llm-engineering
Building features and systems on top of LLMs — designing tools agents can actually use, structured outputs, context budgeting, when multi-agent is worth it, reliability around model calls, evals before prompt-tweaking, and prompt-injection defense. Use whenever writing code that calls an LLM API, designing agent tools or MCP servers, building agents or multi-agent pipelines, writing or tuning prompts, adding RAG/embeddings, or debugging why an LLM feature behaves badly.
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writing-shape
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full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
