ai-engineer
Build Production-Ready LLM Systems
Production AI systems require reliable retrieval, orchestration, safety, monitoring, and cost controls. This skill provides structured engineering guidance across the complete LLM application lifecycle.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "ai-engineer" from https://skillstore.io/skills/sickn33-ai-engineer.md and its manifest at https://skillstore.io/api/skills/sickn33-ai-engineer/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "ai-engineer". Design a RAG system for an internal policy library.
Expected outcome:
A staged architecture covering document ingestion, permission-aware hybrid retrieval, reranking, cited answers, offline evaluation, monitoring, and gradual rollout.
Using "ai-engineer". Reduce the cost and latency of an existing support assistant.
Expected outcome:
A prioritized plan using model routing, semantic caching, prompt compression, streaming, budget limits, and quality checks before each optimization.
Using "ai-engineer". Plan a customer service agent with refund tools.
Expected outcome:
A controlled workflow with scoped tools, explicit approval thresholds, durable state, escalation paths, audit logs, failure recovery, and adversarial tests.
Security Audit
SafeAll four static findings are false positives caused by safety and information-retrieval terminology in SKILL.md. The file contains guidance only and does not direct system reconnaissance, jailbreak behavior, code execution, or data exfiltration. No net-new semantic security risks were found.
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APA citation
sickn33. (2026). ai-engineer security audit report (audit version 6) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-ai-engineer/audits/6BibTeX citation
@techreport{sickn33-sickn33-ai-engineer-2026,
author = {sickn33},
title = {ai-engineer security audit report (audit version 6)},
institution = {Skillstore},
year = {2026},
number = {6},
url = {https://skillstore.io/skills/sickn33-ai-engineer/audits/6},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "ai-engineer security audit report (audit version 6)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-ai-engineer/audits/6"
identifiers:
- type: other
value: "skillstore:sickn33-ai-engineer:audit:6"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Add an LLM Feature
Design a reliable model integration with typed outputs, fallback behavior, testing, and cost limits.
Build Enterprise RAG
Plan ingestion, chunking, hybrid retrieval, reranking, evaluation, access control, and production monitoring.
Define an Agent Architecture
Turn a product workflow into tools, state transitions, guardrails, human escalation, and measurable success criteria.
Try These Prompts
Design an LLM feature for [use case]. State assumptions, model choice, request flow, safety controls, tests, and a small rollout plan.
Design a RAG system for [documents and users]. Cover ingestion, chunking, retrieval, reranking, citations, evaluation, permissions, latency, and cost.
Design an agent for [workflow]. Define tools, permissions, state, memory, failure handling, human approval points, observability, and adversarial tests.
Review this architecture: [details]. Identify reliability, security, quality, latency, and cost risks. Prioritize fixes and define measurable release gates.
Best Practices
- Define quality, latency, cost, safety, and reliability targets before selecting models or frameworks.
- Evaluate retrieval and generation separately with representative data, adversarial inputs, and traceable failure categories.
- Use least-privilege tools, redact sensitive data, require approval for high-impact actions, and monitor production behavior.
Avoid
- Do not choose an agent when a deterministic workflow or simple model call can solve the task.
- Do not send sensitive data to external models without authorization, minimization, redaction, and retention controls.
- Do not launch without evaluation baselines, observability, fallback behavior, cost limits, and a rollback plan.