ai-product-patterns

Builds AI-native products using OpenAI's development philosophy and modern AI UX patterns. Use when integrating AI features, designing for model improvements, implementing evals as product specs, or creating AI-first experiences. Based on Kevin Weil (OpenAI CPO) on building for future models, hybrid approaches, and cost optimization.

Quality

76%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./ai-product-patterns/SKILL.md

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a comprehensive, actionable skill with excellent code examples and clear workflows for building AI-native products. The main weakness is verbosity - the content could be 30-40% shorter while preserving all actionable guidance. The repetition of concepts across sections (hybrid approaches explained multiple times, similar examples repeated) inflates token count unnecessarily.

Suggestions

Consolidate the hybrid approach explanations - currently explained in section 3, the decision tree, and multiple examples with redundant content

Remove the 'Common Pitfalls' section as it largely repeats guidance already covered in the main frameworks

Trim explanatory text around code examples - the code is self-documenting and doesn't need extensive prose setup

Dimension	Reasoning	Score
Conciseness	The skill contains valuable content but is verbose with repetitive explanations and excessive formatting. Many concepts are over-explained (e.g., the hybrid approach is explained multiple times with similar examples), and some sections like 'Common Pitfalls' repeat earlier content.	2 / 3
Actionability	Provides concrete, executable code examples throughout including JavaScript/TypeScript patterns for hybrid approaches, evals as tests, streaming implementations, and cost optimization strategies. Templates are copy-paste ready with clear structure.	3 / 3
Workflow Clarity	Clear decision tree for when to use AI vs traditional code, well-structured checklists (Before Building, During Build, Before Ship), and explicit validation steps in the evals approach. The sequential flow from spec to implementation to shipping is well-defined.	3 / 3
Progressive Disclosure	Well-organized with clear sections progressing from core frameworks to templates to examples. References to related skills and further learning files are clearly signaled at the end. Content is appropriately structured for a comprehensive skill.	3 / 3
	Total	11 / 12 Passed

Description

67%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This description has good structure with explicit 'Use when' triggers and clear attribution to a specific source (Kevin Weil/OpenAI). However, the capabilities described are somewhat abstract ('AI-native products', 'modern AI UX patterns') rather than concrete actions, and the trigger terms could be expanded to include more natural user language variations.

Suggestions

Add more concrete actions like 'design streaming interfaces', 'implement fallback strategies', 'structure evaluation frameworks' to improve specificity

Expand trigger terms to include natural variations users might say: 'LLM integration', 'ChatGPT-style features', 'AI product roadmap', 'prompt optimization'

Dimension	Reasoning	Score
Specificity	Names the domain (AI-native products, OpenAI philosophy) and mentions some actions like 'integrating AI features', 'implementing evals', 'designing for model improvements', but these are somewhat abstract rather than concrete specific actions like 'extract text' or 'create pivot tables'.	2 / 3
Completeness	Clearly answers both what ('Builds AI-native products using OpenAI's development philosophy') and when ('Use when integrating AI features, designing for model improvements, implementing evals as product specs, or creating AI-first experiences') with explicit trigger guidance.	3 / 3
Trigger Term Quality	Includes some relevant keywords like 'AI features', 'evals', 'AI-first experiences', 'cost optimization', but missing common variations users might say like 'LLM integration', 'prompt engineering', 'AI product design', or 'OpenAI API'.	2 / 3
Distinctiveness Conflict Risk	The OpenAI-specific focus and Kevin Weil attribution provide some distinctiveness, but 'AI features' and 'AI-first experiences' are broad enough to potentially overlap with other AI/ML related skills.	2 / 3
	Total	9 / 12 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (539 lines); consider splitting into references/ and linking	Warning

	Total	10 / 11 Passed

Repository: menkesu/awesome-pm-skills
Commit: 53530ef

Reviewed: 4 months ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.