ai-product
Build Production-Ready AI Products
AI prototypes often fail under production traffic, unreliable outputs, prompt attacks, and rising costs. This skill provides practical patterns for reliable, secure, and measurable AI features.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "ai-product" from https://skillstore.io/skills/sickn33-ai-product.md and its manifest at https://skillstore.io/api/skills/sickn33-ai-product/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-product". Plan a customer support ticket classifier.
Expected outcome:
- Use structured model output with category, priority, summary, and confidence fields.
- Validate every response before storage and route invalid or uncertain results to human review.
- Track accuracy, latency, token cost, and escalation rates by prompt version.
Using "ai-product". Review a document assistant that sends every retrieved section to one model.
Expected outcome:
- Rerank results and include only the strongest evidence within a measured token budget.
- Verify cited sources and show uncertainty when evidence is weak.
- Cache repeated retrievals and monitor cost, latency, and answer quality.
Using "ai-product". Improve resilience for a slow AI endpoint.
Expected outcome:
- Stream interactive responses and move long processing into a background queue.
- Set explicit timeouts, use a circuit breaker, and provide cached or rule-based fallbacks.
- Monitor provider errors, queue delay, completion latency, and fallback usage.
Security Audit
SafeAll 35 static findings are false positives caused by TypeScript template literals, Markdown fences, ordinary prose, or quoted defensive examples. The skill contains educational guidance and illustrative code, with no executable shell commands, credential access, reconnaissance, or malicious prompt injection.
Risk Factors
⚙️ External commands (27)
🌐 Network access (3)
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<iframe src="https://skillstore.io/embed/skills/sickn33-ai-product.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). ai-product security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-ai-product/audits/5BibTeX citation
@techreport{sickn33-sickn33-ai-product-2026,
author = {sickn33},
title = {ai-product security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-ai-product/audits/5},
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-product security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-ai-product/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-ai-product:audit:5"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Plan a New AI Feature
Create an architecture covering model calls, validation, latency, failure handling, and cost controls before implementation.
Harden an AI Prototype
Review a working prototype for prompt injection, malformed outputs, missing limits, weak monitoring, and provider failures.
Design a RAG System
Define retrieval, reranking, context budgeting, source verification, and evaluation for a knowledge assistant.
Try These Prompts
Outline an AI feature for [user need]. Include inputs, outputs, model role, validation, latency target, fallback, and success metrics.
Review this AI workflow: [workflow]. Identify reliability, security, latency, cost, and observability gaps. Prioritize practical fixes.
Design RAG for [knowledge source] and [query type]. Specify ingestion, retrieval, reranking, context limits, citations, evaluation, and update strategy.
Create an operations plan for [AI service]. Cover rate limits, budgets, caching, timeouts, fallbacks, monitoring, incident handling, and prompt versioning.
Best Practices
- Validate model outputs and treat all generated content as untrusted input.
- Version prompts, test representative cases, and measure quality after every change.
- Track latency, token usage, cost, failures, and user-level limits from the first release.
Avoid
- Do not place unsanitized user input inside privileged prompt instructions.
- Do not use the largest model for every task without measured quality benefits.
- Do not deploy an AI dependency without timeouts, fallbacks, monitoring, and graceful error states.