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builder-ai — Build LLM Products That Don't Fail Silently v1.0.0

A drop-in skill and agent pack for teams building LLM-powered products. Three hard gates block incomplete work. Five workflow skills cover the full build cycle. Five specialist agents handle prompt engineering, evaluation, RAG, model selection, and safety.

Works standalone. Works alongside A Team.

Skills in This Pack

Hard Gates (mandatory before shipping)

  • eval-before-ship — no LLM feature ships without documented eval results
  • prompt-versioning — all prompts must be versioned in prompts/
  • fallback-required — every LLM call must have a fallback path

Workflow Skills

  • rag-pipeline-design — chunking, embedding, retrieval, reranking, generation
  • model-benchmarking — structured benchmarking before committing to a model
  • context-optimization — reduce cost and latency without quality loss
  • ai-cost-audit — audit and project LLM API spend
  • ai-safety-review — injection, hallucination, abuse, and agentic scope review

Agents in This Pack

Agent Trigger
prompt-engineer Writing, iterating, or debugging prompts
eval-designer Designing evaluation suites for LLM outputs
rag-architect Designing or debugging retrieval pipelines
model-selector Selecting the cost-optimal model for a task
ai-safety-reviewer Safety and abuse review before user-facing features ship

Expected Directory Layout After Installation

your-project/
├── prompts/              ← versioned prompts (created by prompt-engineer)
│   └── feature-name/
│       ├── v1.0.0.md
│       └── CHANGELOG.md
├── evals/                ← eval suites and results (created by eval-designer)
│   └── feature-name/
│       ├── test-set.jsonl
│       └── results-<date>.md
├── benchmarks/           ← model comparison results (created by model-selector)
├── cost-audit/           ← cost audit reports (created by ai-cost-audit skill)
├── safety-reviews/       ← safety review documents (created by ai-safety-reviewer)
├── skills/               ← this pack's skills
└── .claude/agents/       ← this pack's agents

Using Hard Gates

When an agent encounters a task that requires a hard gate, it must:

  1. Read the relevant skill file
  2. Follow the process
  3. Produce the required evidence (eval results, versioned prompt, fallback tests)
  4. Only then mark the task complete

"Looks good" is not evidence. The skill files specify exactly what counts.