Tech Stack Evaluation
- 69 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Helps with ai & agent building tasks.
About
tech-stack-evaluation is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- tech-stack-evaluation
- AI & Agent Building
- AI-coding skill
Tech Stack Evaluation by the numbers
- 69 all-time installs (skills.sh)
- Ranked #5,786 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
npx skills add https://github.com/eyadsibai/ltk --skill tech-stack-evaluationAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 69 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Helps with ai & agent building tasks.
Files
<!-- Adapted from: claude-skills/engineering-team/tech-stack-evaluator -->
Tech Stack Evaluation Guide
Framework for making technology decisions.
When to Use
- Choosing between frameworks/libraries
- Evaluating cloud providers
- Making build vs buy decisions
- Planning technology migrations
- Standardizing tech stack
Evaluation Framework
Decision Matrix
| Factor | Weight | Option A | Option B | Option C |
|---|---|---|---|---|
| Team Experience | 20% | Score | Score | Score |
| Community/Support | 15% | Score | Score | Score |
| Performance | 15% | Score | Score | Score |
| Scalability | 15% | Score | Score | Score |
| Cost (TCO) | 15% | Score | Score | Score |
| Integration | 10% | Score | Score | Score |
| Security | 10% | Score | Score | Score |
Scoring Guide
| Score | Meaning |
|---|---|
| 5 | Excellent, best in class |
| 4 | Good, above average |
| 3 | Adequate, meets needs |
| 2 | Below average, concerns |
| 1 | Poor, significant issues |
Common Comparisons
Frontend Frameworks
| Factor | React | Vue | Angular |
|---|---|---|---|
| Learning Curve | Medium | Low | High |
| Ecosystem | Large | Medium | Large |
| Performance | Good | Good | Good |
| Enterprise | Yes | Growing | Yes |
| Job Market | Largest | Growing | Strong |
Backend Languages
| Factor | Node.js | Python | Go | Java |
|---|---|---|---|---|
| Performance | Good | Medium | Excellent | Good |
| Ecosystem | Large | Large | Growing | Large |
| Learning | Easy | Easy | Medium | Hard |
| Concurrency | Event-loop | Async | Native | Threads |
Databases
| Factor | PostgreSQL | MySQL | MongoDB | DynamoDB |
|---|---|---|---|---|
| Type | Relational | Relational | Document | Key-Value |
| Scalability | Vertical | Vertical | Horizontal | Horizontal |
| Schema | Strict | Strict | Flexible | Flexible |
| Cost | Free | Free | Free/Paid | Usage |
Cloud Providers
| Factor | AWS | GCP | Azure |
|---|---|---|---|
| Market Share | Largest | Growing | Second |
| Services | Most complete | Strong ML/Data | Enterprise |
| Pricing | Complex | Simpler | Complex |
| Free Tier | 12 months | Always free | 12 months |
Evaluation Process
Step 1: Define Requirements
## Requirements
- Scale: [Expected users/requests]
- Performance: [Latency requirements]
- Budget: [Monthly/yearly budget]
- Team: [Current skills and size]
- Timeline: [Implementation deadline]Step 2: Identify Options
Research 3-5 viable options:
- Current market leaders
- Rising alternatives
- Team-familiar options
Step 3: Proof of Concept
Build minimal POC for top 2-3 options:
- Core functionality
- Integration test
- Performance benchmark
Step 4: Total Cost Analysis
| Cost Type | Option A | Option B |
|---|---|---|
| Licensing | $ | $ |
| Infrastructure | $ | $ |
| Development | $ | $ |
| Training | $ | $ |
| Maintenance | $ | $ |
| Total (3 yr) | $ | $ |
Step 5: Risk Assessment
| Risk | Option A | Option B |
|---|---|---|
| Vendor lock-in | Low/Med/High | |
| Technology obsolescence | ||
| Skill availability | ||
| Security concerns |
Build vs Buy
Build When
- Core competitive advantage
- Unique requirements
- Long-term cost savings
- Team has expertise
Buy When
- Commodity functionality
- Time to market critical
- Ongoing maintenance burden
- Industry standard exists
Decision Tree
Is it core to your business?
├── Yes → Does expertise exist?
│ ├── Yes → Build
│ └── No → Buy (for now)
└── No → Buy or use open sourceMigration Planning
Migration Phases
1. Assessment - Current state analysis 2. Planning - Target architecture 3. Preparation - Team training, tooling 4. Migration - Incremental rollout 5. Validation - Testing, monitoring 6. Cutover - Final switch 7. Optimization - Post-migration tuning
Risk Mitigation
- Run parallel systems
- Feature flags for rollback
- Comprehensive testing
- Staged rollout
- Clear rollback plan
Documentation Template
# Technology Decision: [Name]
## Context
Why are we making this decision?
## Options Considered
1. [Option A] - Pros/Cons
2. [Option B] - Pros/Cons
3. [Option C] - Pros/Cons
## Decision
We chose [Option] because...
## Consequences
- Positive: [List]
- Negative: [List]
## Implementation Plan
- Phase 1: [Action]
- Phase 2: [Action]Related skills
Setup Matt Pocock SkillsScaffold the per-repo configuration that Matt Pocock’s engineering agent skills rely on so they understand the issue tracker, triage labels, and domain documentation la495k196k
Lark AppsConnect Claude, Cursor or custom agents directly to Lark (Feishu) for messaging, document automation, approval workflows and enterprise data access.402k
CavemanSlash token usage by roughly 75% while keeping every technical detail intact when working with Claude Code, Cursor or similar agents.391k94.7k
Lark Skill MakerQuickly turn any Lark/Feishu OpenAPI call or multi-step workflow into a reusable agent skill with its own SKILL.md.386k16k
Running Claude Code Via Litellm CopilotRun Claude Code at a fraction of the cost by routing requests through LiteLLM to the GitHub Copilot Chat API.270k72
Codex PetGenerate a complete Codex Pet spritesheet and metadata from one reference image without needing an OpenAI key or Codex Pro.246k10AI & Agent Buildingagents