WHAT - Evidence-based technical unit assessment for repositories, platforms, frontend, backend, infrastructure, data, UI/UX, and AI-native structural readiness.
Instalación
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
WHAT - Evidence-based technical unit assessment for repositories, platforms, frontend, backend, infrastructure, data, UI/UX, and AI-native structural readiness.
Technical Unit Assessment (WHAT)
Assess a technical unit: frontend app, backend API, data platform, infrastructure/IaC scope,
mobile app, AI/ML pipeline, or another technical workload.
Run dots-workstation-project-assessment-evidence first.
Use dots-workstation-project-assessment as the router for multi-unit assessments.
Out of scope
Does NOT score indicators without evidence — mark as Not assessed
Does NOT make technical decisions — scores inform, humans decide
Does NOT produce final report without dots-workstation-output-handshake
Unit intake
Ask before scoring:
Unit name and type (frontend / backend / infra / data / mobile / AI)
Repositories, services, infrastructure scopes, or pipelines included
Authoritative systems for code, docs, CI/CD, incidents, observability, security
Workflow
Run dots-workstation-project-assessment-evidence to build evidence map
Select indicator groups matching the unit type (see references/indicator-groups.md)
Score only indicators with evidence; mark rest as Not assessed
Note confidence and missing evidence per score
Apply dots-workstation-output-handshake before final scorecard
Scoring rules
Use the 1–5 scale from references/indicator-groups.md. Do not average unrelated indicators
without explaining weighting. Request validator for subjective scores.
References
references/indicator-groups.md — all indicator groups + scoring scale + AI-native readiness
references/default-template.md — technical unit scorecard template
references/example-frontend-assessment.md — example assessment for a frontend team
