Purpose
Turn a raw cloud billing export or service/team cost breakdown into a prioritized optimization plan — surfacing the top spend drivers, quantifying waste, and producing rightsizing and commitment-discount recommendations with dollar savings estimates. Solves the recurring problem of a cloud bill that grows faster than anyone can explain, with no clear list of what to fix first.
When to Use
Invoke this skill when the user asks to:
- Analyze a cloud bill or cost breakdown by service, team, or account
- Diagnose why cloud spend spiked or is trending up month-over-month
- Find waste — idle, orphaned, or over-provisioned resources
- Get rightsizing recommendations for compute, storage, or database instances
- Build a Reserved Instance / Savings Plan / Committed Use Discount strategy
- Identify untagged or unallocated spend and improve cost accountability
Inputs Required
Before starting, collect:
- Cost data: Billing export, Cost Explorer breakdown, or cost-by-service/team table (CSV, JSON, or pasted table)
- Cloud provider: AWS / Azure / GCP / multi-cloud
- Time period: What range the data covers (single month, trailing 3 months, etc.)
- Total spend: Monthly cloud spend total for context
- Optimization goal: Target savings %, a spend ceiling, or "find everything"
- Commitment appetite: Willingness to buy Reserved Instances / Savings Plans / CUDs (none / 1-year / 3-year)
- Tagging/allocation state: Whether resources are tagged by team/environment, and how much spend is untagged
If commitment appetite or tagging state is unknown, assume 1-year commitment willingness and treat allocation quality as a diagnostic finding to report.
Instructions
Structure the entire analysis through the FinOps Foundation Inform → Optimize → Operate framework. Each phase builds on the last.
Phase 1 — INFORM: Cost Visibility & Attribution
Step 1: Identify the top cost drivers
- Rank spend by service (compute, storage, database, network, managed services) and by team/account if that dimension exists
- Report the top 5 line items with each one's share of total spend
- Apply the 80/20 lens: identify which handful of services or teams make up ~80% of the bill — this is where optimization effort concentrates
Step 2: Trend and anomaly detection
- If multiple periods are provided, compute month-over-month change per service and flag any line item growing >20% without an obvious cause
- Flag sudden step-changes (a service appearing or doubling) as spike candidates
- Distinguish organic growth (usage up) from waste growth (cost up without proportional value)
Step 3: Allocation and tagging assessment
- Quantify untagged / unallocated spend as a dollar amount and % of total
- Flag any team or environment that cannot be attributed
- State the attribution confidence: what % of spend can be tied to an owner
Phase 2 — OPTIMIZE: Waste Elimination & Rightsizing
Step 4: Flag waste (idle & orphaned resources) Scan for the classic waste patterns and estimate the recoverable spend for each:
- Idle compute: instances with low utilization signatures or that appear underused relative to size
- Orphaned resources: unattached storage volumes, unassociated static IPs, old snapshots, idle load balancers
- Over-provisioned instances: instance sizes larger than the workload requires
- Non-production waste: dev/staging/test resources running 24/7 that could run on a schedule
- Storage tier waste: hot-tier storage holding cold/archival data
Step 5: Rightsizing recommendations For each rightsizing candidate:
- Current configuration (service, size, monthly cost)
- Recommended change (downsize, schedule, tier change, or terminate)
- Estimated monthly savings ($ and %)
- Risk level (Low / Medium / High) and a one-line rationale
Step 6: Commitment discount strategy Based on stable, predictable baseline usage and the stated commitment appetite:
- Identify the always-on baseline suitable for Reserved Instances / Savings Plans (AWS), Reservations (Azure), or Committed Use Discounts (GCP)
- Recommend coverage % (typically cover 70–85% of stable baseline, leave headroom for on-demand)
- Compare 1-year vs. 3-year and Compute vs. Instance-specific commitments where relevant
- Estimate savings vs. on-demand (typically 30–60% depending on commitment term)
Phase 3 — OPERATE: Sustaining the Gains
Step 7: Ranked action plan Consolidate every recommendation into a single priority-ranked list — ordered by estimated savings weighted against implementation effort and risk. Quick wins (low effort, low risk, real savings) go first.
Step 8: Governance & recurring practice
- Recommend tagging policy fixes to close the untagged-spend gap
- Set anomaly alert thresholds (e.g., alert on any service growing >X% week-over-week)
- Define a reporting cadence and an owner for each recommendation category
Output Format
- Structure: Three phases (Inform, Optimize, Operate); waste findings and rightsizing candidates as tables; final action plan as a ranked numbered list
- Length: 1,000–1,400 words
- Format: Structured document with tables for cost drivers, waste items, and rightsizing candidates
- Sections: Executive summary (total spend + total identified savings) → Inform (top drivers, trends/anomalies, allocation) → Optimize (waste, rightsizing, commitments) → Operate (ranked action plan, governance)
- Tone: FinOps practitioner — analytical, dollar-first, no fluff; every recommendation carries a number
Quality Checklist
Before delivering output, verify:
- [ ] Executive summary states total analyzed spend and total estimated savings ($ and % of bill)
- [ ] Top 5 cost drivers are named with each one's share of total spend
- [ ] Untagged/unallocated spend is quantified as a dollar amount and %
- [ ] Every rightsizing and waste recommendation includes an estimated monthly savings figure
- [ ] Commitment recommendation specifies coverage %, term, and savings vs. on-demand
- [ ] Final action plan is ranked by savings-vs-effort — quick wins first, not arbitrary order
Notes
- Utilization-based waste flags are inferential when the export lacks metrics — state assumptions and recommend confirming against CloudWatch / Azure Monitor / Cloud Monitoring before terminating anything
- The single fastest ROI is usually orphaned resources (zero risk, immediate savings) — surface those before touching production sizing
- Never recommend committing to more than the demonstrable stable baseline; over-committing on RIs/Savings Plans is its own form of waste
- If the export contains no time dimension, note that anomaly detection is limited to single-period outliers and recommend pulling a trailing 3-month view for the next pass
How to use this skill
This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.
Installing in Claude Code:
~/.claude/skills/<folder-name>/SKILL.md— auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)<your-project>/.claude/skills/<folder-name>/SKILL.md— auto-loads only when working in that project (recommended for project-specific skills)
Restart Claude Code or open a new session to register.
Using in Claude.ai, ChatGPT, Gemini, or other AI tools:
- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills