By wshobson
Automate code cleanup and legacy modernization with AI-driven analysis that detects code smells, SOLID violations, and technical debt, then generates prioritized refactoring plans and safe migration strategies using the strangler fig pattern while restoring project context from vector storage.
Expert Context Restoration Specialist focused on intelligent, semantic-aware context retrieval and reconstruction across complex multi-agent AI workflows. Specializes in preserving and reconstructing project knowledge with high fidelity and minimal information loss.
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create actionable remediation plans.
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
Refactor legacy codebases, migrate outdated frameworks, and implement gradual modernization. Handles technical debt, dependency updates, and backward compatibility. Use PROACTIVELY for legacy system updates, framework migrations, or technical debt reduction.
Uses power tools
Uses Bash, Write, or Edit tools
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Production-ready agentic workflow building blocks: 88 plugins, 194 agents, 158 skills, 106 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, Gemini CLI, and GitHub Copilot from a single Markdown source.
[!NOTE] One source-of-truth (
plugins/), five harnesses. Each harness gets idiomatic, harness-native artifacts — not lowest-common-denominator translations. See docs/harnesses.md for the capability matrix.
Pick your harness:
/plugin marketplace add wshobson/agents
/plugin install python-development # or any of 88 plugins
→ Full Claude Code setup, troubleshooting, and plugin catalog
Codex and Cursor install natively from the committed registries (which point at the source plugins/):
npx codex-marketplace add wshobson/agents # Codex; then install individual plugins
# Cursor: add the marketplace, then `/plugin install <name>` (reads .cursor-plugin/ + source)
Gemini and OpenCode install via clone + generate (the transformed trees are gitignored):
gh repo clone wshobson/agents ~/agents && cd ~/agents
make generate HARNESS=gemini && gemini extensions install . # Gemini
make install-opencode # OpenCode (runs generate + symlinks)
Setup details and per-harness gotchas: docs/harnesses.md. Gemini-specific setup: GEMINI.md (also auto-loaded by Gemini CLI).
| Count | What it is | |
|---|---|---|
| Plugins | 88 | Granular, single-purpose installable units (85 local + 3 external via git-subdir) |
| Agents | 194 | Domain experts (architecture, languages, infra, security, data, ML, docs, business, SEO) |
| Skills | 158 | Modular knowledge packages with progressive disclosure (load when activated) |
| Commands | 106 | Slash commands: scaffolding, security scans, test gen, infrastructure setup |
| Orchestrators | 16 | Multi-agent coordination workflows (full-stack, security, ML, incident response) |
Browse the catalog: docs/plugins.md · docs/agents.md · docs/agent-skills.md
Each plugin is isolated and composable: agents, commands, and skills are auto-discovered from directory structure. Installing a plugin loads only its components into context — not the whole marketplace.
plugins/python-development/
├── .claude-plugin/plugin.json
├── agents/ # 3 Python agents (python-pro, django-pro, fastapi-pro)
├── commands/ # 1 scaffolding command
└── skills/ # 16 specialized skills (async, testing, packaging, …)
Tiered model strategy:
| Tier | Model | Use |
|---|---|---|
| 0 | Fable 5 | Longest-horizon autonomous work — large migrations, multi-hour runs (opt-in, premium cost) |
| 1 | Opus | Architecture, security, code review, production-critical |
| 2 | inherit | User-chosen — backend, frontend, AI/ML, specialized |
| 3 | Sonnet | Docs, testing, debugging, API references |
| 4 | Haiku | Fast operational tasks, SEO, deployment, content |
This marketplace ships to five agentic harnesses from one Markdown source. Each adapter emits harness-native artifacts (not lowest-common-denominator translations):
| Harness | Generates | Notes |
|---|---|---|
| Claude Code | (source-of-truth) | Native marketplace.json + plugins/ |
| Codex CLI | .agents/plugins/marketplace.json + plugins/*/.codex-plugin/plugin.json (committed); .codex/skills/, .codex/agents/ (gitignored) | 8 KB skill cap respected; commands → skills |
| Cursor | .cursor-plugin/, .cursor/rules/ | Thin marketplace + curated rules; reuses .claude/ |
| OpenCode | .opencode/agents/, .opencode/commands/, .opencode/skills/ | permission: block from tools: allowlist; OpenCode-safe skill names |
| Gemini CLI | skills/, agents/, commands/ (TOML) | Native skills + subagents (April 2026 spec) |
| Copilot | .copilot/agents/, .copilot/skills/, .copilot/commands/ | Markdown agent profiles + SKILL.md skills + commands-as-skills; model maps to native Claude models |
Test-driven development methodology with red-green-refactor cycles and code review
Production-grade Bash scripting with defensive programming, POSIX compliance, and comprehensive testing
LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.6 and GPT-5.4
JavaScript and TypeScript development with ES6+, Node.js, React, and modern web frameworks
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices
npx claudepluginhub wshobson/agents --plugin code-refactoringTechnical debt reduction, dependency updates, and code refactoring automation
AI-powered codebase modernization assessment - Interactive audit and quick scan skills to identify technical debt, anti-patterns, and quality issues from older AI-generated code
Codebase exploration, refactoring, and quality analysis
Disciplined refactoring using small, verifiable steps based on Fowler's 'Refactoring' and Feathers' 'Working Effectively with Legacy Code'
Refactor code following best practices and design patterns
Full-spectrum design patterns skill: Detect anti-patterns, diagnose architectural debt, and generate production-ready refactored code. Covers GoF, SOLID, Clean Architecture, DDD, CQRS, Microservices, and Cloud patterns.