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proffesor-for-testing

consultancy-practices

by proffesor-for-testing

Agentic QE Fleet is an open-source AI-powered quality engineering platform designed for use with Claude Code, featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.

132🍴 27📅 Jan 23, 2026

SKILL.md


name: consultancy-practices description: "Apply effective software quality consultancy practices. Use when consulting, advising clients, or establishing consultancy workflows." category: professional-practice priority: medium tokenEstimate: 950 agents: [qe-quality-analyzer, qe-regression-risk-analyzer, qe-quality-gate] implementation_status: optimized optimization_version: 1.0 last_optimized: 2025-12-03 dependencies: [] quick_reference_card: true tags: [consulting, advisory, client-engagement, quality-assessment, transformation]

Consultancy Practices

<default_to_action> When consulting on quality:

  1. LISTEN FIRST: Understand their context before prescribing solutions
  2. DISCOVER: What's the pain? What have they tried? What are constraints?
  3. PRIORITIZE: Impact/effort matrix - high impact, low effort first
  4. TRANSFER KNOWLEDGE: Leave them better, not dependent on you
  5. MEASURE: Define success metrics upfront, track weekly

Engagement Types:

  • Assessment (1-4 weeks): Discover, analyze, recommend
  • Transformation (3-12 months): Implement new practices
  • Advisory (ongoing): Strategic guidance, course-correct
  • Crisis (1-4 weeks): Fix critical issues blocking production

Key Questions:

  • "Walk me through your last deployment"
  • "Tell me about a recent bug that escaped to production"
  • "If you could fix one thing, what would it be?" </default_to_action>

Quick Reference Card

The Consulting Process

PhaseDurationGoalDeliverable
DiscoveryWeek 1-2Understand contextInterview notes, observations
AnalysisWeek 2-3Identify root causesImpact/effort matrix
RecommendationsWeek 3-4Present findingsReport with roadmap
ImplementationMonth 2-6+Execute changesWorking system, trained team
TransitionFinal monthEnsure self-sufficiencyHandover docs

Impact/Effort Matrix

PriorityWhatAction
High Impact, Low EffortQuick winsDo first
High Impact, High EffortMajor initiativesPlan carefully
Low Impact, Low EffortNice-to-havesIf time permits
Low Impact, High EffortDistractionsSkip

Common Patterns

"We Need Test Automation"

What they say: "We need test automation" What they mean: "Manual testing is too slow/expensive"

Discovery: How long is regression? What's deployment frequency?

Typical Finding: They need faster feedback, not "automation"

Recommendation:

  1. Unit tests for new code (TDD)
  2. Smoke tests for critical paths
  3. Keep exploratory for discovery
  4. Build automation incrementally

"Fix Our Quality Problem"

What they say: "We have too many bugs" What they mean: "Something is broken but we don't know what"

Discovery: Where found? What types? When introduced?

Typical Finding: No test strategy, testing too late, poor feedback loops

Recommendation:

  1. Shift testing left
  2. Improve coverage on critical paths
  3. Speed up CI/CD feedback
  4. Better requirements/acceptance criteria

"We Want to Scale Quality"

What they say: "Growing fast, quality can't keep up" What they mean: "Can't hire enough QA fast enough"

Discovery: Current QA:Dev ratio? Where's QA spending time?

Typical Finding: QA is bottleneck - manual regression, gatekeeping

Recommendation:

  1. Make QA strategic, not tactical
  2. Developers own test automation
  3. QA focuses on exploratory, risk analysis
  4. Use agentic approaches for scale

Anti-Patterns

Anti-PatternProblemBetter
Cookie-CutterSame solution everywhereContext-specific recommendations
Tool PusherRecommend expensive toolsTools that solve actual problems
Process NaziImpose rigid processLightweight, fits their culture
Permanent FixtureNever leave, create dependencyWork toward them not needing you
Blame GamePoint fingers at peopleFix systems, not blame people

Difficult Situations

"We already tried that" → "Tell me what you tried and what didn't work" (learn from their experience)

"Our context is special" → "Help me understand what makes yours special" (they might be right, or making excuses)

"We don't have budget/time" → "What's the cost of not fixing this? Let's start small" (show ROI)

"That won't work here" → "What specific constraints? Let's adapt" (find what WILL work)


Agent Integration

// Automated codebase assessment
const assessment = await Task("Assess Codebase", {
  scope: 'client-project/',
  depth: 'comprehensive',
  reportFormat: 'executive-summary'
}, "qe-quality-analyzer");

// Returns: { qualityScore, testCoverage, technicalDebt, recommendations }

// ROI analysis for quality initiatives
const roi = await Task("Calculate ROI", {
  currentState: { defectEscapeRate: 0.15, mttr: 48 },
  proposedImprovements: ['test-automation', 'ci-cd-pipeline'],
  timeframe: '6-months'
}, "qe-quality-analyzer");

// Returns: { estimatedCost, estimatedSavings, paybackPeriod }

Agent Coordination Hints

Memory Namespace

aqe/consultancy/
├── assessments/*      - Client assessments
├── recommendations/*  - Prioritized recommendations
├── roi-analysis/*     - ROI calculations
└── progress/*         - Implementation tracking

Fleet Coordination

const consultingFleet = await FleetManager.coordinate({
  strategy: 'client-engagement',
  agents: [
    'qe-quality-analyzer',          // Assess current state
    'qe-regression-risk-analyzer',  // Risk assessment
    'qe-quality-gate',              // Define quality gates
    'qe-deployment-readiness'       // Deployment maturity
  ],
  topology: 'hierarchical'
});


Remember

Good consulting is about empowering teams, not creating dependency. Your success is measured by them not needing you anymore - while still wanting to work with you again.

Best compliment: "We've got this now, but when we tackle X next year, we're calling you."

Be honest. Be helpful. Be context-driven. Leave them better.

Score

Total Score

85/100

Based on repository quality metrics

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