「index」の検索結果

Author and validate BE spec files, update indexes/tracker, run deepening and ambiguity gates, and enforce allowed next-step transitions

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。

Document templates for all pipeline output documents — specs, indexes, reports, and strategy docs. Use when any workflow needs to produce a structured document.

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。

Create decomposition directory structure, IA shard skeletons, and all required layer indexes from approved decomposition plan

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。

Exhaustive idea extraction through recursive breadth-before-depth exploration with Deep Think protocol and fractal structure. Use during /ideate to transform raw ideas into comprehensive, structured ideation output. Writes to a fractal folder hierarchy — every node has an index, CX file, and children.

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。

How to run and test the docs/index.html AGSL shader web gallery locally (static server, WebGL2-capable Chrome flags, CDP verification hooks).

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。

Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

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。

Use when working with Convex — convex backend platform management covering project inventory, table schemas, function deployments, scheduled job status, index configuration, environment variable auditing, and usage metrics. Use for comprehensive Convex project health and resource optimization.

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。

Use when working with Chroma — chroma vector database management, collection inspection, embedding analysis, and query performance monitoring. Covers collection metadata, document counts, distance metrics, index health, and tenant/database configuration. Read this skill before any Chroma operations.

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。

Use when working with Aws Iot Core — aWS IoT Core management including things, thing groups, certificates, policies, rules, topic monitoring, device shadows, and fleet indexing. Covers device connectivity health, message throughput, rule action errors, certificate expiration, and shadow drift analysis.

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。

Use when working with Aerospike — aerospike cluster management, namespace utilization, set analysis, and secondary index monitoring. Covers node health, storage engine statistics, migration tracking, UDF management, and XDR cross-datacenter replication status. Read this skill before any Aerospike operations.

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。

Use when working with Algolia Deep — advanced Algolia search platform management including indices, search analytics, A/B tests, query rules, synonyms, API key audit, and infrastructure monitoring. Covers search performance metrics, relevance tuning, click-through analysis, indexing health, and usage quota tracking.

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。

Use when working with Gcp Firestore Deep — google Cloud Firestore document and collection analysis, index management, security rules review, usage monitoring, and performance diagnostics via gcloud CLI.

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。

Use when working with Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.

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。

Use when working with Firestore — google Cloud Firestore collection analysis, index management, security rules review, usage metrics, and query optimization.

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。

Use when working with Couchbase — couchbase bucket analysis, index advisor, N1QL query performance, XDCR status, and cluster health monitoring.

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。

Use when working with Mysql — mySQL and MariaDB database analysis, performance tuning, query optimization, and health monitoring. Covers slow query investigation, index analysis, table statistics, replication health, InnoDB status, connection pool analysis, and schema inspection.

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。

Use when working with Neo4J — neo4j graph database analysis, index management, Cypher query optimization, APOC utilities, and cluster health monitoring.

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。

AI Agent Index Categories Find Agent + Submit Compare Alternatives Stacks Advertise API Home / AI Coding Agents Best AI Coding Agents (2026): IDEs, Te

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。

Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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。

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

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。

NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering workflows.

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。

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

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 en Elasticsearch (indexing, mappings, aggregations, Kibana, DSL queries)

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。