after any UI/UX/performance test run. Includes the L0–L4 optimisation scale and the test-hygiene contract.

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Use when reviewing a migration diff or schema change for scale hazards — indexes, unsafe migrations, unbounded growth, N+1. Triggers on 'review this migration', 'will this scale'.

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Use when working with database architecture, MariaDB/MySQL tuning, indexing strategies, slow queries, or multi-connection patterns — even when the user just says 'this query is slow'.

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Use when capturing an architectural decision — file naming, next ADR number, Status / Context / Decision / Consequences, index regen; fires even without saying 'ADR'.

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Princípios de design de banco de dados e tomada de decisão. Design de schema, estratégia de indexação, seleção de ORM, bancos de dados serverless.

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Projete esquemas de banco de dados robustos e escaláveis para SQL e NoSQL. Fornece diretrizes de normalização, estratégias de indexação, padrões de migração, design de restrições e otimização de desempenho. Garante integridade de dados, desempenho de consultas e modelos de dados mantíveis.

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Workflow de otimização de banco de dados PostgreSQL para sintonia de consultas, estratégias de indexação, análise de desempenho e gerenciamento de banco de dados em produção.

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Padrões especializados para implementação de busca Algolia, estratégias de indexação, React InstantSearch e ajuste de relevância. Use quando: adicionar busca, algolia, instantsearch, search api, funcionalidade de busca.

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Projete um schema específico para PostgreSQL. Abrange práticas recomendadas, tipos de dados, indexação, constraints, padrões de desempenho e recursos avançados

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Especialista em Langfuse - plataforma de observabilidade LLM de código aberto. Cobre rastreamento, gerenciamento de prompts, avaliação, datasets e integração com LangChain, LlamaIndex e OpenAI. Essencial para depuração, monitoramento e melhoria de aplicações LLM em produção. Use quando: langfuse, observabilidade llm, rastreamento llm, gerenciamento de prompts, avaliação llm.

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[Documentation] Use when scanning the codebase to (re)generate ONE project-reference doc. Parameterized by `--target=<key>`: project-structure | backend-patterns | frontend-patterns | scss-styling | design-system | code-review-rules | domain-entities | feature-spec | docs-index | e2e-tests | integration-tests | seed-test-data | ui-system. Shared 4-phase scan engine; per-target detail lives in references/targets.md. `ui-system` is an orchestrator meta-target (`kind: orchestrator`) that runs the 3 UI child scans in parallel instead of the 4-phase engine.

日本語の概要は準備中です。原文の説明を表示しています。