「relations」の検索結果

Map a complex topic by placing factors on hexagonal tiles where adjacency signals a claimed relationship. Use when students need to surface hidden connections in a system before analysis or action.

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Developer relations - community building, documentation, DevRel metrics.

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Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.

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Designs convex/schema.ts tables, validators, indexes, and relationships, and keeps the schema honest as data evolves. Use when creating tables, adding fields, choosing index fields, modeling relationships, or when a validator error appears at deploy time.

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Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

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Provides comprehensive Drizzle ORM patterns for schema definition, CRUD operations, relations, queries, transactions, and migrations. Proactively use for any Drizzle ORM development including defining database schemas, writing type-safe queries, implementing relations, managing transactions, and setting up migrations with Drizzle Kit. Supports PostgreSQL, MySQL, SQLite, MSSQL, and CockroachDB.

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Personal CRM and contact management - manage contacts, relationships, and interactions. Use when the user discusses contacts, people, or relationship tracking.

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Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.

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This skill should be used when the user asks to "create a character", "update a character", "add a character", "build a family tree", "character relationships", "character timeline", "character arc", "character profile", "relationship graph", "name a character", or needs to manage characters in a story project. NOT for recording or checking character voices (use voice-style), rewriting dialogue so the voices differ (use line-editing), or a thematic arc's lie, truth, and arc type (use theme-craft).

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Turn a work-items.md file (produced by /plan-work-items) into Linear issues, one per slice, in a single target Linear team. Use when you want to publish work items as Linear issues, create implementation tickets to track in Linear, or push a broken-down plan into a Linear team. Requires a configured Linear MCP server and a target team. Reads the team's real workflow states, labels, Projects, and members and resolves every option against them before creating anything; defaults each issue to the team's initial state, unassigned, uncategorized, with no parent or Project unless you ask. Links within-file `Depends on` relationships as native Linear "blocked by" relations and annotates the source file so re-runs resume cleanly. Does not produce the work-items file itself — use plan-work-items first. Does not post to Jira — use work-items-to-jira. Does not post to GitHub — use work-items-to-issues.

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Applies abstract interpretation using different abstract domains (intervals, octagons, polyhedra, sign, congruence) to statically analyze program variables and infer invariants, value ranges, and relationships. Use when analyzing program properties, inferring loop invariants, detecting potential errors, or understanding variable relationships through static analysis.

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Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.

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Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.

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Knowledge graph specialist for entity and causal relationship modelingUse when "knowledge graph, graph database, falkordb, neo4j, cypher query, entity resolution, causal relationships, graph traversal, graph-database, knowledge-graph, falkordb, neo4j, cypher, entity-resolution, causal-graph, ml-memory" mentioned.

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Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.

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Use when building Spring Boot applications with Neo4j using Spring Data Neo4j (SDN 7.x/8.x) — @Node entity mapping, @Relationship, @RelationshipProperties, Neo4jRepository, ReactiveNeo4jRepository, @Query annotations, application.yml configuration, projections, Neo4jClient, Neo4jTemplate, transactions, auditing, or Spring AI Neo4jVectorStore vector search. Does NOT handle raw Java driver code without Spring — use neo4j-driver-java-skill. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle driver version upgrades — use neo4j-migration-skill.

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Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser. Covers @neo4j-nvl/base (NVL class, nodes/relationships, Canvas vs WebGL renderer), @neo4j-nvl/interaction-handlers (ZoomInteraction, PanInteraction, DragNodeInteraction, ClickInteraction, HoverInteraction, BoxSelectInteraction, LassoInteraction, KeyboardInteraction), and @neo4j-nvl/react (InteractiveNvlWrapper, BasicNvlWrapper, StaticPictureWrapper). Use when rendering a Neo4j graph in a browser, feeding driver results through nvlResultTransformer, choosing Canvas vs WebGL, wiring node/relationship click/hover/drag handlers, or embedding NVL in React, Vite, or vanilla JS apps. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle driver lifecycle, sessions, or executeQuery setup — use neo4j-driver-javascript-skill. Does NOT handle GraphVisualization/Needle default embed — use @neo4j-ndl/react.

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Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.

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Search and manage hyperedges — n-ary relationships between memories. Use for swarm membership, multi-cause incidents, or any "this involves all of (A, B, C, D)" relationship that doesn't fit a binary edge.

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。

Map a complex topic by placing factors on hexagonal tiles where adjacency signals a claimed relationship. Use when students need to surface hidden connections in a system before analysis or action.

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。

Craft CMS 5 content modeling — sections, entry types, fields, Matrix, relations, project config, and content architecture strategy. Covers choosing section types, designing entry types and field layouts, selecting field types, configuring Matrix and nested entries, relations and eager loading, and multi-site propagation.

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Reviews SQLAlchemy code for session management, relationships, N+1 queries, and migration patterns. Use when reviewing SQLAlchemy 2.0 code, checking session lifecycle, relationship() usage, or Alembic migrations.

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Universal Digital Immortality Engine — Distill anyone or anything you care about into an interactive digital soul. 给灵魂开个 GitHub。 Trigger whenever user wants to: - Create, distill, or awaken a Relic, digital soul, persona, or clone from chat logs, memories, or voice ("create a relic", "digital twin of my grandma", "preserve memory of pet/team", "帮我唤醒/蒸馏/做个Relic", "保存/纪念XX的记忆", "Relicを作りたい/記憶を保存"). - Talk to, chat with, or remember a loved one, pet, mentor, or companion ("talk to grandma", "chat with my cat", "跟奶奶聊天", "召唤咪咪", "おばあちゃんと話したい"). - Preserve or distill workplace history, team culture, or expert knowledge ("archive team culture", "distill business expert", "飞书/Slack协作记忆蒸馏"). Supports: Humans, Pets, Relationships, Teams, Places, Moments, Public figures, Domain experts.

Interpersonal communication theory and practice for one-on-one and small group interaction. Covers communication models (linear, transactional, constructivist), self-disclosure and Johari Window, relational dialectics, communication climate, supportive versus defensive communication, feedback models, and adapting style across relationships and contexts. Use when analyzing communication dynamics, improving relationships, giving feedback, or understanding communication breakdowns between individuals.

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。