tech-stack-evaluator
by alirezarezvanitech-stack-evaluator helps architects compare frameworks, databases, cloud providers, and migration options using weighted scoring, TCO analysis, ecosystem health, security checks, and validation workflows.
This skill scores 82/100, making it a solid listing candidate for directory users who want a structured technology-stack evaluation workflow rather than a generic prompt. It provides clear activation cues, concrete scripts, sample inputs, example outputs, and reference workflows for comparisons, TCO, security, migration, and ecosystem analysis, though adoption would be easier with explicit installation/run instructions and clearer caveats about data assumptions.
- Strong triggerability: the frontmatter explicitly covers comparing frameworks, evaluating stacks, calculating TCO, migration paths, security, and ecosystem viability.
- Operationally useful: SKILL.md includes quick-start prompts, input formats, analysis types, and references to runnable scripts such as stack_comparator.py and tco_calculator.py.
- Good progressive disclosure: supporting references provide workflows, metrics, and examples, while assets include sample structured, TCO, and text inputs plus expected output.
- No install command is included in SKILL.md, so users must infer how to run it from the repository path and script examples.
- The excerpts show scoring algorithms and sample outputs, but users should validate the underlying data/assumptions before relying on recommendations for high-stakes architecture decisions.
Overview of tech-stack-evaluator skill
What tech-stack-evaluator does
tech-stack-evaluator is a Software Architecture decision-support skill for comparing frameworks, platforms, databases, cloud providers, and migration options. It turns a vague “Should we use X or Y?” discussion into a weighted evaluation with scoring, TCO analysis, ecosystem health, security considerations, migration effort, and an actionable recommendation.
Best-fit users and decisions
This skill is most useful for engineering leads, architects, CTOs, platform teams, and senior developers preparing a technology recommendation. It fits decisions such as React vs Vue, PostgreSQL vs MongoDB, AWS vs GCP, Next.js hosting choices, Angular.js migration planning, or evaluating whether a newer ecosystem is mature enough for production.
What makes the skill more useful than a generic prompt
The repository includes structured examples, metrics, workflows, and Python helper scripts rather than only prose guidance. Notable files include references/metrics.md for scoring logic, references/workflows.md for decision flows, assets/sample_input_structured.json for comparison inputs, assets/sample_input_tco.json for cost modeling, and scripts such as stack_comparator.py, tco_calculator.py, security_assessor.py, and migration_analyzer.py.
When it is not the right fit
Do not use tech-stack-evaluator as a substitute for live benchmarking, legal review, procurement due diligence, or a formal security audit. It works best as a decision-structuring layer: it helps you expose assumptions, compare options consistently, and identify what to validate next.
How to Use tech-stack-evaluator skill
tech-stack-evaluator install and repository path
Install the skill from the GitHub skill repository with:
npx skills add alirezarezvani/claude-skills --skill tech-stack-evaluator
The source path is engineering-team/skills/tech-stack-evaluator in alirezarezvani/claude-skills. After installing, read SKILL.md first, then open references/workflows.md, references/metrics.md, and references/examples.md. For machine-readable examples, inspect assets/sample_input_structured.json, assets/sample_input_tco.json, and assets/expected_output_comparison.json.
Inputs that produce stronger evaluations
A weak prompt is: “Compare React and Vue.” A stronger prompt gives the skill enough decision context:
Use tech-stack-evaluator to compare React, Vue, and Angular for a B2B SaaS dashboard.
Context: 8 developers, mostly React experience, 9-month delivery target, real-time collaboration, SOC 2 roadmap.
Weights: developer experience 25%, ecosystem 20%, performance 15%, scalability 15%, learning curve 10%, documentation 10%, enterprise readiness 5%.
Include risks, confidence, migration/training cost, and what we should validate before committing.
Useful inputs include application type, expected scale, team size, existing skills, timeline, compliance needs, hosting model, budget limits, operational constraints, and must-have integrations. If weights are missing, the skill can infer defaults, but explicit weights usually produce better recommendations.
Suggested tech-stack-evaluator usage workflow
Start with the business decision, not the technology preference. Define the use case, list candidate technologies, assign weighted criteria, and name hard constraints. Then ask for a comparison matrix, recommendation, confidence level, tradeoffs, and validation plan.
For financial decisions, use the TCO pattern from assets/sample_input_tco.json: team size, timeline, hosting, training hours, migration cost, support cost, maintenance effort, growth rate, downtime cost, and security incident assumptions. For migration decisions, ask for effort, risks, timeline, compatibility issues, team retraining, and rollback strategy.
Using the included scripts and references
The support scripts indicate how the skill expects work to be decomposed: stack_comparator.py for weighted comparison, tco_calculator.py for cost modeling, ecosystem_analyzer.py for adoption and community signals, security_assessor.py for risk review, migration_analyzer.py for transition planning, format_detector.py for input handling, and report_generator.py for output formatting. Even if you do not run the scripts directly, their names reveal the evaluation dimensions you should request in your prompt.
tech-stack-evaluator skill FAQ
Is tech-stack-evaluator for Software Architecture decisions?
Yes. tech-stack-evaluator for Software Architecture is a strong fit when the decision affects maintainability, delivery speed, platform cost, hiring, security posture, migration risk, or long-term ecosystem viability. It is less useful for tiny library choices where a quick prototype is cheaper than a formal evaluation.
How is this different from asking an AI to compare two tools?
A generic prompt often returns a broad pros-and-cons list. The tech-stack-evaluator skill encourages weighted scoring, confidence levels, TCO components, migration analysis, and ecosystem/security checks. That structure makes the output easier to defend in an architecture review or planning meeting.
Can beginners use this skill?
Yes, but beginners should start with references/examples.md and copy the structure of the sample prompts. The main risk for new users is accepting the recommendation without checking assumptions. Treat the output as a decision brief, then verify benchmark claims, pricing, compliance requirements, and team-specific constraints.
What decisions should not rely on it alone?
Do not rely on it alone for vendor contracts, regulated security approval, production performance guarantees, or exact cloud bills. Use it to narrow options and generate a validation checklist, then follow up with proofs of concept, pricing calculators, security scans, and stakeholder review.
How to Improve tech-stack-evaluator skill
Improve tech-stack-evaluator results with better constraints
The biggest quality lever is constraint clarity. Instead of asking for “best backend framework,” specify workload, latency goals, deployment target, data model, expected growth, team experience, hiring market, compliance obligations, and operational ownership. Add “must not” constraints too, such as no vendor lock-in, no self-managed Kubernetes, or no GPL dependencies.
Calibrate weights before trusting the recommendation
Weighted scores are only as good as the priorities behind them. If the first output feels wrong, do not just ask for a different answer. Adjust the weights and explain why. For example, an enterprise platform may need enterprise readiness and supportability weighted higher than developer experience; an early-stage startup may prioritize time-to-market and hiring availability.
Watch for common failure modes
Common failure modes include overvaluing popularity, underestimating migration cost, ignoring team learning curve, treating cloud pricing as static, or giving high confidence when score gaps are small. Ask the skill to show sensitivity analysis: “What changes if performance is 10% more important?” or “Which assumption would reverse the recommendation?”
Iterate from recommendation to validation plan
After the first tech-stack-evaluator output, ask for a validation plan with concrete checks: benchmark tasks, prototype scope, security review items, cost assumptions to verify, hiring implications, integration risks, and exit criteria. The best final artifact is not just “choose PostgreSQL” or “choose React,” but a decision record with assumptions, tradeoffs, confidence, and next actions.