| name | qlik-ai-readiness-optimizer |
| description | Analyzes a Qlik app via MCP and optimizes it for AI use (Qlik Answers and Qlik MCP) using a 6-Layer Model. Use this skill whenever the user wants to: assess or improve how well a Qlik app works with Qlik Answers or MCP, check AI readiness, fix field naming or grouping issues for AI, improve Master Item descriptions, reduce field noise, audit date fields for time-based questions, create synonym mappings, or prepare an app for AI consumption. Also trigger when: user says "AI-ready", "optimize for Answers/MCP", "Qlik AI Health Check", "AI Readiness", "prepare app for AI", "Answers isn't working", "Answers gives wrong results", "Master Items not showing", "fields not visible to AI", or mentions ungrouped items, noisy fields, or missing descriptions. Always use this skill even if the user only mentions one layer, one issue, or one field — the full assessment provides context for targeted fixes. |
| license | Apache-2.0 |
| metadata | {"author":"JoshQlikDesign","version":"1.1.0","tags":["qlik","ai-readiness","qlik-answers","mcp"]} |
| allowed-tools | read |
Qlik AI Readiness Optimizer
Systematically analyze and optimize a Qlik Sense app for AI readiness — specifically for Qlik Answers and Qlik MCP consumption — based on Turan Pinar's 6-Layer Model (adapted).
Why this exists
The Qlik Engine world is deterministic and technical. The AI world is semantic and probabilistic. Bridging them requires careful preparation across 6 layers. Without this preparation, Qlik Answers will deliver suboptimal results.
The 6-Layer Model
| # | Layer | Priority | What it affects |
|---|
| 1 | Field Naming | 🔴 Critical | AI schema understanding |
| 2 | Field Visibility | 🔴 Critical | AI noise reduction |
| 3 | Field Groups (Logical Model) | 🔴 Critical | Qlik Answers Master Item detection |
| 4 | Master Items + Descriptions | 🔴 Critical | AI metric precision and accuracy |
| 5 | Date Fields + AutoCalendar | 🟠 High | Time-based question accuracy |
| 6 | Synonyms / Vocabulary | 🟡 Medium | Business jargon, multilingual support |
Note: Knowledge Base is not part of the readiness score. A Knowledge Base is a property of an Assistant (which combines one structured app with unstructured document sources), not of a single-app Qlik Answers session — if the user's context is "Qlik Answers on this one app" rather than a multi-app Assistant, Knowledge Base configuration doesn't apply and can be skipped without qualification.
Workflow Overview
This skill follows a Human-in-the-Loop approach:
0. PREFLIGHT → Verify MCP connectivity + data model sanity check
1. CONNECT → Identify the target Qlik app
2. ANALYZE → Assess all 6 layers → produce IST-Zustand report
3. PRESENT → Show findings + AI Readiness Score to user
4. CONFIRM → User reviews and approves the optimization roadmap
5. IMPLEMENT → Execute approved changes layer by layer via MCP
6. VERIFY → Confirm changes, report what's done / what needs manual steps
Never skip the PRESENT → CONFIRM step. Always show the analysis and get explicit user approval before making changes.
Phase 0: PREFLIGHT — MCP + Data Model Sanity Check
Step 1: MCP Connectivity Check
Attempt a lightweight MCP call (e.g., qlik_search or qlik_describe_app) to confirm Qlik MCP tools are connected and responding.
If MCP tools are NOT available:
- Tell the user: "This skill requires Qlik MCP tools to be connected. I can see they're not available in this session. To use this optimizer, connect the Qlik MCP Server first — then come back and we'll run the assessment."
- Do NOT proceed with the workflow. Do NOT attempt to fake an analysis without live data.
- Offer to explain the 6-Layer Model conceptually or provide a manual checklist.
If MCP tools ARE available: proceed to Step 2.
Step 2: Data Model Sanity Check
Use qlik_describe_app and qlik_get_fields to check for:
- Synthetic keys (
$Syn 1, $Syn 2, etc.) — table join problems
- Circular references or link tables suggesting unresolved many-to-many relationships
- Extreme field counts (>200 visible fields) — structural, flag here before a full Layer 2 pass; see references/layer-analysis-guide.md Layer 2 for the full threshold ladder (≤60 ideal, 61–200 noisy, >200 structural)
- Zero tables or zero fields — app may be empty or misconfigured
If critical structural issues are found: present as a blocking warning, explain they undermine AI optimization, and let the user decide whether to proceed.
If the user is resuming a previous optimization session: re-run a quick check (qlik_list_measures/qlik_list_dimensions) before proposing new Master Items, to avoid duplicating items already created in an earlier session.
Phase 1: CONNECT
Ask the user which app to analyze if not already specified.
Use qlik_search (query: name of the app) and qlik_describe_app (app_id).
Collect: App ID, App Name, number of tables, fields, and sheets via qlik_describe_app. No Qlik MCP tool currently reports whether Qlik Answers is enabled for an app — ask the user directly, or note it as unknown and proceed with the technical assessment regardless (the 6-layer model applies whether or not Answers is currently turned on).
Phase 2: ANALYZE — IST-Zustand (Current-State) Assessment
Run a comprehensive analysis across all 6 layers. Batch tool calls where possible.
For the detailed per-layer checklists, severity classifications, scoring rubrics, and example output tables, see references/layer-analysis-guide.md.
