---
name: cloud-readiness-scorer
description: "Scores a single application or business unit against a five-dimension cloud readiness rubric — technical debt, data sensitivity, integration complexity, team skills, and regulatory constraints — producing a weighted 0–100 readiness score, dimension-level breakdown, migration disposition recommendation (rehost/replatform/refactor/retain/retire), and the top blockers to address before migration. Use when assessing whether an application or business unit is ready to move to the cloud, prioritizing a migration backlog, or building a portfolio-wide readiness baseline."
argument-hint: "[app-or-bu] [target-cloud]"
allowed-tools: ["Read"]
---

# Cloud Readiness Scorer

## Purpose
Score an individual application or business unit against a consistent five-dimension cloud readiness rubric and return a weighted readiness score with dimension-level rationale, a recommended migration disposition (the 6 Rs), and a ranked list of blockers — so migration sequencing decisions are defensible, repeatable across a portfolio, and not driven by gut feel or the loudest stakeholder.

## When to Use
Invoke this skill when the user asks to:
- Assess whether a specific application is ready to migrate to the cloud
- Score or rank multiple applications to sequence a migration backlog
- Build a consistent readiness baseline across a portfolio or business unit
- Decide between rehost, replatform, refactor, retain, or retire for a workload
- Justify a migration go/no-go decision to a steering committee or executive sponsor
- Identify the specific blockers that must be resolved before a workload can move

## Inputs Required
Before starting, collect:
- **Workload identity**: Application or business-unit name and what it does in one sentence
- **Target cloud**: AWS / Azure / GCP / multi-cloud / undecided
- **Technical profile**: Tech stack, age, architecture (monolith/microservices), known technical debt, current hosting
- **Data profile**: Data types handled, sensitivity classification (public/internal/confidential/regulated), data volume and residency requirements
- **Integration map**: Upstream/downstream dependencies, integration patterns (API/file/DB-level/message queue), number of connected systems
- **Team capability**: Cloud experience of the owning team, existing platform/DevOps maturity, available migration support
- **Regulatory context**: Applicable frameworks (HIPAA, PCI-DSS, GDPR, SOX, FedRAMP, etc.) and any contractual or sovereignty constraints

If sensitivity classification or regulatory context is missing, score conservatively (assume higher sensitivity) and flag the assumption explicitly.

## Instructions

### Step 1: Score Each of the Five Dimensions (0–100)
Score each dimension independently using the anchored rubric below. For every dimension, assign a score band, then a precise number within that band, and write 2–3 sentences of rationale citing the specific input evidence.

**Dimension 1 — Technical Debt & Architecture (weight 25%)**
Measures how cloud-friendly the current architecture is.
- **80–100**: Modern stack, containerized or stateless, externalized config, CI/CD in place
- **60–79**: Reasonably modern, some refactoring needed, manageable coupling
- **40–59**: Monolithic but documented, moderate tech debt, mutable state
- **20–39**: Legacy stack, tight coupling, hardcoded infra dependencies, sparse documentation
- **0–19**: Unsupported runtime/OS, no source ownership, undocumented black box

**Dimension 2 — Data Sensitivity & Residency (weight 20%)**
Measures how much friction data classification adds to a cloud move. Higher sensitivity lowers the score.
- **80–100**: Public/internal data only, no residency constraints
- **60–79**: Confidential data, standard encryption sufficient, no residency lock
- **40–59**: Regulated data with clear controls available in target cloud
- **20–39**: Regulated data with residency or sovereignty requirements
- **0–19**: Sovereignty-locked or contractually prohibited from leaving current environment

**Dimension 3 — Integration Complexity (weight 20%)**
Measures dependency drag. More tightly coupled, undocumented integrations lower the score.
- **80–100**: Loosely coupled, ≤3 API-based integrations, well documented
- **60–79**: 4–8 integrations, mostly API/message-based, mappable
- **40–59**: 9–15 integrations or some DB-level coupling
- **20–39**: 15+ integrations, DB-level or file-based coupling, shared databases
- **0–19**: Deeply entangled shared-state dependencies with no clear contract

