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A curated hub of agents, skills, guides, tools & commands for building with AI coding agents — drop-in ready and format-validated.
Specialized subagents for focused work
- → API Architect
- → Frontend Developer
- → ML Engineer
Reusable capabilities Claude loads on demand
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Finetuning Engineer
Use this agent to fine-tune an open-weight model end to end — confirming fine-tuning is the right tool, preparing the dataset, choosing the method (LoRA/QLoRA vs. full), running training, and proving the result beats the prompted baseline on a held-out eval set. Examples — "fine-tune a small model to match our support tone and answer format", "we have 800 labeled examples — LoRA-tune and show it beats prompting", "our fine-tune overfits and forgot general ability — fix the data and run".
LLM Cost Optimizer
Use this agent to cut the cost and latency of an application's LLM API usage without losing quality — audit where the tokens and dollars go, then apply caching, model right-sizing, prompt trimming, batching, and budgets, proven against an eval bar. Examples — "our OpenAI bill tripled, find where the spend is and cut it", "this endpoint's p95 is 8s, bring it down", "right-size models per task and add prompt caching to our chat feature".
LLM Inference Engineer
Use this agent to serve and optimize self-hosted LLM inference — sizing GPUs, configuring a serving engine like vLLM (continuous batching, PagedAttention, tensor parallelism), applying quantization, and tuning throughput and tail latency against a cost and p95 budget. Examples — "serve Llama-3-70B at p95 under 2s on our GPUs", "our self-hosted model is slow and the GPUs sit half-idle — raise throughput", "quantize this model to fit one GPU without wrecking quality".
Postgres Migration Engineer
Use this agent to plan and execute a zero-downtime Postgres schema migration — decomposing a breaking change into expand-contract steps, writing batched backfills, building indexes CONCURRENTLY, validating constraints online, and keeping every step reversible with the project's migration tooling. Examples — "add a NOT NULL column to a 200M-row table without downtime", "rename a column safely across a rolling deploy", "split this risky migration into reversible expand/contract steps".
Vector Search Engineer
Use this agent to design, build, and tune the vector-database layer of a search or RAG system — schema and index design (HNSW/IVF + quantization), metadata/payload filtering, hybrid (dense + sparse) search, and ingestion/upsert pipelines — sized to a real latency, recall, and cost budget. Examples — "set up pgvector for our docs with HNSW and filtered search", "our Qdrant queries are slow and recall dropped after quantization", "add metadata filtering so search only returns the current tenant's documents".
Voice Agent Engineer
Use this agent to build or fix a real-time voice agent — the streaming STT → LLM → TTS pipeline, conversational (mouth-to-ear) latency, turn-taking, barge-in/interruptions, and per-stage provider selection. Examples — "our voice bot feels laggy and talks over people, fix the turn-taking and latency", "build a phone agent that transcribes, answers with our LLM, and speaks back", "get our voice agent's response time under a second".