ai-product▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
Production-ready LLM integration patterns, from prompt versioning to safety validation and cost optimization.
- ›Covers structured output with schema validation, streaming responses for reduced latency, and prompt versioning with regression testing
- ›Identifies eight critical sharp edges including output validation, prompt injection risks, context window limits, and API failure handling
- ›Emphasizes treating prompts as code, validating all LLM outputs, and never trusting responses blindly i
AI Product Development
You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard. You treat prompts as code, validate all outputs, and never trust an LLM blindly.
Patterns
Structured Output with Validation
Use function calling or JSON mode with schema validation
Streaming with Progress
Stream LLM responses to show progress and reduce perceived latency
Prompt Versioning and Testing
Version prompts in code and test with regression suite
Anti-Patterns
❌ Demo-ware
Why bad: Demos deceive. Production reveals truth. Users lose trust fast.
❌ Context window stuffing
Why bad: Expensive, slow, hits limits. Dilutes relevant context with noise.
❌ Unstructured output parsing
Why bad: Breaks randomly. Inconsistent formats. Injection risks.
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Trusting LLM output without validation | critical | # Always validate output: |
| User input directly in prompts without sanitization | critical | # Defense layers: |
| Stuffing too much into context window | high | # Calculate tokens before sending: |
| Waiting for complete response before showing anything | high | # Stream responses: |
| Not monitoring LLM API costs | high | # Track per-request: |
| App breaks when LLM API fails | high | # Defense in depth: |
| Not validating facts from LLM responses | critical | # For factual claims: |
| Making LLM calls in synchronous request handlers | high | # Async patterns: |
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
Discussion
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Ratings
4.5★★★★★51 reviews- ★★★★★Ren Zhang· Dec 20, 2024
ai-product reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakura Ndlovu· Dec 16, 2024
Solid pick for teams standardizing on skills: ai-product is focused, and the summary matches what you get after install.
- ★★★★★Fatima Srinivasan· Dec 16, 2024
We added ai-product from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★James Haddad· Nov 7, 2024
I recommend ai-product for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Fatima White· Nov 7, 2024
ai-product fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ren Khanna· Oct 26, 2024
Keeps context tight: ai-product is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Yusuf Thomas· Oct 26, 2024
ai-product has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★James Martinez· Sep 17, 2024
ai-product fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Sep 13, 2024
ai-product fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aanya Nasser· Sep 9, 2024
Useful defaults in ai-product — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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