跳到正文
cccskills
免费在 GitHub 公开

atlas

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)

中文简介正在准备中。当前显示原文说明。

集成工具Azure
查看安装方法

包含的文件(1)

  • SKILL.md5.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

atlas

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.

1. Scan-first, graph-second

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.

  • Primary — scan the user's real sources. Use 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).
  • Secondary — the Atlas knowledge graph. Atlas is a knowledge graph of agents, processes, data models, capabilities, workflows, and wiki pages reached through the 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.

2. When to use

Trigger phraseCommand
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

3. The need → real atlas pipeline (core method)

  1. Parse sources — interpret the stated need into concrete SOURCES: Azure subscription(s), git repos, local directories, URLs, plus the output dir. If the sources are genuinely ambiguous, run a short interview (AskUserQuestion). Per repo policy, interview ONLY when truly unclear.
  2. Scan cloud (primary) — for each Azure source, run read-only 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.
  3. Scan local (primary) — for each repo/dir, scan the filesystem + git (structure, submodules, manifests, languages, services, IaC) and write a real inventory citing real paths.
  4. Enrich (secondary) — map the discovered real systems against the Atlas graph for comparison context. Clearly secondary; never the headline.
  5. Synthesize — assemble a real, cross-linked layered atlas (components / processes / data / integrations / nuances) where EVERY item cites its real source, like SYSTEMS-ATLAS.md, plus a machine mirror.
  6. Converge (TDD) — each phase asserts its own checkable outputs before proceeding (see the atlas processes), iterating until the assertions pass.

4. How to delegate

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-nuances

Do not hand-roll orchestration when a process exists.

5. Guardrails

  • No fallbacks (repo rule). Skipping an out-of-scope source class (e.g. no cloud named) is correct scoping and must be recorded with a reason — it is NOT a silent fallback to the public graph. If you find yourself writing a real fallback, stop and fix the root cause.
  • Scan-first: every system/item in the atlas MUST cite a REAL source (an 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.
  • Read-only scanning only: az read verbs, git status/remote/log, filesystem reads. Never run mutating cloud/git/fs commands and never read secret values.
  • Keep breakpoints sparse; use them only when the sources to scan are genuinely ambiguous.
  • The real scanning is done BY the agent via its Bash tool inside the agent task prompt. Do not emit kind: 'shell' subtasks unless the user explicitly asks for a shell-oriented workflow.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Provide implementations of advanced data structures

中文简介正在准备中。当前显示原文说明。

a5c-ai/babysitter18632026年9月17日 更新

Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.

中文简介正在准备中。当前显示原文说明。

a5c-ai/babysitter18632026年9月17日 更新

WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.

中文简介正在准备中。当前显示原文说明。

a5c-ai/babysitter18632026年9月17日 更新

Coordinate Crew (persistent) and Polecat (transient) agents using Gas Town's hook-based work distribution and GUPP principle.

中文简介正在准备中。当前显示原文说明。

a5c-ai/babysitter18632026年9月17日 更新

Generate visual representations of algorithm execution

中文简介正在准备中。当前显示原文说明。

a5c-ai/babysitter18632026年9月17日 更新

Hierarchical coordination and drift detection with frequent checkpoints, shared memory coherence validation, role specialization enforcement, and short task cycles.

中文简介正在准备中。当前显示原文说明。

a5c-ai/babysitter18632026年9月17日 更新

查看 a5c-ai 的全部技能

报告此技能的问题