Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性，你确认后才安装。
读取 https://funcoding.ai/skills/a5c-ai/babysitter/atlas-graph-query/install.md ，按里面的步骤帮我安装这个 Skill。
A thin reference for the Atlas knowledge-graph MCP tool surface so any agent
(including sub-agents) can query the graph without re-deriving conventions. The
server URL is wired natively by the atlas plugin and is overridable via
ATLAS_MCP_URL. Never invent node ids — only use ids returned by these tools.
Position: this is the SECONDARY / enrichment layer. The
atlasplugin is scan-first — it inventories your REAL systems by scanning your actual sources (Azure via read-onlyaz, git repos, local directories) and process/data mining them. The graph queries below are used ONLY to add best-practice / comparison context to those already-discovered real systems. Do NOT use the graph as the primary content, and never pad a real inventory with generic catalog nodes. Tie every graph lookup back to a real scanned system.
mcp__atlas__atlas_public_searchFull-text/semantic search over the graph. Key params: q (query), optional
kind filter, limit. Prefer it to find seed/anchor nodes from need terms.
mcp__atlas__atlas_public_recordFetch one node's full record by id (fields + edges). Use expandNeighbors to
pull immediate relations in one call. Prefer it to read detail once you have ids.
mcp__atlas__atlas_public_neighborsTraverse the graph from a node. Key params: id, depth, edges (edge-kind
filter), kinds (node-kind filter). Prefer it to expand a subsystem from anchors.
mcp__atlas__atlas_public_kindsList all node kinds in the graph. Use to scope a domain to relevant kinds.
mcp__atlas__atlas_public_kindDescribe a single node kind (schema/fields). Use before relying on a kind's shape.
mcp__atlas__atlas_public_edge_kindsList all edge kinds. Use to understand how nodes relate.
mcp__atlas__atlas_public_edge_kindDescribe a single edge kind. Use to interpret a specific relation type.
mcp__atlas__atlas_public_clustersList graph clusters (thematic groupings). Use to scope a domain to cluster(s).
mcp__atlas__atlas_public_statsGraph-level counts/metrics. Use for sizing and sanity checks.
mcp__atlas__atlas_public_wiki_pageFetch a narrative wiki page for context. Use to capture human-readable nuance.
atlas_public_search(q=<need terms>) → take top ids.atlas_public_neighbors(id, depth=2, kinds=[...]).atlas_public_record(id, expandNeighbors=true).atlas_public_clusters → pick cluster → search/
neighbors scoped to it.
anthropics/skills180kApplies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
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anthropics/skills180kStop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
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