「connection」の検索結果

Map curriculum connections across multiple subjects for a real-world problem or authentic context. Use when planning cross-curricular projects or connecting content to real 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。

Reviews sqlx database code for compile-time query checking, connection pool management, migration patterns, and PostgreSQL-specific usage. Use when reviewing Rust code that uses sqlx, database queries, connection pools, or migrations. Covers offline mode, type mapping, and transaction 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。

Reviews PostgreSQL code for indexing strategies, JSONB operations, connection pooling, and transaction safety. Use when reviewing SQL queries, database schemas, JSONB usage, or connection management.

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。

Meta-mathematical connections, cross-domain synthesis, and the Complex Plane as a navigational tool. Classifies problems by quadrant (Abstract/Embodied x Logic/Creativity), routes them to relevant domains, and traces dependency chains. Use when classifying mathematical problems across domains, navigating the Complex Plane of Experience, finding cross-domain connections, or building multi-domain solution strategies.

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。

Design the Soundcraft Ui24R adapter boundary for AI-Controlled Party: dry-run, Bitfocus Companion, direct Node bridge, connection health, config, failure handling, and live-validation gates. Use whenever a task mentions Soundcraft, Ui24R, snapshots, cues, Companion, soundcraft-ui-connection, mixer bridge, or revising the adapter design.

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。

First-run guided setup for a fresh gAIOS clone. Interviews the user (identity, role, priorities, voice, connections, team, pain, guardrails) and fills the templates — CLAUDE.md, context/, references/voice.md, connections.md — then introduces the WAT / wiki / workflows / experiments features. Trigger on "/setup", "set me up", "get started", "onboard me", or a fresh clone where CLAUDE.md still has {{placeholders}}.

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 the user says 'set up respira', 'respira not connecting', 'check my respira connection', or 'fix respira'. Walks through first-time setup and diagnoses connection failures: timeouts, 401s, 403s, and plugin or server version mismatches.

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。

Parse, search, analyze, and ingest LinkedIn GDPR data exports into structured JSON or RLAMA for semantic search. Covers messages, connections, profile data, and Markdown export. Requires a LinkedIn GDPR ZIP file. Triggers on 'LinkedIn data', 'search messages', 'analyze connections', 'LinkedIn export', 'GDPR download'.

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。

Generates a single LinkedIn connection request message using Jay Abraham's Preeminence approach. Lead with their work, peer-to-peer tone, no pitch, under 300 characters.

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。

Score a PhD applicant's profile and rank candidate advisors using a connection-first 4.0-scale scoring system. Best-supported for physics / HEP and materials science (MSE), with the scoring engine extensible to chemistry, biology, CS, math, EE, ChemE, earth science (each with field-specific caveats — see references/journal_tiers.md). Use when the user wants to evaluate their PhD application chances, find matching advisors at top US programs, score a CV for graduate school, or compare candidate professors. Also triggers when the user mentions phdtaketaketake or its connection-first philosophy of valuing advisor network over h-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。

Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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 and explore an Obsidian vault through the NoteBrain CLI (semantic search, tags, backlinks, connections, hidden links, boosted retrieval). Use it whenever the user mentions their notes, knowledge base, Obsidian vault, semantic search, finding connections or unlinked notes, or asks exploratory questions like "what do I know about X", "find notes related to Y", "what connects to Z", or "summarize my notes on W" — even when they never say NoteBrain, vector search, or ChromaDB.

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 building and deploying Atlassian Forge Teamwork Graph connector apps that ingest external data into Atlassian's Teamwork Graph, making it searchable in Rovo Search and surfaced in Rovo Chat. Use when the user wants to build a Forge connector, ingest external data into Atlassian, connect a third-party tool (e.g. Google Drive, ServiceNow, Salesforce) to Atlassian, make external content searchable in Rovo, build a graph:connector module, use the @forge/teamwork-graph SDK, or implement onConnectionChange / validateConnection functions.

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。

Native WebSocket API patterns, connection lifecycle, reconnection strategies, heartbeat, message typing, binary data, custom 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。

Socket.IO v4.x client patterns, connection lifecycle, reconnection, authentication, rooms, namespaces, acknowledgments, binary data, TypeScript integration

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。

Wire the Prisma Next runtime — `db.ts` setup using `postgres<Contract>(...)` from `@prisma-next/postgres/runtime`, `sqlite<Contract>(...)` from `@prisma-next/sqlite/runtime`, or `mongo<Contract>(...)` from `@prisma-next/mongo/runtime`; middleware composition (telemetry from `@prisma-next/middleware-telemetry`; lints and budgets), `DATABASE_URL` config, per-environment branching, switching between Postgres, SQLite, and Mongo façades. Use for db.ts, postgres(), sqlite(), mongo(), middleware, telemetry, lints, budgets, DATABASE_URL, .env, connection pool, poolOptions, dev vs prod config, transactions, db.transaction, read replicas, multi-database, script won't exit, hangs, close connection, db.end, db.close, pool.end, [Symbol.asyncDispose], await using.

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。

Detects command-and-control beacons in network logs by analyzing connection timing: measuring inter-arrival intervals, accounting for jitter, and scoring regularity per source/destination pair to surface periodic callbacks. Activates for requests to hunt C2 beaconing, find periodic callbacks, or analyze connection-interval regularity.

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。

Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.

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-level structural engineering covering structural analysis, steel and concrete design, load combinations, deflection, connections, and building codes.

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 Exasol `IMPORT` and `IMPORT INTO` SQL plus `exapump upload` local file workflows for moving data into Exasol. Covers CSV, FBV, and Parquet, `CREATE CONNECTION` connection objects for import, cloud credential patterns for S3, Azure Blob Storage, and Google Cloud Storage (GCS), reject handling, and staging-based import workflows. Native IMPORT does not read Avro, ORC, or Delta — those object-storage formats need the Cloud Storage Extension 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。

Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.

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 Exasol `EXPORT` and `EXPORT INTO` SQL plus `exapump export` local file workflows for moving data out of Exasol. Covers native CSV/FBV export, local CSV/Parquet exports with exapump, `CREATE CONNECTION` connection objects for export, FTP, SFTP, HTTP, HTTPS, and cloud targets on S3, Azure Blob Storage, and Google Cloud Storage (GCS), reject limits, and export credential 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。

Figmaデザインをノード情報を活用してレビューし、該当箇所に個別コメントを投稿する。Use when (1) ユーザーがFigmaデザインのレビューを依頼した時 (2) 実装者目線、エンジニア視点でのフィードバックが必要な時 (3) デザインに対してFigma上にコメントを残したい時。padding/margin、色のコントラスト、グリッド整合性などベーシックなデザインチェックも実施。Requires Figma MCP connection and FIGMA_TOKEN environment variable.

Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".

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。