Complete SDLC kit for solo developers using Claude Code. Six-phase pipeline (Discovery → Requirements → Architecture → Implementation → Verification → Ship) with TDD methodology, multi-perspective code review, deep verification, ADR/PRD writing, DDD modeling, design system management, autonomous overnight loops, and Karpathy code discipline rules. Was previously distributed as kit (manual copy) - now first-class plugin. Complements ai-scrum plugin (which adds hard discipline hooks and sprint state machine). Based on philosophy: plan first, verify always, recommend after every phase.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Audit the .claude/ setup for conflicts, stale instructions, drift from current Claude Code spec.
Watch open PRs, automatically respond to code review comments, keep branches rebased. Use with /loop for continuous PR maintenance.
Commit current changes, push to a feature branch, open a PR with auto-generated description.
Implement frontend pixel-equivalent to a provided mockup. Extracts tokens, builds components in isolation, verifies side-by-side.
Investigate a data source ONCE and save reference to docs/data-sources/. Reuses existing reference if fresh.
MUST BE USED for Django implementation - models, views, DRF, admin, signals, migrations. Use when the project is a Django app or Django REST Framework API. Knows the idiomatic Django patterns and the common pitfalls.
MUST BE USED for Go backend implementation. Builds Go services, libraries, CLIs following idiomatic Go and the project's CLAUDE.md rules. Use when implementing in Go - APIs, workers, CLIs, gRPC services.
MUST BE USED for Python backend implementation (FastAPI, Flask, plain Python services). Use when implementing Python services, APIs, workers, CLIs that don't require Django specifically. For Django projects, prefer backend-django.
MUST BE USED after verification passes and before commit/PR. Multi-perspective review: correctness, security, performance, maintainability. Severity-tagged findings (Critical / High / Medium / Low). Stack-aware (Go, Python, Django, React, Vue).
MUST BE USED before writing any code that touches a new data source (database, API, queue). Investigates the source ONCE and produces a permanent reference at docs/data-sources/. PROACTIVELY check for existing reference before re-investigating. Solves the "Claude keeps re-asking about the database" problem.
Write Architecture Decision Records (ADRs) for any meaningful technical decision. Use this skill whenever an architectural choice is made (database, framework, protocol, library, pattern). Triggers when an architecture decision is being made, when alternatives are being weighed, or when the user asks "why did we use X". Outputs a numbered ADR file under docs/architecture/adr/.
Design the system architecture from an approved PRD. Use this skill in Phase 3 of every feature, AFTER PRD is approved and BEFORE writing any code. Triggers on requests for system design, architecture, design doc, "how to build this", tech choices, or component design. Produces a design doc with C4 diagrams, ADRs, and a solutioning gate check.
Apply LLM coding discipline to every edit - minimum code, surgical changes, surface assumptions, goal-driven execution. Use this skill ALWAYS when writing or editing code, especially when modifying existing files. Triggers on any code-writing task, refactor, bug fix, or feature implementation. Inspired by Karpathy's observations on common LLM coding pitfalls. Prevents overcomplication, scope creep, silent assumptions, and weak success criteria.
Multi-perspective code review (correctness, security, performance, maintainability) on implemented changes. Use this skill AFTER verification passes and BEFORE the work is shipped. Triggers when implementation is complete, before merging, or when user asks for review. Outputs severity-tagged findings with concrete fixes, not generic advice.
Generate concrete, actionable improvement recommendations after every phase, every implementation, and every review. Use this skill at the END of every Claude Code task. Triggers when a phase completes, after implementation is done, after a code review, or when the user asks "what could be better". The user explicitly values critical analysis over validation. Outputs structured recommendations the user can act on or ignore.
Uses power tools
Uses Bash, Write, or Edit tools
Marketplace плагинов Claude Code для AI-driven разработки.
| Plugin | Что делает | Версия |
|---|---|---|
| sdlc-audit | Глубокий разовый SDLC-аудит проекта от архитектуры до бизнес-логики. Поддерживает Python, JS/TS, Go. | 0.1.0 |
В Claude Code:
/plugin marketplace add shakhovskiya-create/shakhoff-claude-marketplace
/plugin install sdlc-audit@shakhoff-claude-marketplace
/reload-plugins
Полный аудит проекта:
/audit:full --depth=standard
Точечный аудит:
/audit:architecture
/audit:domain
/audit:code
/audit:tests
/audit:security
/audit:docs
/audit:deploy
Применение фиксов:
/audit:fix next
/audit:fix CRITICAL-001
/audit:fix --severity=BLOCKER
Подробная документация в plugins/sdlc-audit/README.md.
shakhoff-claude-marketplace/
├── .claude-plugin/
│ └── marketplace.json # манифест маркетплейса
├── plugins/
│ └── sdlc-audit/ # плагин полного SDLC аудита
│ ├── .claude-plugin/plugin.json
│ ├── commands/ # 11 команд
│ ├── skills/ # 14 skills
│ ├── agents/ # 8 subagents
│ ├── templates/ # шаблоны отчётов
│ ├── README.md
│ └── INSTALL.md
├── .claude/ # audit config для самого репо
├── README.md # этот файл
├── CONTRIBUTING.md # как добавлять новые плагины
├── LICENSE
└── .gitignore
Если нужно тестировать плагин до публикации на GitHub:
git clone https://github.com/shakhovskiya-create/shakhoff-claude-marketplace
cd shakhoff-claude-marketplace
# В Claude Code в любом тестовом проекте
/plugin marketplace add file:///absolute/path/to/shakhoff-claude-marketplace
/plugin install sdlc-audit@shakhoff-claude-marketplace
См. CONTRIBUTING.md.
Marketplace построен вокруг идеи плагин = набор инструментов для одной задачи:
MIT - см. LICENSE.
Issues и feature requests - GitHub Issues этого репо.
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Sign in to claimnpx claudepluginhub shakhovskiya-create/shakhoff-claude-marketplace --plugin solo-sdlcDeep one-shot SDLC audit covering architecture, domain logic, code quality, tests, security, documentation, and deployment. Produces actionable backlog with severity classification and ready-to-paste fix prompts. Multi-language support: Python, JavaScript/TypeScript, Go. Integrates with ai-scrum plugin for execution discipline (sprint planning, AC/DoR/DoD gates, internal SE review).
Universal sprint orchestrator for AI agents in Claude Code. Applies Scrum-like discipline to any development task: context investigation, task decomposition with AC/DoR/DoD, hard execution gates, internal SE review, optional Codex external review, HTML reports with design system integration, LSP-based code analysis, issue tracker MCP integration. Works as platform - any plugin can declare sprint_required and ai-scrum orchestrates execution. v0.1.2: integration with claude-solo-factory kit (delegate to kit skills if available - tdd, code-review, verify, prd-writer, adr-writer, wiki-workflow).
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Feature development with code-architect/explorer/reviewer agents, CLAUDE.md audit and session learnings, and Agent Skills creation with eval benchmarking from Anthropic.
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
Intelligent draw.io diagramming plugin with AI-powered diagram generation, multi-platform embedding (GitHub, Confluence, Azure DevOps, Notion, Teams, Harness), conditional formatting, live data binding, and MCP server integration for programmatic diagram creation and management.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.