Tech Stack Evaluator
Score and compare technologies (e.g. PostgreSQL vs MongoDB) across weighted criteria before you lock your MVP stack.
Overview
Tech Stack Evaluator is an agent skill most often used in Validate (also Idea research, Build backend) that scores technologies across eight weighted categories and returns a recommendation with confidence and comparison
Install
npx skills add https://github.com/alirezarezvani/claude-skills --skill tech-stack-evaluatorWhat is this skill?
- 8 weighted evaluation dimensions per technology (performance, scalability, DX, ecosystem, learning curve, docs, communit
- Per-tech weighted totals plus ranked strengths and weaknesses
- Decision-factor breakdown with category importance percentages and best performer per category
- Structured comparison_matrix output for side-by-side stack debates
- Emits recommendation string and numeric confidence score for the winner
- 8 evaluation categories per technology
- Weighted decision factors with percentage importance (e.g. 20% performance, 20% scalability)
- Recommendation confidence score emitted in output (example: 52.0)
Adoption & trust: 792 installs on skills.sh; 17.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You are comparing databases or frameworks with opinions and blog posts instead of a consistent weighted scorecard.
Who is it for?
Solo founders narrowing two to four stack options before the first production schema or API skeleton.
Skip if: Teams that already ran benchmarks and signed an architecture decision record—skip re-scoring unless requirements changed materially.
When should I use this skill?
When comparing technologies for an upcoming build or scope decision and you need weighted category scores plus a recommendation.
What do I get? / Deliverables
You get a ranked recommendation, per-category winners, and a comparison matrix you can paste into scope docs—then proceed to prototype or implementation on the chosen stack.
- Per-technology category scores and weighted totals
- Recommendation with confidence and comparison_matrix
- Decision_factors list with best performer per category
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Validate is the canonical shelf because stack choice is a commitment gate—prove the architecture fits scope, cost, and solo maintainer capacity before Build. Scope subphase covers explicit tradeoff matrices and confidence-backed recommendations, not day-one coding.
Where it fits
Survey two database options with the same eight dimensions before you commit to a landing-page promise about scale.
Document why PostgreSQL wins for your MVP with explicit weakness on learning curve for your timeline.
Re-run the matrix when adding a read-heavy feature to see if scalability scores still favor your incumbent database.
How it compares
Structured stack scorecard—not a live benchmark runner or cloud cost estimator.
Common Questions / FAQ
Who is tech-stack-evaluator for?
Indie builders and small teams choosing backends, databases, or platforms who want repeatable weighted comparisons instead of one-off chat opinions.
When should I use tech-stack-evaluator?
During Validate scope when locking MVP architecture, Idea research when surveying options, or Build backend when revisiting a major component swap—with real benchmarks afterward.
Is tech-stack-evaluator safe to install?
It primarily produces analytical JSON and prose; review the Security Audits panel on this page and do not treat scores as audited security or performance certifications.
SKILL.md
READMESKILL.md - Tech Stack Evaluator
{ "technologies": { "PostgreSQL": { "category_scores": { "performance": 85.0, "scalability": 90.0, "developer_experience": 75.0, "ecosystem": 95.0, "learning_curve": 70.0, "documentation": 90.0, "community_support": 95.0, "enterprise_readiness": 95.0 }, "weighted_total": 85.5, "strengths": ["scalability", "ecosystem", "documentation", "community_support", "enterprise_readiness"], "weaknesses": ["learning_curve"] }, "MongoDB": { "category_scores": { "performance": 80.0, "scalability": 95.0, "developer_experience": 85.0, "ecosystem": 85.0, "learning_curve": 80.0, "documentation": 85.0, "community_support": 85.0, "enterprise_readiness": 75.0 }, "weighted_total": 84.5, "strengths": ["scalability", "developer_experience", "learning_curve"], "weaknesses": [] } }, "recommendation": "PostgreSQL", "confidence": 52.0, "decision_factors": [ { "category": "performance", "importance": "20.0%", "best_performer": "PostgreSQL", "score": 85.0 }, { "category": "scalability", "importance": "20.0%", "best_performer": "MongoDB", "score": 95.0 }, { "category": "developer_experience", "importance": "15.0%", "best_performer": "MongoDB", "score": 85.0 } ], "comparison_matrix": [ { "category": "Performance", "weight": "20.0%", "scores": { "PostgreSQL": "85.0", "MongoDB": "80.0" } }, { "category": "Scalability", "weight": "20.0%", "scores": { "PostgreSQL": "90.0", "MongoDB": "95.0" } }, { "category": "WEIGHTED TOTAL", "weight": "100%", "scores": { "PostgreSQL": "85.5", "MongoDB": "84.5" } } ] } { "comparison": { "technologies": [ { "name": "PostgreSQL", "performance": {"score": 85}, "scalability": {"score": 90}, "developer_experience": {"score": 75}, "ecosystem": {"score": 95}, "learning_curve": {"score": 70}, "documentation": {"score": 90}, "community_support": {"score": 95}, "enterprise_readiness": {"score": 95} }, { "name": "MongoDB", "performance": {"score": 80}, "scalability": {"score": 95}, "developer_experience": {"score": 85}, "ecosystem": {"score": 85}, "learning_curve": {"score": 80}, "documentation": {"score": 85}, "community_support": {"score": 85}, "enterprise_readiness": {"score": 75} } ], "use_case": "SaaS application with complex queries", "weights": { "performance": 20, "scalability": 20, "developer_experience": 15, "ecosystem": 15, "learning_curve": 10, "documentation": 10, "community_support": 5, "enterprise_readiness": 5 } } } { "tco_analysis": { "technology": "AWS", "team_size": 10, "timeline_years": 5, "initial_costs": { "licensing": 0, "training_hours_per_dev": 40, "developer_hourly_rate": 100, "training_materials": 1000, "migration": 50000, "setup": 10000, "tooling": 5000 }, "operational_costs": { "annual_licensing": 0, "monthly_hosting": 5000, "annual_support": 20000, "maintenance_hours_per_dev_monthly": 20 }, "scaling_params": { "initial_users": 5000, "annual_growth_rate": 0.30, "initial_servers": 10, "cost_per_server_monthly": 300 }, "productivity_factors": { "productivity_multiplier": 1.2, "time_to_market_reduction_days": 15, "avg_feature_time_days": 45, "avg_feature_value": 15000, "technical_debt_percentage": 0.12, "vendor_lock_in_risk": "medium", "security_incide