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Get Started Free →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,
.claude/skills/a5c-ai-atlas/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 429% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 5% | 0% |
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).
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.
| 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 |
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.
az and writea real cloud inventory citing resource ids/RGs. Skip cleanly (record a reason) if no cloud source is in scope — only scan what's named.
(structure, submodules, manifests, languages, services, IaC) and write a real inventory citing real paths.
graph for comparison context. Clearly secondary; never the headline.
processes / data / integrations / nuances) where EVERY item cites its real source, like SYSTEMS-ATLAS.md, plus a machine mirror.
proceeding (see the atlas processes), iterating until the assertions pass.
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.
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.
azresource 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, filesystemreads. Never run mutating cloud/git/fs commands and never read secret values.
ambiguous.
Bash tool inside the agenttask prompt. Do not emit kind: 'shell' subtasks unless the user explicitly asks for a shell-oriented workflow.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,929 | 10,671 | +80% | 1 | 1 | 0% | 392 | 2,430 | +520% | 0 | 0 | — |
case-02 | fail→fail | 28,371 | 7,967 | -72% | 1 | 1 | 0% | 6,040 | 2,055 | -66% | 0 | 0 | — |
case-03 | fail→pass | 2,917 | 3,694 | +27% | 1 | 1 | 0% | 353 | 1,868 | +429% | 0 | 0 | — |
case-04 | fail→fail | 29,725 | 6,906 | -77% | 1 | 1 | 0% | 4,832 | 1,773 | -63% | 0 | 0 | — |
case-05 | fail→fail | 33,607 | 10,754 | -68% | 1 | 1 | 0% | 3,063 | 1,999 | -35% | 0 | 0 | — |
case-06 | fail→fail | 7,144 | 9,219 | +29% | 1 | 1 | 0% | 1,280 | 2,031 | +59% | 0 | 0 | — |
case-07 | fail→fail | 6,740 | 3,404 | -49% | 1 | 1 | 0% | 1,268 | 1,905 | +50% | 0 | 0 | — |
case-08 | fail→fail | 19,100 | 14,448 | -24% | 1 | 1 | 0% | 1,489 | 2,172 | +46% | 0 | 0 | — |
case-09 | fail→fail | 15,872 | 8,859 | -44% | 1 | 1 | 0% | 1,095 | 2,754 | +152% | 0 | 0 | — |
case-10 | fail→pass | 10,505 | 4,685 | -55% | 1 | 1 | 0% | 1,963 | 1,896 | -3% | 0 | 0 | — |
case-11 | fail→fail | 10,465 | 33,991 | +225% | 1 | 1 | 0% | 1,967 | 2,408 | +22% | 0 | 0 | — |
case-12 | fail→fail | 2,870 | 9,829 | +242% | 1 | 1 | 0% | 403 | 2,125 | +427% | 0 | 0 | — |
case-13 | fail→fail | 6,370 | 7,544 | +18% | 1 | 1 | 0% | 1,252 | 1,763 | +41% | 0 | 0 | — |
case-14 | fail→fail | 12,007 | 11,222 | -7% | 1 | 1 | 0% | 2,284 | 1,831 | -20% | 0 | 0 | — |
case-15 | fail→fail | 3,940 | 6,364 | +62% | 1 | 1 | 0% | 507 | 2,386 | +371% | 0 | 0 | — |
case-16 | fail→fail | 11,016 | 8,648 | -21% | 1 | 1 | 0% | 2,174 | 2,890 | +33% | 0 | 0 | — |
case-17 | fail→fail | 8,130 | 3,197 | -61% | 1 | 1 | 0% | 1,297 | 1,791 | +38% | 0 | 0 | — |
case-18 | fail→pass | 7,451 | 2,051 | -72% | 1 | 1 | 0% | 993 | 1,615 | +63% | 0 | 0 | — |
case-19 | fail→pass | 6,726 | 3,379 | -50% | 1 | 1 | 0% | 1,156 | 1,934 | +67% | 0 | 0 | — |
case-20 | fail→fail | 17,023 | 10,946 | -36% | 1 | 1 | 0% | 2,650 | 2,604 | -2% | 0 | 0 | — |
case-21 | fail→pass | 12,317 | 4,680 | -62% | 1 | 1 | 0% | 2,016 | 2,109 | +5% | 0 | 0 | — |
case-22 | fail→fail | 3,202 | 3,563 | +11% | 1 | 1 | 0% | 544 | 1,838 | +238% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 13 counted toward the lift figure. The other 9 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +23 percentage points is the difference between those two pass rates over the 13 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.