Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Cloud Cost Audit
Analyze multi-cloud spend data to identify waste, rightsizing, reserved instance savings, and generate a prioritized 90-day cost optimization roadmap.
skill openclawclawhub Free
Analyze multi-cloud spend data to identify waste, rightsizing, reserved instance savings, and generate a prioritized 90-day cost optimization roadmap.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- 1kalin
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Cloud Cost Optimization Audit
Analyze cloud infrastructure spend across AWS, Azure, and GCP. Identify waste, rightsizing opportunities, and reserved instance savings.
What This Skill Does
When given cloud spend data (billing exports, cost explorer screenshots, or manual input), this skill: Categorizes spend across 8 cost domains (compute, storage, networking, databases, AI/ML, observability, security, licensing) Identifies waste patterns using 12 common anti-patterns Calculates savings with specific dollar amounts per optimization Prioritizes actions by effort vs. impact (quick wins → strategic moves) Generates executive summary with 90-day roadmap
1. Compute (typically 40-55% of total)
Idle instances: >30% idle = waste. Benchmark: <10% idle capacity Rightsizing: 60% of instances are oversized by 1+ size category Spot/preemptible: Batch workloads not on spot = 60-80% overpay Reserved/savings plans: On-demand for steady-state = 30-50% overpay Container density: <40% CPU utilization on nodes = poor bin-packing
2. Storage (typically 10-20%)
Tiering: Data not accessed in 90 days still on hot storage = 60-80% overpay Snapshot sprawl: Orphaned snapshots older than 30 days Duplicate data: Cross-region replication without business justification Object lifecycle: No lifecycle policies = guaranteed bloat
3. Networking (typically 8-15%)
Cross-AZ traffic: Unnecessary data transfer between zones ($0.01-0.02/GB) NAT gateway abuse: High-throughput through NAT vs. VPC endpoints CDN miss rate: >20% miss rate = CDN config issue Egress optimization: No committed use discounts on egress
4. Databases (typically 10-20%)
Over-provisioned RDS/Cloud SQL: Multi-AZ for dev/staging environments Read replica sprawl: Replicas with <5% query load DynamoDB/Cosmos over-provisioning: Provisioned capacity 3x+ actual usage License waste: Commercial DB when open-source works
5. AI/ML Infrastructure (growing — 5-25%)
GPU idle time: Training instances running 24/7 for 4hr/day workloads Inference over-provisioning: GPU instances for CPU-viable inference Model storage: Old model versions consuming storage API costs: Frontier model API calls without caching layer
6. Observability (typically 3-8%)
Log ingestion bloat: Debug logs in production, duplicate log streams Metric cardinality: High-cardinality custom metrics ($$$) Trace sampling: 100% trace sampling when 10% suffices Retention overkill: 13-month retention for non-compliance data
7. Security (typically 2-5%)
WAF rule bloat: Managed rule groups not actively tuned Key management: KMS keys for non-sensitive data Compliance scanning: Overlapping tools doing same checks
8. Licensing (typically 5-15%)
Shelfware: Paid seats not logged in 60+ days Duplicate tools: Multiple tools solving same problem Enterprise tiers: Enterprise features unused, paying enterprise price
12 Waste Anti-Patterns
#PatternTypical WasteFix Effort1Zombie resources (stopped but attached)5-15% of billLow2Over-provisioned instances15-30% computeMedium3No reserved capacity strategy25-40% computeMedium4Hot storage hoarding40-70% storageLow5Cross-AZ data transfer abuse10-30% networkMedium6Dev/staging mirrors production20-40% of envsLow7Orphaned snapshots/AMIs3-8% storageLow8Log ingestion without sampling30-60% observabilityLow9GPU instances for CPU workloads70-85% computeMedium10No spot/preemptible for batch60-80% batchMedium11Shelfware licenses20-40% licensingLow12No tagging = no accountabilityUnmeasurableHigh
Savings Estimation Framework
For each finding, calculate: Annual Savings = (Current Cost - Optimized Cost) × 12 Implementation Cost = Engineering Hours × Loaded Rate ROI = (Annual Savings - Implementation Cost) / Implementation Cost Payback Period = Implementation Cost / (Annual Savings / 12)
Typical Savings by Company Size
Company SizeMonthly Cloud SpendTypical Waste %Annual SavingsStartup (5-15)$2K-$15K35-50%$8K-$90KGrowth (15-50)$15K-$80K25-40%$45K-$384KMid-market (50-200)$80K-$500K20-35%$192K-$2.1MEnterprise (200+)$500K-$5M+15-25%$900K-$15M+
Output Format
Generate a report with: Executive Summary: Total spend, waste identified, savings potential, top 3 quick wins Domain Breakdown: Spend per domain vs. benchmarks Findings Table: Each finding with current cost, optimized cost, savings, effort, priority 90-Day Roadmap: Week 1-2 quick wins, Week 3-6 medium effort, Week 7-12 strategic Governance Recommendations: Tagging strategy, budget alerts, review cadence
Usage
Provide your cloud billing data in any format: AWS Cost Explorer export / Azure Cost Management / GCP Billing Monthly bill summary Architecture description with approximate sizing Or just describe your stack and team size for estimates The agent will analyze and produce the full optimization report.
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Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md