Provide implementations of advanced data structures
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Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill turns a stated need into a real systems atlas by SCANNING your
actual sources — Azure subscriptions (via read-only az), git repos, and local
directories — and process/data mining them, THEN enriching the result against the
Atlas knowledge graph. It is the brain of the atlas plugin. The scan is
PRIMARY; the graph is SECONDARY. For non-trivial runs it delegates orchestration
to babysitter:babysit using an atlas-specific .a5c process; for simple
lookups it queries the graph directly.
The output you want is an evidence-backed inventory of your systems — e.g.
azure-inventory.json (every real resource id + RG from az),
workspace-inventory.json (real repo/dir scan), processes.json (real mined
CI/CD/IaC/.a5c processes), and a cross-linked SYSTEMS-ATLAS.md. Every item must
cite its REAL source. Generic catalog nodes are NOT the deliverable.
Bash to run READ-ONLY scans:
az (account/group/resource list + per-service list/show) for Azure;
git + filesystem (Read/Glob) for repos and directories. NEVER invent
resource ids, regions, SKUs, or file paths — if you didn't observe it in real
output, it does not go in the atlas. Only scan the sources named in the need
(scoping, not a fallback).mcp__atlas__atlas_public_* MCP tools (server URL overridable via
ATLAS_MCP_URL). Use it ONLY to add best-practice / comparison context for the
real systems you found — never as the primary content, never to pad the atlas
with generic nodes. See the atlas-graph-query skill for the tool surface.| Trigger phrase | Command |
|---|---|
| scan/inventory my real systems (azure + repos + dirs), map them | /atlas:discover |
| mine the real processes in my repos/cloud (CI/CD, IaC, .a5c, cron) | /atlas:mine-processes |
| mine the real data stores/models in my cloud + repos | /atlas:mine-data |
| collect the real constraints/gotchas of my scanned systems | /atlas:collect-nuances |
AskUserQuestion). Per repo policy, interview ONLY when truly unclear.az and write
a real cloud inventory citing resource ids/RGs. Skip cleanly (record a reason)
if no cloud source is in scope — only scan what's named.SYSTEMS-ATLAS.md, plus a machine mirror.For any non-trivial run, hand off to babysitter:babysit (via the Skill tool)
naming the matching atlas process:
/atlas:discover → atlas-systems-discovery/atlas:mine-processes → atlas-process-mining/atlas:mine-data → atlas-data-mining/atlas:collect-nuances → atlas-collect-nuancesDo not hand-roll orchestration when a process exists.
az
resource id / RG, or a file path). Never invent resource ids, regions, SKUs, or
file paths. Never invent graph node ids either — only reference ids returned by
the Atlas tools, and keep graph content strictly secondary.az read verbs, git status/remote/log, filesystem
reads. Never run mutating cloud/git/fs commands and never read secret values.Bash tool inside the agent
task prompt. Do not emit kind: 'shell' subtasks unless the user explicitly
asks for a shell-oriented workflow.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
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Provide implementations of advanced data structures
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Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.
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WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.
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Coordinate Crew (persistent) and Polecat (transient) agents using Gas Town's hook-based work distribution and GUPP principle.
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Generate visual representations of algorithm execution
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Hierarchical coordination and drift detection with frequent checkpoints, shared memory coherence validation, role specialization enforcement, and short task cycles.
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