By dxas90
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — content feeds, search ranking, RAG rerankers, task prioritizers, notification triage, ad selection.
Uses power tools
Uses Bash, Write, or Edit tools
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Production-ready agentic workflow building blocks: 92 plugins, 199 agents, 162 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 92 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 | 92 | Granular, single-purpose installable units (88 local + 4 external via git-subdir) |
| Agents | 199 | Domain experts (architecture, languages, infra, security, data, ML, docs, business, SEO) |
| Skills | 162 | 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 |
npx claudepluginhub p/dxas90-machine-learning-ops-plugins-machine-learning-opsDocumentation generation, code explanation, and technical writing with automated doc generation and tutorial creation
Interactive debugging, developer experience optimization, and smart debugging workflows
Error analysis, trace debugging, and multi-agent problem diagnosis
Performance analysis, test coverage review, and AI-powered code quality assessment
Git workflow automation, pull request enhancement, and team onboarding processes
Harness-native ECC operator layer - 64 agents, 262 skills, 84 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
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.
Consult multiple AI coding agents (Gemini, OpenAI, Grok, Perplexity, plus codex and antigravity CLIs when installed) to get diverse perspectives on coding problems
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.
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.
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.