“knowledge-graph”的搜索结果

Read and refresh wp-rocket's pre-built dependency graph at .claude/graph/dependency-graph.json. Use to locate a class file, trace dependencies, find the ServiceProvider that wires a service, or enumerate Subscribers in a module — without re-scanning the codebase from scratch. This skill is primarily a reader: the graph is built (and incrementally refreshed) by `node bin/build-knowledge-graph.js`. Invoke this skill at session start (to refresh if stale) and before grep/glob searches for class relationships.

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Create an onboarding guide for new project members from a knowledge graph, a map of project components. The Markdown guide covers architecture, key concepts, a suggested learning path, important files, and areas that need extra care.

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• Preparing a guide for new team members
• Organizing project architecture and key files
• Planning a codebase walkthrough

Launch the Understand Anything web dashboard to explore an existing codebase knowledge graph, a map of its structure and relationships. Open the current project or a specified directory and receive the local access URL.

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• Explore an analyzed codebase visually
• Open the current project's dashboard
• View graph data from another project directory

Get a detailed explanation of a file, function, or module using an existing knowledge graph and its source code. It explains architectural roles, dependencies, callers, and data flow for readers who may not know the programming language.

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• Understand the role of a specific file
• Trace a function’s callers and dependencies
• Follow inputs, processing, and outputs

Ask questions about a codebase using a knowledge graph, a map of its components and relationships. Get explanations tied to specific files, functions, dependencies, and architectural layers, with warnings when the graph may be outdated.

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• Understanding how a feature works
• Finding callers and dependencies
• Locating code related to a concept

Analyze project code, configuration, and documentation to build a graph for interactive architecture exploration. It maps components and dependencies, supports Japanese descriptions, and can update the analysis based on changes since the previous analyzed commit.

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• Understand the structure of an unfamiliar project
• Explore dependencies between components
• Generate architecture descriptions in Japanese

Investigate a username, file, code identifier, URL, or concept by exploring connected entities across relevant sources. Produce a dossier with a summary, relationship diagram, source references, and a machine-readable JSON graph.

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• Mapping connections around a username or concept
• Investigating references to a file or code identifier
• Tracing links to an architecture decision record

Look up biomedical relationships in ARAX using explicit entity types and tightly bounded paths. Inspect direct links or paths through one intermediate entity, along with returned publications and source records, to identify candidates for further research.

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• Checking recorded drug–gene relationships
• Exploring paths between a drug and a disease
• Inspecting publications and original data sources

Turn documents, notes, research, or code into a knowledge graph that maps identifiable things and their relationships. Create or update entity files and typed connections, with source evidence attached for inspection and later visualization.

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• Map people and organizations across documents
• Organize research with traceable evidence
• Map relationships between software modules

Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.

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Capture, validate, query, and sync architectural patterns and design decisions in the knowledge graph

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Build, refresh and query a deterministic code knowledge graph to cut orientation-token cost. Triggers: knowledge graph, graphify, code graph, god nodes, orientation cost, map the codebase, what connects, callers of, blast radius.

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Structured data implementation, validation, and optimization. Covers JSON-LD patterns for 20+ schema types, rich snippet eligibility, AI search visibility, Knowledge Graph optimization, and CMS-specific deployment guides.

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Linked nodes drifting — a data / AI backdrop. A bold, canvas-based effect from Open Design's html-ppt fx pack — for slides and launch videos. Guards prefers-reduced-motion (does not start). One per screen; on web use sparingly.

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User-facing entry point for the wp-rocket issue workflow. Invoke directly to start a delivery run from a GitHub issue number, URL, or raw description. Runs inline in your conversation context; spawns specialist agents (ticket-writer, grooming-agent, challenger, backend-agent, frontend-agent, release-agent, lead-reviewer, qa-engineer) as isolated sub-agents; invokes supporting skills (knowledge-graph, dod, docs, issue-workflow) inline. Routes based on structured JSON outputs from each agent, manages loop counters, handles escalations, and maintains a live HTML run log.

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Design or audit a repo-local markdown knowledge graph with wiki links, source-code backlinks, drift checks, and searchable sections. Use when AGENTS.md/CLAUDE.md is too flat for a large codebase or when a custom harness needs durable structured project memory.

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Knowledge graph integration for token-efficient codebase understanding. Uses codebase-memory MCP for AST indexing, dependency graphs, and smart context selection. 6-71x token savings vs raw file reading.

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Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction workflows.

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Build structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.

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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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Use when asked to prove, disprove, investigate, or find evidence for a claim about people, concepts, ideas, or their relationships. Also use when asked to explore connections, find who influenced what, trace how ideas developed, or answer questions that require reasoning over multiple linked notes.

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