/migrate challenge — Skeptical Review Half-Step

Run an autonomous quality review on a completed workflow step. Three coordinated agent personas (Orchestrator, Challenger, Researcher) loop through structured review rounds until a confidence score algorithm determines the data meets threshold.

Usage

/migrate challenge [step]

Where [step] is one of: discovery, analysis, estimate, refine. If omitted, auto-detect the most recently completed step from the assessment status.

Instructions

1. Load State

1. Call get_assessment with project_path set to the current working directory. Fall back to .migration/assessment.json. If neither exists, tell the user to run /migrate new first.
2. Determine the target step:
   • If the user specified a step, use it.
   • Otherwise, auto-detect from assessment status: discovery → challenge discovery, analysis → challenge analysis, estimation → challenge estimate, refinement → challenge refine.
3. Load all data relevant to the target step:
   • Discovery: Call get_discovery (all dimensions). Read skills/migrate-knowledge/discovery/discovery-tree.json for the full question set.
   • Analysis: Call get_analysis. Read skills/migrate-knowledge/heuristics/gotcha-patterns.json, complexity-multipliers.json, dependency-chains.json.
   • Estimate: Call get_estimate. Read skills/migrate-knowledge/heuristics/base-effort-hours.json, ai-alternatives.json.
   • Refine: Call get_estimate. Load scope exclusions via the refine page API or .migration/scope-exclusions.json.
4. Call get_challenge_reviews with the assessment ID and step to load any prior challenge review rounds.
5. Read relevant knowledge files from skills/migrate-knowledge/knowledge/ for cross-referencing.

2. Orchestrator: Compute Baseline Score

Adopt the Orchestrator persona. You are the control loop — systematic, precise, data-driven.

Calculate the initial review confidence score using a weighted average of five dimensions:

review_confidence = Σ(dimension_weight × dimension_score) / Σ(dimension_weight)

Dimension weights (all steps):

Dimension	Weight
Completeness	25
Consistency	25
Plausibility	20
Currency	15
Risk Coverage	15

Scoring per step:

Post-Discovery

Dimension	Measurement
Completeness	(answered_questions / total_required_questions) across all 17 dimensions × 100
Consistency	Cross-dimension references align: topology matches component counts, DB count matches connection strings, instance counts match across dimensions
Plausibility	Values within reasonable ranges: instance counts 1-50, DB sizes 1GB-10TB, cache sizes 256MB-128GB
Currency	Azure target services listed in discovery are still available/recommended (verify via web search)
Risk Coverage	Known risk patterns for this topology type have sufficient data to evaluate

Post-Analysis

Dimension	Measurement
Completeness	Every in-scope complex dimension has >= 1 risk identified
Consistency	Assumptions don't contradict each other or contradict discovery answers
Plausibility	Risk severity ratings proportional to estimated hours impact (critical >= 16h, high >= 8h, medium >= 4h)
Currency	Mitigation strategies reference current Azure capabilities (verify via web search)
Risk Coverage	All gotcha patterns evaluated, all multiplier conditions checked, risk clusters identified for known combinations

Post-Estimate

Dimension	Measurement
Completeness	All in-scope components have hours assigned, role breakdowns present, AI alternatives evaluated
Consistency	Phase ordering respects dependency chains, role hours sum to phase totals, component hours sum to totals
Plausibility	Total hours within expected range for topology type, no single component > 30% of total, optimistic/pessimistic ratio between 1.5x and 3x
Currency	AI tool savings reflect current tool capabilities (verify via web search for latest versions)
Risk Coverage	Pessimistic range covers all identified risks, gotcha patterns add appropriate buffer, assumption widening present for unvalidated assumptions

Post-Refine

Dimension	Measurement
Completeness	Cascade impacts fully propagated for all exclusions, dependent components flagged
Consistency	No in-scope component depends on an excluded component without acknowledgment
Plausibility	Less than 40% of total scope excluded (otherwise the scope may be too narrow), remaining scope is coherent
Currency	Excluded items are not critical for current Azure deployment requirements (verify via web search)
Risk Coverage	Out-of-scope risks and assumptions are properly flagged, excluded component risks noted

Present the baseline score:

CHALLENGE REVIEW: Post-{Step}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Baseline Confidence Score: {score}%

  Completeness     {score}%  ████████░░
  Consistency      {score}%  ██████████
  Plausibility     {score}%  ███████░░░
  Currency         {score}%  ██░░░░░░░░  (needs web verification)
  Risk Coverage    {score}%  █████████░

Focus areas: {lowest 2 dimensions}

3. Challenger Pass

Switch to the Challenger persona. You are skeptical, curious, and thorough. Your job is to find what's missing, inconsistent, or implausible. You push back on every assumption and look for gaps others would miss.

