magic-powers

Use when conducting quality audits — reviewing process compliance, identifying gaps between defined process and actual practice, conducting structured inspections (code review audits, test quality reviews), and producing audit reports with remediation plans.

Use when managing quality risk — identifying quality risks in a product or release, applying risk-based testing prioritization, creating risk mitigation plans, and communicating quality risk to stakeholders for go/no-go decisions.

Use when designing or implementing test automation — choosing the right automation framework (Playwright, pytest, JUnit), Page Object Model, selector strategies, test isolation, managing flaky tests, and CI integration.

Use when managing defects — writing effective bug reports, applying severity/priority matrix, tracking defect lifecycle, conducting root cause analysis, and measuring defect metrics for process improvement.

Use when measuring and reporting QA quality — defect escape rate, test coverage analysis, flaky test rate, mean time to detect, shift-left metrics, and building quality dashboards for stakeholders.

Use when managing test data — designing test data strategies, using factories and builders, creating fixtures, generating synthetic data, masking PII for testing, and managing test database state.

Use when designing test cases — applying boundary value analysis, equivalence partitioning, decision tables, pairwise testing, and exploratory testing techniques to maximize defect detection with minimal test cases.

Use when starting any conversation - establishes how to find and use skills, model routing, and cost-aware development

Use when testing mobile applications — device matrix strategy, iOS and Android testing tools (XCUITest, Espresso, Appium), gesture and interaction testing, network condition testing, app lifecycle testing, and mobile-specific quality concerns.

Use when testing security from a QC perspective — OWASP Top 10 test cases, authentication and authorization testing, input validation testing, security regression testing, and integrating security checks into the QC process.

Use when facilitating User Acceptance Testing — planning UAT sessions with business stakeholders, designing business-scenario test cases (not technical), coordinating UAT execution, managing UAT defects, and obtaining formal sign-off.

Use when designing quality assurance processes — defining quality standards, integrating QA checkpoints into SDLC, creating process documentation, onboarding teams to quality practices, and building a quality-first engineering culture.

Use when implementing container CI/CD on Azure DevOps — Docker image builds with caching, pushing to Azure Container Registry, deploying to AKS with Helm or kubectl, and image promotion across environments.

Use when designing Azure Pipelines YAML — multi-stage pipelines, reusable templates, conditions and expressions, matrix strategies, triggers, and pipeline dependencies for complex CI/CD workflows.

Use when improving Azure Pipelines performance — caching dependencies, parallel job strategies, artifact management between stages, test result publishing, code coverage gates, and reducing pipeline runtime.

Use when hardening Azure Pipelines security — YAML pipeline permissions, fork build security, resource authorization, secret scanning, protected resources, and preventing pipeline-based attacks.

Use when implementing release management on Azure DevOps — deployment gates (quality gates), pre/post-deployment approvals, deployment rings, rollback strategies, deployment freeze windows, and multi-environment promotion.

Use when automating Azure DevOps operations — az devops CLI, REST API calls, PAT management, service principal automation, webhooks, and scripting repeatable ADO administrative tasks.

Use when managing Azure Artifacts — feed creation and permissions, upstream sources, retention policies, package promotion across views, and connecting build pipelines to artifact feeds.

Use when setting up or managing Azure DevOps organizations and projects — project creation, team structure, user management, billing, extensions, and org-level settings.

Use when managing Azure DevOps pipeline infrastructure — self-hosted agent pools, service connections, variable groups, secure files, environments, approvals, and pipeline resource governance.

Use when configuring Azure DevOps security — security groups and permissions, branch policies, PR policies, audit log review, and org/project-level security governance.

Use when configuring Azure DevOps work tracking — boards setup, backlog configuration, sprint management, work item type customization, process templates, queries, and team area/iteration paths.

Use when starting or ending a Claude Code working session — load context from memory, output a session brief, save decisions and progress at session end, and ensure work is resumable next session.

Use when executing a structured workflow — select and run a feature, bugfix, refactor, research, or incident template with correct agent and model assignments per phase.

Use when choosing between Claude models for a task — decision tree for Haiku/Sonnet/Opus based on task type, cost estimates, escalation triggers, and cascade patterns.

Use when building defensibility into an AI product — designing data collection strategies that compound over time, domain-specific dataset building, proprietary data as competitive moat vs base models, and when data beats prompt engineering.

Use when pricing an AI product — choosing between usage-based/hybrid/outcome pricing, calculating unit economics, protecting margins against LLM cost, and setting prices that reflect value without losing customers.

Use when defining how an AI product stands out — defensibility assessment, outcome-based messaging, feature vs product decision, competitive moat design, and positioning for a specific niche.

Use when designing AI products for long-term retention — stickiness patterns, daily engagement hooks, workflow integration depth, habit loops specific to AI, and measuring whether users actually keep using your AI feature.

Use when deciding whether to build an AI product — rapid problem validation, market discovery, early user interviews, and demand signals BEFORE writing any code. Prevents the

Use when automating Claude Code workflows with hooks — PreToolUse (validate/block actions), PostToolUse (react to completions), Stop (enforce standards before finishing), and SessionStart (load context). Configure in .claude/settings.json.

Use when connecting Claude Code to external services via MCP (Model Context Protocol) — configuring MCP servers for databases, APIs, file systems, and custom tools, and designing effective tool descriptions for Claude.

Use when writing or improving CLAUDE.md files — project context that Claude Code reads every session, global vs project rules, what to include for maximum AI effectiveness, and memory-aware documentation patterns.

Use when leveraging Claude Code's auto-memory system — understanding what Claude saves to memory, writing good memory entries manually, structuring the memory directory, and using memory for project continuity across sessions.

Use when configuring Claude Code for a project — .claude/settings.json structure, permission modes, model selection, tool allowlists/denylists, and team vs personal settings.

Use when building go-to-market as a solo founder — distribution playbook for <5 hours/week, AI-powered personalized outreach, community leverage (Twitter/Reddit/ProductHunt/IndieHackers), and sales-led loops for early traction.

Use when rapidly prototyping AI products — going from idea to working demo in 4-8 hours using AI-assisted development tools (Cursor, Bolt, v0, Lovable), knowing when to vibe vs spec, and transitioning prototypes to production.

Use when building evaluation infrastructure for AI systems — test harnesses, CI pipelines for AI, automated regression detection, golden datasets, and continuous quality measurement.

Use when productizing AI features for end users — UX patterns for AI, streaming, loading states, error handling, fallback design, reliability, and responsible AI disclosure.