Imported from borghei/claude-skills (engineering/tech-stack-evaluator/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill tech-stack-evaluator. Copyright stays with the author (MIT + Commons Clause).

Technology Stack Evaluator

Evaluate and compare technologies, frameworks, and cloud providers with data-driven, weighted analysis and actionable recommendations.

Core Capabilities

• Technology comparison — weighted multi-criteria scoring of frameworks and libraries across 8 categories.
• TCO analysis — 5-year total cost of ownership including hidden costs (technical debt, vendor lock-in, turnover).
• Ecosystem health — viability scoring from GitHub, npm, community, and corporate-backing metrics.
• Security assessment — vulnerability/patch scoring and compliance readiness for GDPR, SOC2, HIPAA, PCI-DSS.
• Migration analysis — complexity, effort, risk, and recommended approach (direct/phased/strangler).
• Cloud comparison — compare AWS, Azure, GCP for specific workloads.

When to Use

• Comparing frontend/backend frameworks for new projects
• Evaluating cloud providers for specific workloads
• Planning technology migrations with risk assessment
• Calculating build vs. buy decisions with TCO
• Assessing open-source library viability

When NOT to use: trivial decisions between similar tools (use team preference), mandated technology choices (decision already made), emergency production issues (use monitoring tools).

Clarify First

Before the evaluation, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Candidates — the specific frameworks / libraries / cloud providers to compare (the subject of the analysis)
• Analysis type — comparison / TCO / ecosystem health / security / migration (selects which module runs)
• Decision criteria & weights — which of the 8 categories matter most for this context (drives the weighted scoring and recommendation)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

All scripts are Python library modules — import the class and call its methods. See references/tool-reference.md for full parameters and outputs.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/tool-reference.md — full constructor parameters, methods, scoring weights, and output formats for all 7 scripts. Read when wiring up or debugging any tool.
• references/usage-and-criteria.md — example prompts, input formats (text/YAML/JSON), analysis depth tiers, confidence levels, troubleshooting table, and success criteria. Read before invoking the evaluator.
• references/metrics.md — detailed scoring algorithms and calculation formulas.
• references/examples.md — input/output examples for all analysis types.
• references/workflows.md — step-by-step evaluation workflows for common scenarios.

Scope & Limitations

Covers:

• Weighted multi-criteria comparison of frameworks, libraries, and cloud providers
• Multi-year TCO projections including hidden costs (technical debt, vendor lock-in, turnover)
• Ecosystem viability assessment using GitHub, npm, and community metrics
• Security posture scoring and compliance readiness for GDPR, SOC2, HIPAA, PCI-DSS

Does NOT cover:

• Live data fetching from GitHub API, npm registry, or vulnerability databases (all data must be provided as input dictionaries)
• Performance benchmarking or load testing (use engineering/senior-qa for test execution)
• Licensing legal review or contract negotiation (use ra-qm-team compliance skills for regulatory guidance)
• Team hiring or organizational design decisions (use hr-operations/talent-acquisition for staffing analysis)

Integration Points