Example prompts & workflows

Real prompts you can paste into any connected client, with the tools each one typically drives. Phrase questions naturally — the agent picks the tools. Prefix with “Using CloudQuell, …” if you have several connectors enabled.

Tip: these workflows also ship as installable FinOps skills — packaged runbooks your agent follows automatically, so you don’t have to steer each step.

Each workflow below is read-only unless noted.

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1. Anomaly investigation — “why did spend spike?”

The most common FinOps question, and the workflow the tools are shaped around.

“Using CloudQuell, what cost anomalies showed up this month, and what’s driving the biggest one?”

What happens:

1. list_anomalies — derives the month-over-month / new-service spikes, ranked by severity.
2. investigate_anomaly (service from step 1) — chains the weekly trend + MoM row, the per-resource breakdown for the anchor date, and the ±7-day daily series, and returns a verdict on whether the trend looks anomalous.
3. (optional) get_weekly_cost_slices (that service) — to chart the trend.

Follow-ups:

“Drill into AmazonRDS for 2026-06-15 — which resources and accounts drove it?” → investigate_anomaly { service: "AmazonRDS", date: "2026-06-15" }

“That spike is a known one-off backfill — dismiss it.” (write — needs admin:write + confirm) → dismiss_anomaly { service: "AmazonRDS", period: "2026-06", window: "mtd", reason: "known backfill", confirm: true } — window is required and forms part of the dismissal key (use the same value with undismiss_anomaly to reverse it).

2. Waste & savings — “where can we cut?”

“Using CloudQuell, show me our top 10 savings opportunities worth at least $100/month, and how much we could save in total.”

1. get_savings { minMonthlySavings: 100, limit: 10 } — returns the headline rollup (banked / open / potential), the by-action-type breakdown, and the top recommendations sorted by savings.

Follow-ups:

“Just the commitment-purchase ones for the prod account.” → get_savings { actionType: "PURCHASE", accountId: "prod" }

“Which services have the most untagged spend? We want to fix allocation first.” → get_tag_coverage { limit: 10 }

“Mark recommendation rec-123 as applied.” (write) → update_recommendation_status { recommendation_id: "rec-123", status: "applied", confirm: true }

3. Budgets & forecast — “are we on track?”

“Using CloudQuell, which budgets are over or forecast to go over this period?”

1. get_budget_status — joins each budget with current actual + forecast and a derived status (on_track / warning / over).
2. (optional) get_budgets / get_budget — to inspect a specific budget’s definition, thresholds, and scope.

Follow-ups:

“Set up a $50k monthly budget for prod AWS starting July 1, alert at 80% and 100%.” (write) → create_budget { name: "Prod AWS monthly", amount: 50000, start_date: "2026-07-01", period: "monthly", threshold_pcts: [80, 100], confirm: true }

4. Commitment coverage — “what should we renew?”

“Using CloudQuell, which Reserved Instances or Savings Plans expire soon, and how well are they utilized?”

1. get_expiring_commitments (defaults to expiring_soon) — the renewal list + the rollup (count expiring within 60 days, already expired, total dollars saved).
2. (optional) list_commitments — the full portfolio with utilization % to spot under-used commitments.

Follow-up:

“Cross-reference: are there purchase recommendations that would cover what’s expiring?” → get_savings { actionType: "PURCHASE" } alongside the expiry list.

5. Cost Q&A — “slice it for me”

“Using CloudQuell, what was our AWS spend by service in May, and how does it compare to April?”

1. query_costs { view: "overview", start_date: "2026-05-01", end_date: "2026-05-31" } — MTD/period totals + the per-service comparison.
2. For a finer pivot: query_costs { view: "detailed", group_by: "product" } (or usage_type / tag / account).

If you’re unsure of the exact filter values:

“What services and accounts can I filter on?” → get_metadata (or get_available_services / get_available_linked_accounts).

6. Allocation & cost centers — “who owns this spend?”

“Using CloudQuell, break down current spend by cost center, and show me what’s unallocated.”

1. get_allocation_breakdown — per-cost-center spend, rolled-up spend, % of total, and the synthetic Unallocated bucket.
2. (optional) list_cost_centers + list_allocation_rules — to see the taxonomy and the rules that produced the allocation.

7. Alerts & history — “did anything fire?”

“Using CloudQuell, have any cost alerts or budget thresholds fired since June 1? Any failed deliveries?”

1. list_triggered_alerts { since: "2026-06-01" } — the unified firing history.
2. list_triggered_alerts { since: "2026-06-01", severity: "failed" } — only failed deliveries.
3. (optional) list_alert_rules — the rules that could fire.

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A note on multi-step workflows

investigate_anomaly is a single tool that chains three backend reads for you — it mirrors the manual “anomaly → resource breakdown → daily trend” flow in one call. Prefer it over orchestrating the pieces yourself when you want a verdict on one service. Use the individual tools (list_anomalies, get_weekly_cost_slices, query_costs view=detailed) when you want to explore broadly first, then dive in.