By discopops
Systems programming with Rust, Go, C, and C++ for performance-critical and low-level development
Write efficient C code with proper memory management, pointer arithmetic, and system calls. Handles embedded systems, kernel modules, and performance-critical code. Use PROACTIVELY for C optimization, memory issues, or system programming.
Write idiomatic C++ code with modern features, RAII, smart pointers, and STL algorithms. Handles templates, move semantics, and performance optimization. Use PROACTIVELY for C++ refactoring, memory safety, or complex C++ patterns.
Master Go 1.21+ with modern patterns, advanced concurrency, performance optimization, and production-ready microservices. Expert in the latest Go ecosystem including generics, workspaces, and cutting-edge frameworks. Use PROACTIVELY for Go development, architecture design, or performance optimization.
Master Rust 1.75+ with modern async patterns, advanced type system features, and production-ready systems programming. Expert in the latest Rust ecosystem including Tokio, axum, and cutting-edge crates. Use PROACTIVELY for Rust development, performance optimization, or systems programming.
Master Go concurrency with goroutines, channels, sync primitives, and context. Use when building concurrent Go applications, implementing worker pools, or debugging race conditions.
Implement memory-safe programming with RAII, ownership, smart pointers, and resource management across Rust, C++, and C. Use when writing safe systems code, managing resources, or preventing memory bugs.
Master Rust async programming with Tokio, async traits, error handling, and concurrent patterns. Use when building async Rust applications, implementing concurrent systems, or debugging async code.
Uses power tools
Uses Bash, Write, or Edit tools
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Production-ready agentic workflow building blocks: 82 plugins, 191 agents, 155 skills, 102 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, and Gemini CLI 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 82 plugins
→ Full Claude Code setup, troubleshooting, and plugin catalog
gh repo clone wshobson/agents ~/agents
cd ~/agents
make generate HARNESS=<codex|cursor|opencode|gemini>
Per-harness setup guides: CODEX.md · CURSOR.md · OPENCODE.md · GEMINI.md
| Count | What it is | |
|---|---|---|
| Plugins | 82 | Granular, single-purpose installable units (81 local + 1 external via git-subdir) |
| Agents | 191 | Domain experts (architecture, languages, infra, security, data, ML, docs, business, SEO) |
| Skills | 155 | Modular knowledge packages with progressive disclosure (load when activated) |
| Commands | 102 | 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, …)
Three-tier model strategy:
| Tier | Model | Use |
|---|---|---|
| 1 | Opus 4.7 | 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 | .codex/skills/, .codex/agents/, AGENTS.md | 8 KB skill cap respected; commands → skills |
| Cursor | .cursor-plugin/, .cursor/rules/ | Thin marketplace + curated rules; reuses .claude/ |
| OpenCode | .opencode/agents/, .opencode/commands/ | permission: block from tools: allowlist |
| Gemini CLI | skills/, agents/, commands/ (TOML) | Native skills + subagents (April 2026 spec) |
make generate-all # all four
make validate # structural checks
make garden # drift / dead-link / cap detection
→ Full capability matrix and per-harness deep-dives
plugin-eval is a three-layer evaluation framework for measuring
and certifying plugin/skill quality:
uv run plugin-eval score path/to/skill --depth quick
uv run plugin-eval certify path/to/skill
→ PluginEval framework documentation
Detail lives in docs/. Read in this order:
npx claudepluginhub discopops/agents --plugin systems-programmingMulti-perspective code analysis covering architecture, security, and best practices
Pre-deployment checks, configuration validation, and deployment readiness assessment
Database architecture, schema design, and SQL optimization for production systems
Deployment patterns, rollback automation, and infrastructure templates
Distributed system tracing and debugging across microservices
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.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex, antigravity, and grok CLIs when installed) to get diverse perspectives on coding problems
Complete developer toolkit for Claude Code
Production-grade engineering skills for AI coding agents — covering the full software development lifecycle from spec to ship.
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.
Feature development with code-architect/explorer/reviewer agents, CLAUDE.md audit and session learnings, and Agent Skills creation with eval benchmarking from Anthropic.