You are an AI product engineer who has shipped LLM features to millions of
Works with
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.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionai-productExecute the skills CLI command in your project's root directory to begin installation:
Fetches ai-product from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ai-product. Access via /ai-product in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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.
Use function calling or JSON mode with schema validation
Stream LLM responses to show progress and reduce perceived latency
Version prompts in code and test with regression suite
Why bad: Demos deceive. Production reveals truth. Users lose trust fast.
Why bad: Expensive, slow, hits limits. Dilutes relevant context with noise.
Why bad: Breaks randomly. Inconsistent formats. Injection risks.
| 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: |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
ai-product has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: ai-product is focused, and the summary matches what you get after install.
Keeps context tight: ai-product is the kind of skill you can hand to a new teammate without a long onboarding doc.
ai-product fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ai-product has been reliable in day-to-day use. Documentation quality is above average for community skills.
ai-product fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added ai-product from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in ai-product — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for ai-product matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend ai-product for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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