「ecto」の検索結果

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.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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。

Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.

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。

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

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。

Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery issues.

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 the GeoParquet specification and bundled inspection helpers to author, validate, distribute, or safely review geospatial Parquet data, including GeoParquet 2.0 vector metadata, external writer choices, and the alpha Parquet Raster proposal.

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。

Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection.

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 for Feast feature store tasks: feature repositories, definitions, CLI, retrieval, materialization, serving, RAG/vector search, integrations, and Feast contributor 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。

Create, polish, and troubleshoot figures4papers-style publication-ready matplotlib figures, including grouped bars, trends, radar charts, heatmaps, concept diagrams, manifolds, and high-DPI/vector export 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 Fiona workflows for GDAL-backed vector data I/O, GeoJSON-like features and schemas, CRS transforms, local virtual files, cloud-session boundaries, and the fio CLI.

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Use when working with GeoPandas GeoDataFrame and GeoSeries workflows, vector geospatial I/O, CRS-aware spatial operations, mapping, geocoding, and GeoPandas-specific validation.

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 Diffusion Planner for nuPlan autonomous-driving trajectory generation: prepare model-ready data, train or resume the diffusion model, configure closed-loop planning, and add differentiable collision or classifier guidance.

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 DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval 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。

Guides CUDA-accelerated cuRobo v2 robotics workflows for robot models, differentiable kinematics, collision-aware IK and motion planning, trajectory optimization, MPC, perception mapping, and motion retargeting.

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。

Routes AWS SDK for pandas (awswrangler) workflows across S3 lakehouse data, Athena/Glue catalogs, SQL connectors, runtime configuration, and AWS service integrations.

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。

Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.

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Route BlenderGIS add-on tasks for georeferencing, vector and OSM data, raster/DEM basemaps, terrain analysis, and geocamera rendering in Blender.

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 Atomic Agents to build schema-driven agents, integrate tools and MCP connectors, adapt example applications, and maintain the monorepo safely.

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。

Router for Alpamayo R1 multimodal driving inference and trajectory sampling.

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 IBM AI Fairness 360 for tabular fairness datasets, metrics, bias mitigation algorithms, sklearn-compatible workflows, subgroup detectors, and explainers.

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。

AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.

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。

Route Airweave tasks to the most specific sub-skill: local-development, backend-api, source-connectors, frontend-dashboard, connect-widget, mcp-search, or monke-e2e.

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。

Repair a failing WooCommerce Android Maestro flow by reproducing it against the store it runs on, inspecting Maestro selectors, patching the smallest selector/wait/setup issue, and rerunning with repeat evidence. Human-triggered only.

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。