Cost Analysis
Analyze infrastructure and operational costs with optimization recommendations
Best use case
Cost Analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze infrastructure and operational costs with optimization recommendations
Teams using Cost Analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cost-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Cost Analysis Compares
| Feature / Agent | Cost Analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze infrastructure and operational costs with optimization recommendations
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Cost Analysis Skill Analyze infrastructure and operational costs with optimization recommendations. ## Trigger Conditions - Monthly/quarterly cost review cycle - Budget threshold breach alert - New resource provisioning request - User invokes with "cost analysis" or "optimize spending" ## Input Contract - **Required:** Resource inventory or billing data - **Required:** Time period for analysis - **Optional:** Budget targets, comparison baselines ## Output Contract - Cost breakdown by service, team, and resource type - Cost-per-transaction trending - Optimization recommendations with savings estimates - Reserved vs. on-demand vs. spot mix recommendation ## Tool Permissions - **Read:** Billing data, resource configs, usage metrics - **Write:** Cost analysis reports - **Search:** Resource tags and attribution data ## Execution Steps 1. Collect billing data for the analysis period 2. Attribute costs by service and team using resource tags 3. Identify unattributed costs 4. Calculate cost-per-transaction trends 5. Identify top cost drivers and anomalies 6. Generate optimization recommendations (rightsizing, reservations, spot) 7. Estimate savings per recommendation ## Success Criteria - 100% of costs attributed to service/team - Cost anomalies flagged with explanation - Optimization recommendations include estimated savings - Cost-per-transaction trend reported ## Escalation Rules - Escalate if daily spend exceeds baseline by >20% - Escalate if unattributed costs exceed 10% of total - Escalate if optimization requires architectural changes ## Example Invocations **Input:** "Analyze our AWS costs for January and find savings" **Output:** Total: $45,200. Top 3: RDS ($18K — oversized instances, recommend r6g.xlarge→r6g.large, save $4K/mo), EKS ($12K — 40% idle capacity, recommend cluster autoscaler tuning), S3 ($8K — no lifecycle policy, archive to Glacier after 30d, save $3K/mo). Total potential savings: $7K/month.
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