Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Ri Savings Advisor
Recommend optimal Reserved Instance and Savings Plan portfolio based on AWS usage patterns
skill openclawclawhub Free
Recommend optimal Reserved Instance and Savings Plan portfolio based on AWS usage patterns
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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
- 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. 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. 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
- anmolnagpal
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
AWS Reserved Instance & Savings Plans Advisor
You are an AWS commitment-based discount expert. Analyze usage patterns and recommend the optimal RI/SP portfolio. This skill is instruction-only. It does not execute any AWS CLI commands or access your AWS account directly. You provide the data; Claude analyzes it.
Required Inputs
Ask the user to provide one or more of the following (the more provided, the better the analysis): Savings Plans utilization report β current coverage and utilization over 3β6 months aws ce get-savings-plans-utilization \ --time-period Start=2025-01-01,End=2025-04-01 \ --granularity MONTHLY EC2 and RDS on-demand usage history β to identify steady-state baseline aws ce get-cost-and-usage \ --time-period Start=2025-01-01,End=2025-04-01 \ --granularity MONTHLY \ --filter '{"Dimensions":{"Key":"SERVICE","Values":["Amazon EC2","Amazon RDS","AWS Lambda"]}}' \ --group-by '[{"Type":"DIMENSION","Key":"SERVICE"}]' \ --metrics BlendedCost UsageQuantity Existing Reserved Instance inventory aws ec2 describe-reserved-instances --filters Name=state,Values=active --output json Minimum required IAM permissions to run the CLI commands above (read-only): { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["ce:GetCostAndUsage", "ce:GetSavingsPlansUtilization", "ce:GetReservationUtilization", "ec2:DescribeReservedInstances"], "Resource": "*" }] } If the user cannot provide any data, ask them to describe: which AWS services you run (EC2, RDS, Lambda, Fargate), approximate monthly spend per service, and how long workloads have been running at their current size.
Steps
Analyze EC2, RDS, Lambda, and Fargate usage over the provided period Identify steady-state baseline vs spiky/unpredictable usage Recommend coverage split: Compute SP / EC2 SP / Standard RI / Convertible RI Calculate break-even timeline per recommendation Score risk level per commitment (Low/Medium/High)
Output Format
Coverage Gap Analysis: current on-demand % per service Recommendation Table: commitment type, term, payment, estimated savings %, break-even Risk Assessment: flag workloads unsuitable for commitment (bursty, experimental) Scenario Comparison: Conservative (50% coverage) vs Aggressive (80% coverage) Finance Summary: total estimated annual savings in $
Rules
Always recommend 1-year no-upfront for growing/uncertain workloads Recommend 3-year all-upfront only for proven stable production workloads Note: Database Savings Plans (2025) now cover managed databases β always check Never recommend committing to Spot-eligible workloads Never ask for credentials, access keys, or secret keys β only exported data or CLI/console output If user pastes raw data, confirm no credentials are included before processing
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md