ai-product

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.

Quality

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/antigravity-ai-product/SKILL.md

Quality

Discovery

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 is a marketing slogan or philosophical statement, not a functional skill description. It provides zero information about what the skill does, what actions it performs, or when it should be selected. It would be completely unusable for skill selection in a multi-skill environment.

Suggestions

Replace the marketing tagline with concrete actions the skill performs, e.g., 'Guides architecture and implementation of AI-powered product features, including prompt engineering, model selection, and API integration.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about building AI features, integrating LLMs into products, or moving AI prototypes to production.'

Remove opinion-based language ('The question is whether you'll build it right') and replace with objective, third-person capability statements that describe specific deliverables or outputs.

Dimension	Reasoning	Score
Specificity	The description contains no concrete actions whatsoever. It reads as a marketing tagline or opinion statement rather than describing what the skill does. There are no verbs indicating capabilities like 'extract', 'analyze', 'generate', etc.	1 / 3
Completeness	The description answers neither 'what does this do' nor 'when should Claude use it'. There is no 'Use when...' clause or any equivalent trigger guidance. It is entirely a motivational/opinion statement with no functional information.	1 / 3
Trigger Term Quality	The only potentially relevant keyword is 'AI-powered', which is extremely generic. There are no natural terms a user would say when needing a specific skill. 'Production', 'demo', and 'ship' are too vague to serve as meaningful triggers.	1 / 3
Distinctiveness Conflict Risk	The description is so vague and generic that it could apply to virtually any AI-related skill. It provides no distinguishing characteristics that would help Claude select it over any other skill.	1 / 3
	Total	4 / 12 Passed

Implementation

14%

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

This skill is extremely verbose and redundant, explaining many concepts Claude already understands (probabilistic outputs, prompt injection, streaming benefits) while repeating the same themes across Patterns and Sharp Edges sections. It reads more like a blog post or tutorial for junior developers than a concise skill file for Claude. The lack of any progressive disclosure structure or clear workflow sequencing makes it a monolithic reference that wastes significant context window space.

Suggestions

Cut content by 60-70%: Remove explanations of why patterns matter (Claude knows), eliminate redundancy between Patterns and Sharp Edges sections, and drop the Validation Checks and Collaboration sections which add little actionable value.

Split into multiple files: Move Sharp Edges to a separate SHARP_EDGES.md, patterns to PATTERNS.md, and keep SKILL.md as a concise overview with references.

Add a clear workflow sequence: Define a step-by-step process for implementing AI features (e.g., 1. Design prompt → 2. Add validation → 3. Implement streaming → 4. Add monitoring) with explicit validation checkpoints.

Make code examples fully executable: Add missing imports, fix syntax issues (e.g., `topK: 20` should be in a proper options object), and add language annotations to all code blocks.

Dimension	Reasoning	Score
Conciseness	Extremely verbose at ~500+ lines. Extensively explains concepts Claude already knows (what RAG is, why LLMs hallucinate, what prompt injection is, why streaming matters). Massive redundancy—the same patterns (validation, streaming, caching) appear in both Patterns and Sharp Edges sections with overlapping code examples. The principles section explains obvious things like 'LLM calls take 1-30 seconds' and 'LLMs are probabilistic.'	1 / 3
Actionability	Contains numerous code examples that are mostly executable TypeScript/Python snippets, but many are incomplete (missing imports, pseudocode-like syntax like `topK: 20` without proper object syntax, `rerank()` function undefined). Some code blocks lack language annotations. The patterns are concrete but not fully copy-paste ready.	2 / 3
Workflow Clarity	No clear multi-step workflow with validation checkpoints. The content is organized as a reference catalog of patterns and anti-patterns rather than a sequenced workflow. For a skill covering AI product development (which involves destructive/batch operations like RAG indexing, prompt deployment), there are no explicit validation steps, feedback loops, or sequenced processes to follow.	1 / 3
Progressive Disclosure	Monolithic wall of text with no references to external files despite the massive length. Everything is inline—patterns, sharp edges, validation checks, collaboration workflows—all in one enormous document. Content like the detailed sharp edges section, validation checks, and collaboration workflows could easily be split into separate referenced files.	1 / 3
	Total	5 / 12 Passed

Validation

81%

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 — 9 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (754 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	9 / 11 Passed

Repository: boisenoise/skills-collections
Commit: 431bfad

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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