Show HN: Claude Code Subagents – 100 domain-expert helpers for dev workflows

Show HN: Claude Code Subagents – 100 domain-expert helpers for dev workflows(github.com)

2 points by 0xfurai 268 days ago | 1 comment

I built a collection of 100+ specialized subagents for Claude Code that act like on-call experts (Python, React, Postgres, Docker, Stripe, Kafka, Prometheus, etc.). They can be auto-invoked by context or explicitly called (“use the postgres-expert to design indexes”), and each carries a focused system prompt + quality checklist. The runner picks a Claude model per task to balance speed/cost.

- Covers languages, frameworks, DBs/ORMs, DevOps, testing, ML, observability, auth, payments, messaging, and more. - Zero server: they’re just files in ~/.claude/agents/. - MIT-licensed and contributions welcome.

I’d love feedback on missing domains, rough edges in prompts, and real-world cases where this helped (or failed). Thanks!

mutant 266 days ago |

For foundational models, I feel like this is working against the model, they already are trained as experts, they need guidance, not prose about how to be an expert

# Effective steering stack: "FastAPI + SQLAlchemy + Redis" scale: "10k RPS, sub-50ms P99" deployment: "K8s, multi-region" constraints: ["async-first", "12-factor", "observability"]

# Not this python-expert: "You are an expert in advanced Python..."

# This context: "Building FastAPI backend, PostgreSQL, Redis cache, Docker deployment" constraints: "Sub-100ms response times, 10k concurrent users" preferences: "Async-first, type hints, structured logging"

I stopped telling ai how to do their jobs a long time ago, and started context management, I get crazy better results. The only time i need to bash training in is when it doesn't know an API, then I spawn a research agent to create an updated training prompt for an API, or command, then import it as needed. Keeps the primary context window cleaner for longer.