Key tools per layer:
- Layers 1, 2, 5:
qlik_get_fields
- Layer 3:
qlik_list_dimensions, qlik_get_fields
- Layer 4:
qlik_list_measures, qlik_list_dimensions
- Layer 6:
qlik_list_dimensions, qlik_list_measures (check for synonyms/tags)
Bonus check (non-scored, advisory): Sheet structure — check qlik_describe_app for sheet count and naming quality.
Phase 3: PRESENT — AI Readiness Report
Generate a structured report with:
- AI Readiness Score (0–100%)
- Layer-by-layer findings
- Prioritized Optimization Roadmap
- What MCP can automate vs. what needs manual steps
Scoring weights
| Layer | Weight |
|---|
| 1 — Field Naming | 15% |
| 2 — Visibility | 15% |
| 3 — Groups | 27% |
| 4 — Master Items | 27% |
| 5 — Dates | 9% |
| 6 — Synonyms | 7% |
Readiness thresholds
| Score | Rating | Interpretation |
|---|
| 0–30% | 🔴 Not Ready | Poor or misleading AI results. Do not enable Qlik Answers until layers 3, 4 are addressed. |
| 31–50% | 🟠 Partially Ready | Misses key metrics or picks wrong fields. Fix ungrouped Master Items and descriptions first. |
| 51–75% | 🟡 Functional | Works for common questions but has gaps. Good enough to start testing. |
| 76–90% | 🟢 AI-Ready | Well-prepared. Remaining improvements are polish. |
| 91–100% | ✅ Optimized | Best-in-class. All layers addressed, descriptions rich, noise minimal. |
Present the roadmap as a prioritized table with columns: Priority, Layer, Action, MCP Automatable?, Effort.
Phase 4: CONFIRM — Human-in-the-Loop
Always pause here. Present the roadmap and confirm with the user before proceeding. Match the user's language (detect from their messages).
Offer options:
- "Optimize all layers" (still confirm each layer before executing)
- "Only specific layers" (user picks)
- "Start with Layer 3 and 4" (highest impact — recommended default)
Phase 5: IMPLEMENT — Layer-by-Layer Optimization
For each approved layer, execute via MCP and confirm with user before moving to next.
For detailed per-layer implementation steps, output templates, and copy-paste script snippets, see references/implementation-guide.md.
For the MCP capability matrix and reload requirements, see references/mcp-capability-matrix.md.
Summary of what MCP can vs. cannot do: create, edit, and delete Master Measures/Dimensions live immediately; load script updates are in preview; variable definitions and synonym import are not available at all. See references/mcp-capability-matrix.md for the full tool-by-tool table.
Phase 6: VERIFY
After completing each layer:
- Confirm what was changed. If any approved change failed to apply (rate limit, validation error, stale app state), explicitly list which ones succeeded vs. failed before asking to continue — don't let a partial failure silently roll into the next layer's confirmation.
- Note what required manual steps (and provide exact instructions)
- Update the AI Readiness Score
- Remind user about reload requirements for load script changes
- If the change touched the Logical Model (grouping, or a fix to an "invalid logical model" indexing error), tell the user Qlik Answers can take up to 24 hours to reindex — this is separate from the app-engine "live immediately" behavior, and testing in Answers right away may still show stale results even though the fix applied correctly
- Ask if user wants to continue to next layer
See references/mcp-capability-matrix.md for reload requirement details.
Key Rules
- Ungrouped Master Items are invisible to Qlik Answers — no exceptions
- "Logic Disabled" in Logical Model = ALL Master Items invisible — check this first
- Dates loaded as strings will not work for time-based questions
- Don't give the AI more fields than necessary — less noise = fewer errors
- Descriptions are primary — the AI uses them to decide which metric to use
- High variable density in expressions reduces AI resolution confidence — enrich descriptions or inline where feasible
- This skill never deletes existing Master Items — its job is to assess and improve AI readiness, not to manage master-item lifecycle, so the IMPLEMENT phase only creates new items and edits/groups existing ones. Deletion is a real, sometimes-correct action (e.g. a true duplicate created in error), but it requires its own usage-verification and confirmation workflow — treat any delete request as out of scope for this skill and hand it off to whatever governs master-item lifecycle in your environment, rather than calling
qlik_delete_measure/qlik_delete_dimension here.
- Always get user confirmation before making changes
- MCP cannot retrieve variable definitions — ask the user for resolved values if inlining
- Load script changes require an app reload — Master Item changes via MCP are live immediately
- Only structured, named Master Items are indexed by Qlik Answers — business glossary definitions, label expressions on Master Items, and content inside Packages, Hierarchies, Behaviors, Calendar Periods, Custom analysis, and Example questions are not indexed. On tenants enabled for the agentic Qlik Answers experience, those unindexed feature types are removed entirely, not just skipped — treat logic living in any of them as something that needs to be re-expressed as a real field or Master Item, not left in place.
- If two users get different answers to the same question, check section access first — Qlik Answers automatically inherits and enforces existing section access / row-level security with no extra configuration, so differing permissions are a common, expected cause of differing answers. Don't assume a data-model defect until access parity is ruled out.
Communication Style
- Speak in the user's language — detect from their messages and match it
- Use clear Layer numbers when referencing the model
- Be specific about what MCP can vs. cannot do
- Offer copy-paste ready code/scripts for manual steps
- Celebrate wins (adapt tone/language to the user)