Generate 5-15 structured challenges targeting the lowest-scoring dimensions. Each challenge must include:

{
  id: "CHK-001",           // Sequential within this round
  category: string,        // completeness | consistency | plausibility | currency | risk_coverage
  severity: string,        // critical | high | medium | low
  description: string,     // What's wrong or suspicious
  data_reference: string,  // Specific data point or file referenced
  status: "open",
  resolution: null,
  researcher_needed: boolean,  // true if web search could help resolve
  score_impact: number     // Estimated confidence score improvement if resolved (1-10)
}

Challenge generation rules:

• At least 1 challenge per dimension scoring below 80
• At least 2 challenges tagged researcher_needed: true for currency verification
• Critical severity: Could invalidate the entire step's output
• High severity: Missing data that significantly affects accuracy
• Medium severity: Inconsistency that should be addressed
• Low severity: Minor gap or suggestion for improvement

Present challenges grouped by severity:

CHALLENGER FINDINGS — Round {N}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🔴 CRITICAL ({count})
  CHK-001 [completeness] No database sizing data — cannot validate migration approach
  CHK-002 [currency] Azure SQL Managed Instance pricing model changed in 2024

🟠 HIGH ({count})
  CHK-003 [consistency] Discovery says 4 CD instances but analysis only models 2
  ...

🟡 MEDIUM ({count})
  ...

⚪ LOW ({count})
  ...

{count} challenges need researcher investigation.

4. Researcher Pass

Switch to the Researcher persona. You are methodical and evidence-driven. You investigate challenges using live web search and cross-reference against local knowledge files.

For each challenge tagged researcher_needed: true:

1. Use WebSearch to find current information:

   • Azure service changes: pricing, SKU availability, region support, deprecation notices
   • Sitecore compatibility: supported Azure services, known issues, version constraints
   • Migration tooling: current Terraform, Azure Migrate, DMS versions and capabilities
   • Best practices: latest Microsoft and Sitecore migration guidance

2. Cross-reference findings against local knowledge files in skills/migrate-knowledge/knowledge/

3. Produce a finding for each investigated challenge:

{
  challenge_id: "CHK-002",
  finding: string,           // What was discovered
  source: string,            // Knowledge file path OR "web search"
  source_url: string | null, // URL if from web search
  verified_date: string,     // Today's ISO date
  recommendation: string,    // What to do with this information
  data_update_suggested: boolean  // Should knowledge files be updated?
}

For non-researcher challenges, attempt to resolve from existing data:

• Check if the data exists elsewhere in the assessment
• Cross-reference between discovery dimensions
• Apply domain knowledge to resolve plausibility concerns

Present findings:

RESEARCHER FINDINGS — Round {N}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

CHK-002: Azure SQL MI Pricing Update
  Finding: Azure SQL MI pricing was restructured in November 2024. The
  vCore-based purchasing model now includes a "Next-gen" tier.
  Source: https://learn.microsoft.com/en-us/azure/azure-sql/...
  Verified: {today}
  Recommendation: Update estimate to reflect current pricing tier options.
  ⚠️ Suggests updating knowledge file: azure-sitecore-requirements.md

CHK-005: Sitecore 10.3 Azure Compatibility
  Finding: Sitecore 10.3 officially supports Azure SQL MI with specific
  configuration requirements documented in KB1001489.
  Source: https://support.sitecore.com/...
  Verified: {today}
  Recommendation: No changes needed — current data is accurate.

5. Orchestrator: Recompute Score

Switch back to the Orchestrator persona.

1. Update dimension scores based on challenge resolutions and researcher findings:

   • Resolved challenges: improve the relevant dimension score
   • Findings confirming current data: improve currency score
   • Findings suggesting updates: note but don't penalize (these are improvements)

2. Resolve challenges where possible:

   • If a finding directly addresses a challenge, mark it resolved with the finding as resolution
   • If the data was found elsewhere in the assessment, mark it resolved
   • Challenges that need user input remain open

3. Recompute the confidence score with updated dimension values

4. Check convergence:

   Pass conditions (stop looping):

   • Score >= 80 → Status: passed
   • Score >= 65 AND no critical/high challenges remain open → Status: conditional_pass

   Continue condition:

   • Score improved by >= 5 points since last round AND threshold not met → loop back to Step 3

   Plateau condition (engage user):

   • Score < 80 AND score improved by < 5 points → go to Step 6

Present updated score:

SCORE UPDATE — Round {N}
━━━━━━━━━━━━━━━━━━━━━━━

Previous: {prev_score}% → Current: {new_score}% (+{delta})

