jeremy-vertex-engine

23-agent engineering + product team with 125 skills for Claude Code

49 skills · 100 agents · hooks

Multi-agent orchestration system with 51 specialized agents for development workflows, code quality, deployment, research, games, and more. Includes orchestrators for checkpoint, deploy, quality, fullstack, research, games, corpus, web, and python workflows.

51 agents

Point it at a GitHub issue and get back a reviewed pull request. Hyperflow turns one AI coding session into a structured engineering pipeline: plan sharpens and decomposes the work, dispatch fans out parallel workers, and a domain specialist reviews every step — nothing ships unreviewed. 18 skills, 22 specialist reviewer and investigator agents, adaptive depth so a 5-line fix never triggers a deep run, and persistent per-project memory that lives in your repo and never leaves it. Runs on whatever model your session already uses — zero config, no API keys, no daemon. Works across Claude Code, Codex App/CLI, OpenCode, Grok, Antigravity, and Cursor.

18 skills · 23 agents · hooks

Claude Code skill pack for Shopify (38 skills covering e-commerce development, storefront APIs, and app integration)

38 skills

LangChain 1.0 + LangGraph 1.0 skill pack for Python. Pain-first skills anchored to a 68-entry pain catalog covering content blocks, streaming token accounting, structured-output, vector stores, checkpointing, HITL, middleware, Deep Agents, and native OTEL. Includes 33 skills across four epics.

33 skills

Claude Code skill pack for building with the Claude API and Anthropic SDK.

32 skills

Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.

10 skills · 2 commands · 3 agents

Build Gemini- and ChatGPT-style apps, assistants with memory, RAG over vector databases and multi-provider AI features in JavaScript with the IntelliNode library, plus an MCP server with cross-provider tools.

1 skill · 1 MCP server

LLM application development, prompt engineering, and AI assistant optimization

5 skills · 3 commands · 2 agents

LLM application development, prompt engineering, and AI assistant optimization

5 skills · 3 commands · 2 agents

LLM application development, prompt engineering, and AI assistant optimization

4 skills · 3 commands · 2 agents

Expert FinOps guidance for cloud, AI, SaaS and data-platform spend. Ask Claude how to reduce a bill, size a commitment, allocate shared costs or justify AI spend, and it answers with billing mechanics, decision rules and worked examples, not generic advice. Cloud: AWS, Azure, GCP, OCI, Kubernetes and on-premises or hybrid estates, including Savings Plans, Reserved Instances, Azure Hybrid Benefit and contract negotiation. AI: Anthropic, Bedrock, Azure OpenAI, Vertex AI, open-weight model APIs, AI coding tools, agent costs, self-hosted vs managed inference. Data platforms: Databricks, Snowflake, Microsoft Fabric. FinOps practice: allocation, showback and chargeback, forecasting, operating model, FOCUS multi-cloud cost data, tagging, anomalies, KPIs, workload onboarding, SaaS and IT asset management, GreenOps. Waste playbooks: 44 runbooks for waste patterns such as idle NAT gateways, orphaned disks, snapshot sprawl, unused reservations, oversized GPUs and coding-agent token waste. Each gives a detection query to run in your account, the fix, and a confidence level. Good to know: this plugin adds instructions only and does not connect to your cloud accounts. Open source (CC BY-SA 4.0), maintained by OptimNow and updated weekly.

1 skill