qlik-assessment

name	qlik-assessment
description	Take inventory of a Qlik Cloud tenant and produce a migration-readiness readout — app/space/user counts, per-app complexity (master-measure expression convertibility, chart-type coverage, Set Analysis / Section Access flags, data model size), reload health, and a value/cost-ranked migration shortlist. Use to scope a Qlik→Sigma migration or audit BI sprawl. qlik-cli driven; hands off to qlik-to-sigma.
user-invocable	true

Qlik Assessment

STATUS: working inventory (qlik-cli driven). Mirrors tableau-assessment / powerbi-assessment / domo-assessment: same value / (1 + cost) scoring and migrate-first / easy-win / moderate / needs-gap-scout / retire tags.

Read first:

• refs/complexity-scoring.md — the Qlik convertibility rubric (expression + viz buckets)
• ../qlik-to-sigma/refs/connection.md — qlik-cli auth + the two M2M gotchas
• PRIVACY.md — read-only posture
• refs/output-shapes.md — exact inventory.json shape the renderers consume
• refs/readout-template.md — readout.html section ordering + Sigma-branded theme

The key idea

The same convert_qlik_to_sigma translation rules that drive the converter also predict migration effort: bucket each master-measure expression (auto / manual / unhandled) and each chart's viz type against Sigma's coverage. Set Analysis → manual (Sigma SumIf); Aggr()/Dual()/selection-state/Range* → unhandled.

Phases

0. Probe — qlik context use; qlik item ls --resourceType app; confirm spaces/users visibility (M2M only sees granted spaces — see connection.md). 1–2. Inventory — apps (+ itemViews, resourceReloadStatus, lastReloadTime, hasSectionAccess, isDirectQueryMode, resourceSize), spaces, users. scripts/qlik-inventory.py.
1. Per-app complexity — master measures (Engine MeasureList) bucketed by expression; chart objects bucketed by viz type; Section Access & DirectQuery flags; data-model table count from the load script.
2. Shortlist — cost = 10·unhandled + 3·manual + 1·hint; value = itemViews × √(distinct, when available) else size/complexity proxy; score = value/(1+cost); tag.
3. Readout — qlik-inventory.py writes inventory.json + readout.md; then scripts/render-readout-html.rb --out <dir> renders the customer-facing, Sigma-branded readout.html (same theme as tableau-assessment).
4. Hand off — to qlik-to-sigma (ask the user which apps first).

Scripts

Script	Phase	Purpose
scripts/qlik-inventory.py	0–4	Enumerate apps/spaces/users, per-app expression+viz complexity, score + tag → inventory.json + readout.md
scripts/render-readout-html.rb	5	Render the Sigma-branded readout.html from inventory.json (ruby scripts/render-readout-html.rb --out <dir>)

qlik-inventory.py --out assessment-<tenant> (uses the active qlik-cli context). Per-app deep complexity reuses the MeasureList enumeration from qlik-to-sigma/scripts/qlik-discover.py.

Open work

• Usage granularity: itemViews is app-level; per-sheet usage needs the tenant audit/usage API.
• Cross-tenant roll-up via the DomoStats-equivalent (Qlik "App analyzer" governance apps).