By wshobson
Build and automate end-to-end ML pipelines from data preparation through model training, hyperparameter tuning, experiment tracking, and production deployment with multi-agent orchestration across cloud platforms.
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 wshobson/agents --plugin machine-learning-opsModern Julia development with Julia 1.10+, package management, scientific computing, high-performance numerical code, and production best practices
Team workflows, issue management, standup automation, and developer experience optimization
Binary reverse engineering, malware analysis, firmware security, and software protection research for authorized security research, CTF competitions, and defensive security
Dependency auditing, version management, and security vulnerability scanning
SAST analysis, dependency vulnerability scanning, OWASP Top 10 compliance, container security scanning, and automated security hardening
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.
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Train and optimize machine learning models with automated workflows
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
AI/ML development: LLM architecture, prompt engineering, ML ops, and NLP with production deployment focus