builder-ai v1.0.0
June 8, 2026 · View on GitHub
Your AI assistant will skip the eval, change the prompt without versioning it, add no fallback, and ship without a safety review.
This pack makes that impossible.
Drop one folder into your project. Your AI coding assistant now enforces production standards for every LLM feature — not as suggestions it can ignore, but as gates it cannot pass without evidence.
Mac / Linux / WSL:
bash <(curl -fsSL https://raw.githubusercontent.com/RBraga01/builder-ai/master/install.sh)
Windows PowerShell:
irm https://raw.githubusercontent.com/RBraga01/builder-ai/master/install.ps1 | iex
Works on Claude Code, Codex CLI, Cursor, and OpenCode. Works alongside A Team, builder-design, builder-product, and builder-growth.
The Four Ways LLM Features Fail in Production
Every one of these has happened to a team that was confident before launch:
1. Shipped without an eval "I tested it and it looked good." The feature worked on the 8 examples you chose. It failed on 30% of real traffic. Nobody knew which prompt change caused it because there was no baseline.
2. Prompt changed, nobody noticed "Small tweak." A single instruction shifted pass rate from 89% to 72%. There was no previous version to compare against. The regression took three weeks to diagnose.
3. No fallback when the model misbehaved Timeout at peak load → blank response → support ticket at 3am. "We'll add error handling after launch." You added it at 3am.
4. Shipped without a safety review "Nobody will try that." Someone did, on day two. The injection vector was in the document upload — not the user message — and it had been there since the first commit.
builder-ai makes each of these a gate your AI assistant must pass before marking any LLM task complete.
What's in the Pack
Hard Gates — Cannot Be Skipped
| Skill | What It Blocks |
|---|---|
eval-before-ship | No LLM feature merges without a named eval suite, documented pass rate, failure analysis, and baseline |
prompt-versioning | No prompt goes to production without a version file in prompts/ and a CHANGELOG entry |
fallback-required | No LLM call ships without tested fallback paths for timeout, malformed output, low confidence, and refusal |
Workflow Skills — How to Do the Work Right
| Skill | What It Enforces |
|---|---|
rag-pipeline-design | Data audit + query audit before any pipeline decision — no "standard chunking" shortcuts |
model-benchmarking | Task-specific benchmarking across three tiers before committing to a model |
context-optimization | Measure → reduce by hierarchy → measure again — not guessing at token savings |
ai-cost-audit | Token count + call volume + cost at 10× scale before launch, not after the billing alert |
ai-safety-review | Four-category review with tested attack surfaces before any feature reaches users |
Agents — Specialist Roles
| Agent | Role | Model |
|---|---|---|
prompt-engineer | Writes, versions, and iterates prompts with eval criteria | Sonnet |
eval-designer | Designs evaluation suites and writes eval harnesses | Sonnet |
rag-architect | Designs and debugs retrieval pipelines | Opus |
model-selector | Benchmarks models and recommends the cost-optimal choice | Sonnet |
ai-safety-reviewer | Reviews for injection, hallucination, abuse, and agentic scope | Opus |
How Enforcement Works
Each hard gate defines exactly what an agent must produce — not a checklist to tick, a formatted evidence block it must fill in with real numbers.
An agent reading eval-before-ship cannot say "task complete" without producing:
Eval complete.
Suite: evals/email-classifier/test-set.jsonl — 200 examples
Model: claude-sonnet-4-6, temperature: 0.0, seed: 42
Pass rate: 178/200 = 89% (threshold: ≥ 85% ✓)
Top failure mode: format violation (12 cases — emails > 2000 tokens)
Baseline: v1 = 82% → v2 = 89%, delta: +7pp ✓
Results stored: evals/email-classifier/results-2026-06-07.md
"It looks good" does not fill that template. That is the entire point.
Each skill also lists the Rationalization Red Flags — the exact things teams say when they want to skip the gate — and explains why each one is wrong. The agent has already read the rebuttals.
Integration with A Team
If A Team is installed, builder-ai slots in with no conflicts:
cp -r builder-ai/skills/* your-project/skills/
cp -r builder-ai/.claude/agents/* your-project/.claude/agents/
The builder-ai agents can be dispatched by the A Team orchestrator like any other specialist. All names are unique across both packs.
Directory Layout After Installation
your-project/
├── skills/ ← drop-in enforcement rules
├── .claude/agents/ ← specialist roles
├── prompts/ ← versioned prompts (prompt-engineer creates these)
├── evals/ ← eval suites + results (eval-designer creates these)
├── benchmarks/ ← model comparison results (model-selector creates these)
├── cost-audit/ ← cost projections
└── safety-reviews/ ← safety review documents