本文へ移動
cccskills
無料GitHub で公開

consultancy-practices

Apply effective software quality consultancy practices. Use when consulting, advising clients, or establishing consultancy workflows.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qeスター 515(GitHub 515 + cccskills 0)

実行スコア 未検証

インストール方法を見る

含まれるファイル(2)

  • SKILL.md5.1 KB
  • schemas/output.json9.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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


Common Patterns (What Clients Say vs. What They Mean)

"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

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'
});

Related Skills


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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

コメント

ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。

まだコメントはありません。最初のコメントを書いてみませんか。

送信するとすぐに公開されます。メールアドレスや電話番号などの個人情報は書かないでください。URL は 2 つまでです。

0 / 1000 文字

このフォームは reCAPTCHA で保護されており、Google のプライバシーポリシーと利用規約が適用されます。

同じリポジトリのスキル

a11y-ally

無料

Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

proffesor-for-testing のスキルをすべて見る

このスキルの問題を報告する