AZANIR

Master skill coordinating all QA skills through pipeline modes (full-cycle, docs-only, testcases-only, write-tests, report), formalized handoff chains, scheduler rules, and framework/language selection based on project context.

QA project memory with auto-update. Structured log of bugs, decisions, tests, regressions, environments. Automatically updated after every QA task. Archive system with searchable index for large projects. Trigger phrases (EN): "initialize memory", "init qa memory", "find known bug", "search memory", "show what was done", "check regressions", "memory status", "archive memory", "log a bug", "add decision", "memory summary", "what bugs do we know", "update memory", "show test log". Trigger phrases (UA): "ініціалізувати пам'ять", "знайти відомий баг", "пошук у пам'яті", "показати що було зроблено", "перевірити регресії", "статус пам'яті", "архівувати пам'ять", "залогувати баг", "додати рішення", "зведення пам'яті", "які баги відомі", "оновити пам'ять", "показати лог тестів", "що ми вирішили про", "які тестові середовища є".

Analyze OpenAPI/Swagger spec (JSON or YAML) against existing test files and generate an HTML coverage report with QA automation tasks. Use when user provides an OpenAPI spec file and wants to know test coverage status.

Generate accessibility tests for WCAG 2.2 compliance using axe-core, Pa11y, and Lighthouse with automated checks for ARIA patterns, keyboard navigation, color contrast, and screen reader support.

Manage and formalize API contracts from existing endpoints, swagger/JSON, network traffic, or developer interviews into OpenAPI specifications.

Autonomously explore live web applications using Playwright MCP to collect page structure, form fields, validation rules, API endpoints, and user flows for test case generation.

Generate structured bug reports from test failures with expected/actual results, reproduction steps, evidence, severity/priority, and auto-create GitHub Issues via GitHub MCP with duplicate detection.

Analyze git diff and commit history to recommend which tests to add, update, or run based on code changes.

Pull weekly traffic data from Plausible Analytics for artstroy.net, produce a structured digest with top articles, category breakdown, referrer mix, and trend signals. Posted to the root company issue weekly.

Cross-post published ArtStroy articles to dev.to and Hashnode with correct canonical URLs, metadata mapping, and formatting. Handles API integration for automated posting and creates manual-assist drafts for platforms without API access.

Scan ArtStroy npm/bun dependencies for known vulnerabilities. Ranks CVEs by CVSS severity, creates vuln-fix issues with deadlines, and verifies fixes post-deploy. Run monthly and on any new dependency addition.

Manage and verify the ArtStroy CI/CD pipeline. Covers the GitHub Actions deploy workflow, SSH-based Hetzner deployment, Docker Compose orchestration, and post-deploy health checks. Used by DevOps Engineer for deploy operations and pipeline maintenance.

Manage ArtStroy's Docker Compose configuration, Hetzner server provisioning, environment variable management, and infrastructure-as-code practices. Used by DevOps Engineer for all infrastructure changes.

Compile new ArtStroy articles into a Markdown newsletter draft ready for Buttondown. Runs weekly; sends draft to human for final review before dispatch.

Manage secrets rotation, GitHub Actions secret updates, and environment variable injection for ArtStroy. Ensures secrets never appear in logs, git history, or plaintext files. Used by DevOps Engineer and Security Auditor.

Verify artstroy.net HTTP security headers against OWASP best practices. Checks CSP, HSTS, X-Frame-Options, X-Content-Type-Options, Permissions-Policy, and Referrer-Policy. Creates issues for missing or misconfigured headers.

Optional code-navigation aid for large or unfamiliar codebases. Use during the plan or implement phases to map module dependencies, find where a symbol is defined and used, and orient before making changes. This is a navigation helper only — it is NOT the memory layer. Skip for small projects.

Persistent memory layer across sessions. Use to recall prior context at the start of work, to store checkpoints at phase boundaries, and to answer "what did we decide / do before" questions. Backed by the agentmemory server (local SQLite + vector embeddings) on port 3111, with an MCP-only fallback. Every phase skill calls this to survive /clear and compaction boundaries.

Phase 4 of the SDLC, used ONLY for user-facing features. Use this to produce UI/UX design documentation and a persistent design system before implementation. Triggers when tasks.md exists and the spec marked a UI surface. Coordinates with the ui-ux-pro-max skill to generate design-system/MASTER.md and per-page specs. Skip entirely for CLI tools, libraries, or backend-only work.

Phase 5 of the SDLC. Use this to write code that satisfies the tasks, strictly test-first (TDD). Triggers when tasks.md exists and design is done or not needed. Implements one task at a time, writing a failing test before code, then making it pass, then refactoring. Persists progress to memory after each task so work survives session boundaries.

Master coordinator for the full software development lifecycle. Use this when starting a new feature or project, when the user is unsure which phase to run, when resuming work after a break, or to establish/edit the project constitution. Routes to specify, plan, tasks, design, implement, review, and ship phases in the correct order and enforces that no phase is skipped. ALWAYS consider this skill first at the start of any development work.

Phase 2 of the SDLC. Use this to decide HOW a specified feature will be built — architecture, tech choices, data model, API contracts, and trade-offs. Triggers after a spec exists and the user wants a technical plan or design decisions. Produces specs/<feature>/plan.md plus Architecture Decision Records (ADRs). Must read spec.md and memory/constitution.md and respect their constraints.

Phase 6 of the SDLC. Use this to verify the implementation against the spec's acceptance criteria and the project constitution before shipping. Triggers when implementation is complete. Produces specs/<feature>/review.md — a filled acceptance checklist with pass/fail per criterion and required fixes. Does not ship; gates whether ship is allowed.

Phase 7 of the SDLC, the final phase. Use this to prepare a release after review has passed. Triggers when review.md shows all criteria passing. Runs the mandatory security-scan gate, generates release notes in Ukrainian, and prepares (but does not auto-execute) the commit/tag/PR. A failing security scan blocks the ship and is surfaced to the user.

Manage git branches and create Conventional Commits for the Gapta project. Use this skill when committing changes, creating branches, preparing PRs, or when the user says "commit", "create branch", "prepare PR", or "push changes".

Write complete MDX blog articles for gapta.com.ua (computerized embroidery niche) in Ukrainian, with correct frontmatter, SEO, and optional React components. Use when creating new posts for the Gapta blog once content collections exist, or when drafting article structure and copy.

Set up and maintain a structured project memory system in docs/project_notes/ that tracks bugs with solutions, architectural decisions, key project facts, and work history. Use this skill when asked to "set up project memory", "track our decisions", "log a bug fix", "update project memory", or "initialize memory system". Configures both CLAUDE.md and AGENTS.md to maintain memory awareness across different AI coding tools.

SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.

Write technical articles, blog posts, and long-form content that reads like it was written by a human, not generated by AI. Use this skill when creating articles, tutorials, case studies, essays, or any written content where authenticity matters. Avoids common AI writing tells documented by Wikipedia and other sources.