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Get Started Free →Evaluate and compare technology stacks with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, calculating total cost of ownership, assessing migration paths, or analyzing ecosystem viability.
.claude/skills/borghei-tech-stack-evaluator/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -6% | 0% |
Evaluate and compare technologies, frameworks, and cloud providers with data-driven, weighted analysis and actionable recommendations.
When NOT to use: trivial decisions between similar tools (use team preference), mandated technology choices (decision already made), emergency production issues (use monitoring tools).
Before the evaluation, confirm these inputs. If any is unknown or vague, ASK — do not assume:
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.
All scripts are Python library modules — import the class and call its methods. See references/tool-reference.md for full parameters and outputs.
| Tool | Purpose | Entry point | |------|---------|-------------| | stack_comparator.py | Weighted comparison across 8 categories | from stack_comparator import StackComparator | | tco_calculator.py | Multi-year TCO incl. hidden costs | from tco_calculator import TCOCalculator | | ecosystem_analyzer.py | Ecosystem health & viability scoring | from ecosystem_analyzer import EcosystemAnalyzer | | security_assessor.py | Security posture & compliance readiness | from security_assessor import SecurityAssessor | | migration_analyzer.py | Migration complexity, effort, risks | from migration_analyzer import MigrationAnalyzer | | report_generator.py | Context-aware report rendering | from report_generator import ReportGenerator | | format_detector.py | Detect/parse JSON, YAML, URL, or text input | from format_detector import FormatDetector |
Load the reference that matches the task — keep this file lean and pull detail on demand:
Covers:
Does NOT cover:
engineering/senior-qa for test execution)ra-qm-team compliance skills for regulatory guidance)hr-operations/talent-acquisition for staffing analysis)| Skill | Integration | Data Flow | |-------|-------------|-----------| | engineering/senior-security | Feed security assessor output into deeper vulnerability analysis | SecurityAssessor results → security review input | | engineering/senior-devops | Use TCO hosting projections to inform infrastructure planning | TCOCalculator hosting/scaling data → DevOps capacity models | | engineering/senior-qa | Migration test coverage scores inform QA test planning | MigrationAnalyzer testing_requirements → QA test strategy | | ra-qm-team/compliance-auditor | Compliance readiness gaps feed into formal audit preparation | SecurityAssessor.assess_compliance() missing features → audit checklist | | c-level-advisor/cto-advisor | Executive summaries and TCO reports support CTO decision-making | ReportGenerator executive summary → strategic technology decisions | | product-team/product-manager | Ecosystem viability and migration timelines inform product roadmaps | EcosystemAnalyzer + MigrationAnalyzer → roadmap planning |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 32,561 | 27,845 | -14% | 1 | 1 | 0% | 4,911 | 6,071 | +24% | 0 | 0 | — |
case-02 | fail→fail | 26,018 | 27,958 | +7% | 1 | 1 | 0% | 4,078 | 5,686 | +39% | 0 | 0 | — |
case-03 | fail→fail | 27,969 | 25,399 | -9% | 1 | 1 | 0% | 4,493 | 6,078 | +35% | 0 | 0 | — |
case-04 | fail→pass | 16,246 | 15,181 | -7% | 1 | 1 | 0% | 2,372 | 3,618 | +53% | 0 | 0 | — |
case-05 | fail→fail | 13,702 | 7,443 | -46% | 1 | 1 | 0% | 2,434 | 2,337 | -4% | 0 | 0 | — |
case-06 | pass→pass | 11,620 | 6,761 | -42% | 1 | 1 | 0% | 2,112 | 2,195 | +4% | 0 | 0 | — |
case-07 | fail→pass | 20,798 | 8,343 | -60% | 1 | 1 | 0% | 3,178 | 2,584 | -19% | 0 | 0 | — |
case-08 | fail→fail | 37,932 | 18,637 | -51% | 1 | 1 | 0% | 3,450 | 4,303 | +25% | 0 | 0 | — |
case-09 | pass→pass | 7,563 | 1,247 | -84% | 1 | 1 | 0% | 1,272 | 1,412 | +11% | 0 | 0 | — |
case-10 | fail→pass | 13,191 | 5,459 | -59% | 1 | 1 | 0% | 2,100 | 2,192 | +4% | 0 | 0 | — |
case-11 | fail→pass | 11,519 | 2,631 | -77% | 1 | 1 | 0% | 1,860 | 1,683 | -10% | 0 | 0 | — |
case-12 | fail→pass | 11,093 | 2,550 | -77% | 1 | 1 | 0% | 1,736 | 1,638 | -6% | 0 | 0 | — |
case-13 | fail→pass | 11,946 | 4,350 | -64% | 1 | 1 | 0% | 1,976 | 1,975 | -0% | 0 | 0 | — |
case-14 | fail→pass | 10,775 | 2,799 | -74% | 1 | 1 | 0% | 1,550 | 1,559 | +1% | 0 | 0 | — |
case-15 | fail→fail | 17,958 | 5,375 | -70% | 1 | 1 | 0% | 2,610 | 2,092 | -20% | 0 | 0 | — |
case-16 | fail→fail | 21,625 | 9,571 | -56% | 1 | 1 | 0% | 2,089 | 2,646 | +27% | 0 | 0 | — |
case-17 | fail→fail | 19,489 | 10,459 | -46% | 1 | 1 | 0% | 3,044 | 2,779 | -9% | 0 | 0 | — |
case-18 | pass→pass | 15,324 | 16,212 | +6% | 1 | 1 | 0% | 2,277 | 3,816 | +68% | 0 | 0 | — |
case-19 | pass→pass | 14,623 | 11,674 | -20% | 1 | 1 | 0% | 2,019 | 3,003 | +49% | 0 | 0 | — |
case-20 | pass→pass | 6,084 | 2,831 | -53% | 1 | 1 | 0% | 866 | 1,630 | +88% | 0 | 0 | — |
case-21 | fail→pass | 27,754 | 6,090 | -78% | 1 | 1 | 0% | 4,210 | 2,217 | -47% | 0 | 0 | — |
case-22 | fail→pass | 13,502 | 5,619 | -58% | 1 | 1 | 0% | 2,190 | 1,764 | -19% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +41 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.