Agent skill · AI & Agents

atlas-graph-query

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)

a5c-aigithub.com/a5c-aiGitHub ↗
claude-codecodexread-onlyMIT
Install
npx skills add a5c-ai/babysitter --skill atlas-graph-query --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 3 KB
Bundled scripts: none
Version: 0.1.0
Allowed tools: mcp__atlas__atlas_public_searchmcp__atlas__atlas_public_recordmcp__atlas__atlas_public_neighborsmcp__atlas__atlas_public_kindsmcp__atlas__atlas_public_kindmcp__atlas__atlas_public_edge_kindsmcp__atlas__atlas_public_edge_kindmcp__atlas__atlas_public_clustersmcp__atlas__atlas_public_statsmcp__atlas__atlas_public_wiki_page
Path: plugins/atlas-unified/skills/atlas-graph-query/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,642
Language: JavaScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# atlas-graph-query 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 `atlas` plugin is > scan-first — it inventories your REAL systems by scanning your actual sources > (Azure via read-only `az`, 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. ## Tools ### `mcp__atlas__atlas_public_search` Full-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_record` Fetch one node's full record by `id` (fields + edges). Use `expandNeighbors` to pull immediate

What's inside
Steps it walks through
  1. Tools
  2. mcpatlasatlaspublicsearch
  3. mcpatlasatlaspublicrecord
  4. mcpatlasatlaspublicneighbors
  5. mcpatlasatlaspublickinds
  6. mcpatlasatlaspublickind
  7. mcpatlasatlaspublicedgekinds
  8. mcpatlasatlaspublicedgekind
  9. mcpatlasatlaspublicclusters
  10. mcpatlasatlaspublicstats
  11. mcpatlasatlaspublicwikipage
  12. Query recipes
More from babysitter
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About this skill
What does the atlas-graph-query skill do?

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)

How do I install it?

Run `npx skills add a5c-ai/babysitter --skill atlas-graph-query --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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