Back to Skills

container-image-optimizer

majiayu000
Updated Yesterday
58
9
58
View on GitHub
Otherai

About

This skill helps developers optimize Docker container images by minimizing layers and using lightweight base images. It provides structured guidance for planning and implementing image optimizations within your existing architecture and constraints. Use it when you need to reduce image size, improve security, and accelerate deployments.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/majiayu000/claude-skill-registry
Git CloneAlternative
git clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/container-image-optimizer

Copy and paste this command in Claude Code to install this skill

Documentation

Container Image Optimizer

Purpose

  • Optimize layers and use minimal base images.

Preconditions

  • Access to system context (repos, infra, environments)
  • Confirmed requirements and constraints
  • Required approvals for security, compliance, or governance

Inputs

  • Problem statement and scope
  • Current architecture or system constraints
  • Non-functional requirements (performance, security, compliance)
  • Target stack and environment

Outputs

  • Design or implementation plan
  • Required artifacts (diagrams, configs, specs, checklists)
  • Validation steps and acceptance criteria

Detailed Step-by-Step Procedures

  1. Clarify scope, constraints, and success metrics.
  2. Review current system state, dependencies, and integration points.
  3. Select patterns, tools, and architecture options that match constraints.
  4. Produce primary artifacts (docs/specs/configs/code stubs).
  5. Validate against requirements and known risks.
  6. Provide rollout and rollback guidance.

Decision Trees and Conditional Logic

  • If compliance or regulatory scope applies -> add required controls and audit steps.
  • If latency budget is strict -> choose low-latency storage and caching.
  • Else -> prefer cost-optimized storage and tiering.
  • If data consistency is critical -> prefer transactional boundaries and strong consistency.
  • Else -> evaluate eventual consistency or async processing.

Error Handling and Edge Cases

  • Partial failures across dependencies -> isolate blast radius and retry with backoff.
  • Data corruption or loss risk -> enable backups and verify restore path.
  • Limited access to systems -> document gaps and request access early.
  • Legacy dependencies with limited change tolerance -> use adapters and phased rollout.

Tool Requirements and Dependencies

  • CLI and SDK tooling for the target stack
  • Credentials or access tokens for required environments
  • Diagramming or spec tooling when producing docs

Stack Profiles

  • Use Profile A, B, or C from skills/STACK_PROFILES.md.
  • Note selected profile in outputs for traceability.

Validation

  • Requirements coverage check
  • Security and compliance review
  • Performance and reliability review
  • Peer or stakeholder sign-off

Rollback Procedures

  • Revert config or deployment to last known good state.
  • Roll back database migrations if applicable.
  • Verify service health, data integrity, and error rates after rollback.

Success Metrics

  • Measurable outcomes (latency, error rate, uptime, cost)
  • Acceptance thresholds defined with stakeholders

Example Workflows and Use Cases

  • Minimal: apply the skill to a small service or single module.
  • Production: apply the skill to a multi-service or multi-tenant system.

GitHub Repository

majiayu000/claude-skill-registry
Path: skills/container-image-optimizer

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill