WHAT - Evidence-based technical unit assessment for repositories, platforms, frontend, backend, infrastructure, data, UI/UX, and AI-native structural readiness.
Installation
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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
