「db」の検索結果

The craft of producing creative assets that communicate brand, product, and message effectively. Creative communications bridges strategy and execution—translating brand positioning and marketing goals into visual, audio, and interactive experiences that resonate with audiences. This skill covers creative briefing, creative production workflows, asset creation, creative feedback, and cross-channel creative adaptation. Great creative communications amplifies strategy; it doesn't replace it. Use when "creative, creative brief, assets, video, motion, animation, creative production, photoshoot, visual content, creative review, ad creative, creative feedback, creative, production, video, motion, assets, briefs, feedback, visual-content" mentioned.

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Expert in the human side of code review. Covers giving feedback that lands, receiving criticism gracefully, navigating disagreements, and building a healthy review culture. Understands that code review is as much about relationships as it is about code quality. Use when "code review, PR feedback, review comments, how to give feedback, harsh review, review culture, constructive criticism, " mentioned.

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。

Run Claude Code INSIDE a ca-sandbox box (`--with-claude`). Routed to when the user wants an agent loop running against an isolated, ephemeral sandbox rather than the host. Authenticates via an env-injected CLAUDE_CODE_OAUTH_TOKEN with no host bind of ~/.claude; the image pins the CLI and disables the autoupdater; HOME is backed by a named volume so the .claude state persists across restart. Five gated phases — posture, image, token, run, teardown. The hard default is offline or Anthropic-domains-only egress, and the token volume is NEVER co-mounted with an untrusted-code run; both are enforced, not advised.

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 considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This skill covers how to obtain important concepts, calculations, definitions, thresholds, and others from the handbook and how to do cross validations between handbook and datasets.

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。

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, AuraGraphDataScience, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection with gds.graph.project.cypher and gds.graph.project.remote, gds.graph.project.native, gds.graph.construct, graphdatascience client 2.0 session endpoints, async compute and gds.jobs, AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle. Use for AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads. Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.

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 writing or changing analytical SQL or a dbt model — CTE structure, window functions, grain and join correctness, dbt data tests, unit tests, contracts and incremental models, and how to run 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。

Design database schemas with Prisma/Drizzle, PostgreSQL index strategy (B-tree, GIN, GiST, BRIN, Hash), query optimization (EXPLAIN ANALYZE), migration safety (expand/contract, zero-downtime), and sharding/partitioning. Use when user asks to design schema, create migrations, optimize slow queries, add indexes, choose between SQL/NoSQL, or set up Prisma/Drizzle. Do NOT use for data warehouse dimensional modeling, ETL pipeline design, or non-relational (MongoDB, DynamoDB) schema 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。

Use when an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.

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。

Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick.

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。

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

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。

Promote a validated pattern into a reusable Skill in AgentDB's skill library. Use when the same approach has worked 3+ times across episodes, or when the user explicitly says "make this a skill" / "save this as reusable".

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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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 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。

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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。

K-hop traversal from a starting node in AgentDB's graph. Use to explore neighborhoods, find reachable nodes, or visualize a memory's "context".

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Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.

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。

Retrieve relevant memories for the current task from AgentDB. Use at the start of a task to load prior knowledge, when stuck to surface what worked before, or when the user asks "what do we know about X" / "have we done this before?"

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 persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Train one of AgentDB's 9 RL algorithms on a stream of episodes. Use when the user has accumulated successful/failed episodes and wants to derive a policy, or when a task type is repeated enough to benefit from RL routing.

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。

Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges

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。

Execute Cypher queries against AgentDB's graph backend. Use when the user wants to write a custom traversal that the standard tools don't cover, or when explaining graph state.

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。

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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。

Record a causal relationship between two memories in AgentDB — "X caused Y", "A supersedes B", "patch-foo depends-on patch-bar". Use when the user is documenting cause/effect, dependencies, supersessions, or after-action 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。