Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.
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。
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS). Use when reviewing SQL, schema, or functions written for PostgreSQL.
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 PostgreSQL logical replication slots retain WAL, become orphaned or invalid after switchover, or must be proven synchronized and usable before failover.
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。
Migrates Oracle PL/SQL stored procedures to PostgreSQL PL/pgSQL. Translates Oracle-specific syntax, preserves method signatures and type-anchored parameters, leverages orafce where appropriate, and applies COLLATE "C" for Oracle-compatible text sorting. Use when converting Oracle stored procedures or functions to PostgreSQL equivalents during a database migration.
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。
Creates integration test cases for .NET data access artifacts during Oracle-to-PostgreSQL database migrations. Generates DB-agnostic xUnit tests with deterministic seed data that validate behavior consistency across both database systems. Use after the test project is scaffolded, to cover a project's data access code before porting it.
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。
Makes the agent list the files it needs to see before answering a question about the codebase, instead of guessing. Use when a question depends on code the agent has not read yet, or when earlier answers were confidently wrong about the code.
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 a long session should continue in a fresh agent — context is nearly full, compaction would lose detail, or the work is moving to another tool or teammate. Compacts the conversation into a handoff document in the OS temp directory, pointing at existing specs, plans, and diffs instead of repeating 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。
Refactors and reviews SwiftUI views: consistent layout ordering, subview extraction, dependency injection, Observation, and MV versus view-model patterns. Use when SwiftUI views have grown large or inconsistent.
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。
Implements, adopts, and reviews iOS 26+ Liquid Glass in SwiftUI: glassEffect, GlassEffectContainer, correctness, performance, and design fit. Use when adding Liquid Glass to a SwiftUI app or refactoring existing UI to the new design language.
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。
Interactive onboarding tour for the context-matic MCP server. Walks the user through what the server does, shows all available APIs, lets them pick one to explore, explains it in their project language, demonstrates model_search and endpoint_search live, and ends with a menu of things the user can ask the agent to do. USE FOR: first-time setup; "what can this MCP do?"; "show me the available APIs"; "onboard me"; "how do I use the context-matic server"; "give me a tour". DO NOT USE FOR: actually integrating an API end-to-end (use integrate-context-matic instead).
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 Entity Framework Core — modeling entities and relationships, migrations, LINQ query performance, change tracking, N+1 problems, and connection and transaction handling.
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。
Grounds every implementation decision in official documentation. Use when you want to verify an approach against the official docs before implementing it, or when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.
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。
Writes and reviews dbt projects targeting Snowflake: models, schema.yml metadata, sources, tests, macros, and project structure. Use when building or reviewing dbt models, generating dbt YAML, or applying dbt best practices.
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 designing or auditing long-term memory for persistent AI agents. Stores immutable source evidence before canonical facts, links beliefs to provenance, applies novelty-aware ADD/UPDATE/NOOP write gating, keeps uncertain merges explicit, separates retrieval from answer generation, supports deletion, and evaluates memory failures layer by layer.
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。
Discovers and integrates third-party APIs using the context-matic MCP server. Uses `fetch_api` to find available API SDKs, `ask` for integration guidance, `model_search` and `endpoint_search` for SDK details. Use when the user asks to integrate a third-party API, add an API client, implement features with an external API, or work with any third-party API or SDK.
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 turning GitHub issues or bug reports into reproducible coding-agent repair loops with sandboxed execution, focused tests, patch generation, and regression verification.
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。
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
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。
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.
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。
A persistent, compounding knowledge base combining Karpathy's LLM Wiki pattern with a structured knowledge graph. Ingest documents once — the LLM writes wiki pages, extracts entities/relations into the graph, and stores raw content for evidence retrieval. Knowledge accumulates and cross-references; it is never re-derived from scratch. Use when building a knowledge base from many documents that should accumulate and cross-reference over time instead of being re-read each session.
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。
Audits SwiftUI performance: rendering, scrolling, CPU and memory, unnecessary view updates, body re-evaluations, and layout thrashing, with Instruments guidance when code review is inconclusive. Use when a SwiftUI screen stutters, scrolls poorly, or uses too much CPU.
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 a coding agent (Claude Code, Cursor, Codex, Copilot CLI) burns through tokens or usage limits, sessions get slow or hit context limits, or the user asks to cut agent cost. Measures where the tokens go, then applies levers in order: session hygiene, always-loaded context (CLAUDE.md, skills, MCP), tool-output filtering, subagent delegation, output verbosity, and model/effort choice.
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。
This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent 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。
Terse caveman voice: answer first, fluff gone, every technical fact kept. Use for /caveman, "caveman mode", "talk like caveman", "be brief", "less tokens". Stays on until "stop caveman" or "normal mode".
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。
Maps the files relevant to a task before any edit: files to modify, dependencies that may need updates, test files, reference patterns to follow, and risks such as API, migration, or config changes. Use at the start of a non-trivial change in an unfamiliar codebase.
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。