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LLMs-Universal-Life-Science-and-Clinical-Skills- Multi_Cloud_Pricing

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install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Software_Engineering/Multi_Cloud_Pricing" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-multi-cloud-pricin && rm -rf "$T"
manifest: Skills/Software_Engineering/Multi_Cloud_Pricing/SKILL.md
source content
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name: multi-cloud-pricing description: Compares pricing across major cloud providers (AWS, Azure, GCP, OCI) for various instance types, aiding in cost optimization. keywords:

  • cloud
  • pricing
  • finops
  • mcp measurable_outcome: Generates a cost comparison report across 3 providers within 2 minutes. license: MIT metadata: author: Software Engineering Tools version: "1.0.0" compatibility:
  • system: Multi-platform allowed-tools:
  • run_shell_command

Multi-Cloud Pricing Analysis

This skill provides comparative pricing metrics for infrastructure components across major cloud providers, enabling cost-effective architecture planning.

When to Use This Skill

  • When planning migration to the cloud.
  • To optimize existing infrastructure costs.
  • To perform "what-if" scenarios for scaling up services.

Core Capabilities

  1. Cross-Cloud Querying: Fetches up-to-date pricing data via provider APIs.
  2. Resource Normalization: Matches equivalent instance types (e.g., vCPUs, RAM) across providers.
  3. Cost Projections: Estimates monthly/yearly spend based on usage patterns.

Example Usage

User: "Compare the cost of a 16 vCPU, 64GB RAM instance on AWS and GCP."

Agent Action:

python3 src/cloud_tools/pricing_comparator.py --vcpu 16 --ram 64 --providers aws,gcp
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