Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: oracle

英語版ディレクトリ

Feature-rich ORM for modern Node.js and TypeScript, it supports PostgreSQL (with JSON and JSONB support), MySQL, MariaDB, SQLite, MS SQL Server, Snowflake, Oracle DB, DB2 and DB2 for IBM i.

30K
Stars
85/100
信頼
カテゴリ: data-analysis監査

A query builder for PostgreSQL, MySQL, CockroachDB, SQL Server, SQLite3 and Oracle, designed to be flexible, portable, and fun to use.

20K
Stars
85/100
信頼
カテゴリ: data-analysis監査

node of the decentralized oracle network, bridging on and off-chain computation

8.2K
Stars
75/100
信頼
カテゴリ: web3-analytics監査

Technical resources for AI developers to build applications, agents, and systems using Oracle AI Database and OCI services

4.2K
Stars
75/100
信頼
カテゴリ: data監査

OLake - Fastest Databases, Kafka & S3 Replication to Apache Iceberg with Table optimization (Called OLake Fusion). ⚡ Efficient, quick and scalable data ingestion for real-time analytics. Supported sources : Postgres, MongoDB, MySQL, Oracle, MSSql, DB2, Kafka, S3.

1.4K
Stars
83/100
信頼
カテゴリ: data-analysis監査

Entity Framework EF Core efcore Bulk Batch Extensions with BulkCopy in .Net for Insert Update Delete Read (CRUD), Truncate and SaveChanges operations on SQL Server, PostgreSQL, MySQL, SQLite, Oracle

4.0K
Stars
73/100
信頼
カテゴリ: data-analysis監査

Command line tool to generate idiomatic Go code for SQL databases supporting PostgreSQL, MySQL, SQLite, Oracle, and Microsoft SQL Server

3.9K
Stars
78/100
信頼
カテゴリ: data-analysis監査

SQL query builder, written in c#, helps you build complex queries easily, supports SqlServer, MySql, PostgreSql, Oracle, Sqlite and Firebird

3.4K
Stars
80/100
信頼
カテゴリ: data-analysis監査

A self-learning skill layer for Claude Code that automatically distills, merges, updates, and prunes skills from real sessions.

413
Stars
75/100
信頼
カテゴリ: coding-agents監査

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査

Tanel Poder's Performance & Troubleshooting Tools for Oracle Databases

724
Stars
66/100
信頼
カテゴリ: data-analysis監査