**Dimension 4 — Team Skills & Operating Maturity (weight 20%)**
Measures whether the owning team can run the workload in the cloud.
- **80–100**: Team has shipped cloud workloads, mature DevOps/IaC, on-call discipline
- **60–79**: Some cloud experience, partial automation, learning curve manageable
- **40–59**: Limited cloud exposure, manual ops, will need enablement
- **20–39**: No cloud experience, no automation, dependent on external help
- **0–19**: No owning team or no operational capacity at all

**Dimension 5 — Regulatory & Compliance Constraints (weight 15%)**
Measures compliance friction beyond data residency. More frameworks and stricter controls lower the score.
- **80–100**: No applicable regulatory frameworks
- **60–79**: One framework, controls natively supported in target cloud
- **40–59**: Multiple frameworks, achievable with documented compensating controls
- **20–39**: Strict frameworks (FedRAMP High, PCI Level 1) requiring significant attestation work
- **0–19**: Conflicting or unmet regulatory requirements with no current path to compliance

### Step 2: Calculate the Weighted Readiness Score
Compute the overall score:
`Score = (D1×0.25) + (D2×0.20) + (D3×0.20) + (D4×0.20) + (D5×0.15)`

Map the result to a readiness tier:
- **80–100 — Ready**: Migrate in the near-term wave
- **60–79 — Conditionally Ready**: Migrate after addressing specific blockers
- **40–59 — Not Yet**: Requires remediation investment before scheduling
- **0–39 — High Risk / Defer**: Do not migrate without restructuring, or retain/retire

### Step 3: Recommend a Migration Disposition (6 Rs)
Based on the dimension pattern, recommend one primary disposition with one-line rationale:
- **Rehost** (lift-and-shift): High team/data scores but minimal modernization appetite
- **Replatform**: Solid architecture score, modest debt, worth small optimizations
- **Refactor**: Low technical debt score but high business value — invest to modernize
- **Repurchase** (SaaS swap): Commodity capability, low integration lock-in
- **Retain**: Low overall score with low business urgency — leave on-prem for now
- **Retire**: Low score and low/declining business value — decommission instead

### Step 4: Rank the Blockers
List the top 3–5 blockers in priority order. For each: the blocker, which dimension it sits in, the remediation action, and a rough effort estimate (Low / Medium / High). Rank by what would most raise the score per unit of effort.

### Step 5: State the Verdict
Give a one-paragraph go/no-go verdict with the score, tier, disposition, and the single most important thing to resolve first. Be decisive — do not hedge.

## Output Format
- **Structure**: Dimension scorecard (table) → weighted score and tier → disposition recommendation → ranked blockers (table) → verdict paragraph
- **Length**: 700–1,000 words
- **Format**: Two tables (dimension scores, blockers) plus structured prose for disposition and verdict
- **Sections**: Dimension Scorecard → Weighted Readiness Score → Migration Disposition → Top Blockers → Verdict
- **Tone**: Enterprise architect to steering committee — analytical, evidence-anchored, decisive; quantified rather than qualitative

## Quality Checklist
Before delivering output, verify:
- [ ] All five dimensions are scored 0–100 with a precise number, not just a band
- [ ] Every dimension score has rationale citing the specific input provided
- [ ] The weighted score is arithmetically correct against the stated weights
- [ ] The score maps to the correct readiness tier
- [ ] A single primary disposition (one of the 6 Rs) is recommended with rationale
- [ ] Blockers are ranked by score-impact-per-effort, each with a remediation action and effort estimate
- [ ] Any missing input (especially data/regulatory) is flagged with the conservative assumption used

## Notes
- Scores are only comparable across a portfolio when the same rubric and weights are applied to every workload — keep the weights fixed unless the program explicitly recalibrates them.
- The data sensitivity and regulatory dimensions are inverted: more sensitive/regulated workloads score lower because they add migration friction, not because they are less valuable.
- A high-value workload with a low score is a refactor candidate, not a retire candidate — separate the readiness score from the business-value judgment when advising sequencing.
- When scoring a business unit rather than a single app, score the constraining (lowest-readiness) critical application unless the unit is being assessed for an all-or-nothing move.

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