Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ancoleman/ai-design-components --skill optimizing-costsgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ancoleman/ai-design-components/optimizing-costs)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/optimizing-costs"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/optimizing-costs/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/optimizing-costs"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/optimizing-costs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.04846 |
| Opus 5.5 | $0.00020 | $0.01938 |
| Sonnet 5.5 | $0.00010 | $0.00969 |
| Haiku 4.5 | $0.00005 | $0.00485 |
Grade A, and why
optimizing-costs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 491 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cost Optimization
Purpose
Cloud cost optimization transforms uncontrolled spending into strategic resource allocation through the FinOps lifecycle: Inform, Optimize, and Operate. This skill provides decision frameworks for commitment-based discounts (Reserved Instances, Savings Plans), right-sizing strategies, Kubernetes cost management, and automated cost governance across multi-cloud environments.
When to Use This Skill
Invoke cost-optimization when:
- Reducing cloud spend by 15-40% through systematic optimization
- Implementing cost visibility dashboards and allocation tracking
- Establishing budget alerts and anomaly detection
- Optimizing Kubernetes resource requests and cluster efficiency
- Managing Reserved Instances, Savings Plans, or Committed Use Discounts
- Automating idle resource cleanup and right-sizing recommendations
- Setting up showback/chargeback models for internal teams
- Preventing cost overruns through CI/CD cost estimation (Infracost)
- Responding to finance team requests for cloud cost reduction
FinOps Principles
The FinOps Lifecycle
┌─────────────────────────────────────────────────────┐
│ INFORM → OPTIMIZE → OPERATE (continuous loop) │
│ ↓ ↓ ↓ │
│ Visibility Action Automation │
└─────────────────────────────────────────────────────┘
Inform Phase: Establish cost visibility
- Enable cost allocation tags (Owner, Project, Environment)
- Deploy real-time cost dashboards for engineering teams
- Integrate cloud billing data (AWS CUR, Azure Consumption API, GCP BigQuery)
- Set up Kubernetes cost monitoring (Kubecost, OpenCost)
Optimize Phase: Take action on cost drivers
- Purchase commitment-based discounts (40-72% savings)
- Right-size over-provisioned resources (target 60-80% utilization)
- Implement spot/preemptible instances for fault-tolerant workloads
- Clean up idle resources (unattached volumes, old snapshots)
Operate Phase: Automate and govern
- Budget alerts with cascading notifications (50%, 75%, 90%, 100%)
- Automated cleanup scripts for idle resources
- CI/CD cost estimation to prevent surprise increases
- Continuous monitoring with anomaly detection
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/ci-cd/infracost-github-action.yml 3.9 KB
- examples/kubernetes/kubecost-values.yaml 5.8 KB
- examples/kubernetes/resource-quotas.yaml 4.7 KB
- examples/terraform/aws-cost-optimization.tf 6.4 KB
- outputs.yaml 8.4 KB
- references/cloud-specific-tactics.md 8.8 KB
- references/commitment-strategies.md 17 KB
- references/finops-foundations.md 16 KB
- references/kubernetes-cost-optimization.md 14 KB
- references/tagging-for-cost-allocation.md 10 KB
- references/tools-comparison.md 9.4 KB
- scripts/cleanup_idle_resources.py 11 KB runs code
- scripts/ri_coverage_report.py 11 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 491 lines · 51 tokens per session scan A 804ed40cf8b2
optimizing-costs is a skill published in the GitHub repository ancoleman/ai-design-components (525 stars, last pushed 10mo ago), licensed MIT. It adds 51 tokens to every session and 4,846 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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