  Completeness     {score}% (+{delta})
  Consistency      {score}% (+{delta})
  Plausibility     {score}% (+{delta})
  Currency         {score}% (+{delta})  ← improved by web verification
  Risk Coverage    {score}% (+{delta})

Challenges: {resolved}/{total} resolved
Open critical/high: {count}

Decision: {PASS | CONDITIONAL_PASS | CONTINUE | PLATEAU}

6. Save Round Results

After each round (regardless of outcome), save the results:

Step 1 — Save to MCP: Call save_challenge_review with:

• assessment_id, step, round (auto-increments)
• status: current round status
• confidence_score: computed score
• score_breakdown: per-dimension scores
• acceptance_criteria_met: boolean map of criteria
• challenges: full challenge array with updated statuses
• findings: all researcher findings with source URLs
• summary: narrative summary of this round
• completed_at: ISO timestamp if round is final

Step 2 — Write JSON snapshot: Write .migration/challenge-reviews/{step}-round-{N}.json:

{
  "step": "discovery",
  "round": 1,
  "status": "failed",
  "confidence_score": 62,
  "score_breakdown": {
    "completeness": 85,
    "consistency": 72,
    "plausibility": 68,
    "currency": 30,
    "risk_coverage": 55
  },
  "challenges": [],
  "findings": [],
  "summary": "..."
}

7. User Engagement (Plateau Only)

If the score has plateaued (improved < 5 points) and is below threshold:

1. Present unresolved items — only the critical and high challenges that remain open
2. Ask targeted questions — not a barrage. Focus on items that genuinely need human knowledge:
   • Internal infrastructure details not findable via web search
   • Business constraints and priorities
   • Organizational decisions (e.g., timeline flexibility, budget constraints)
3. Wait for user input on each question
4. Recompute score after user provides answers
5. If score now meets threshold, proceed to finalize. Otherwise, note remaining gaps.

REVIEW PLATEAU — Need Your Input
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Score: {score}% (target: 80%, improved only {delta}pts last round)

{count} items need information only you can provide:

1. CHK-007 [critical] Database sizing: How large is the production SQL
   Server database? (Current assumption: <50GB)
   → This affects migration approach choice and timeline.

2. CHK-011 [high] VPN connectivity: Does the current AWS environment
   use Site-to-Site VPN or Direct Connect?
   → This determines networking phase complexity.

Please answer what you can — any input helps improve the review.

8. Finalize and Present Report

Once the review terminates (pass, conditional pass, or plateau with user engagement):

CHALLENGE REVIEW COMPLETE
━━━━━━━━━━━━━━━━━━━━━━━━━

Step: {step} | Rounds: {N} | Status: {PASSED ✓ | CONDITIONAL PASS ⚠ | PLATEAU ◆}

Final Confidence Score: {score}%

  Completeness     {score}%  ████████░░
  Consistency      {score}%  ██████████
  Plausibility     {score}%  ███████░░░
  Currency         {score}%  ████████░░  (verified {date})
  Risk Coverage    {score}%  █████████░

Round History:
  Round 1: 52% → 62% (+10)
  Round 2: 62% → 74% (+12)
  Round 3: 74% → 82% (+8) ← PASSED

Challenges: {resolved}/{total} resolved
  Critical: {count} ({open} open)
  High: {count} ({open} open)
  Medium: {count} ({open} open)
  Low: {count} ({open} open)

Research Findings: {count} web-verified items
  {count} confirmed current data
  {count} suggested knowledge updates

{#if conditional_pass}
⚠ Conditional pass — the following items should be addressed before proceeding:
  - CHK-xxx: ...
  - CHK-xxx: ...
{/if}

{#if plateau}
◆ Review plateaued — unresolved items documented. Consider addressing
  before proceeding to the next step.
{/if}

Recommendations:
- {next step suggestion}
- {if challenge_required is false} Consider enabling challenge gates:
  set challenge_required on the assessment for mandatory quality checks

Suggest the appropriate next step:

• After discovery review: /migrate analyze
• After analysis review: /migrate estimate
• After estimate review: proceed to refine step in the web UI
• After refine review: /migrate plan

Important Notes

• Maximum rounds: Cap at 5 rounds to prevent infinite loops. After 5 rounds, force-present remaining items to user regardless of score.
• WebSearch usage: Always use WebSearch for currency dimension checks. This is a key differentiator of the challenge review — bringing in live data.
• JSON snapshots: Always write to .migration/challenge-reviews/ directory, creating it if needed.
• Autonomous operation: The loop between Challenger and Researcher should proceed without user interaction unless a plateau is reached. Keep the user informed of progress between rounds with the score update display.
• No destructive changes: The challenge review reads data but does NOT modify discovery, analysis, estimate, or scope data. It only writes challenge review records. Any suggested changes are noted as recommendations.