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ai-wrapper-product

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc. ) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI.

35

Quality

32%

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tessl review fix ./plugins/antigravity-awesome-skills/skills/ai-wrapper-product/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%Scale 1-3

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

The skill provides genuinely useful, executable code examples for building AI wrapper products, covering cost tracking, rate limiting, streaming, and output validation. However, it is severely bloated — much of the content explains concepts Claude already knows (retry patterns, exponential backoff, caching), and the entire skill is a monolithic document that should be split across multiple files. The redundancy between sections (duplicate model tables, overlapping Expertise/Capabilities lists) and unnecessary personality framing further inflate token cost.

Suggestions

Cut the file by 50%+: remove the Role/personality preamble, deduplicate Expertise/Capabilities, eliminate explanations of standard patterns (exponential backoff, caching, rate limiting concepts) and keep only the product-specific implementation details.

Split into multiple files: move Sharp Edges, Prompt Engineering patterns, and Cost Management into separate referenced files (e.g., COST-MANAGEMENT.md, PROMPT-PATTERNS.md) with one-line summaries and links in the main SKILL.md.

Add an explicit end-to-end build workflow with validation checkpoints (e.g., 'verify API key is server-side only', 'confirm usage tracking logs before launch', 'test rate limit handling under load').

Remove duplicate model selection tables and consolidate into a single, up-to-date reference (noting that specific pricing and model names are time-sensitive and may become stale).

DimensionReasoningScore

Conciseness

Extremely verbose at ~400+ lines. Explains concepts Claude already knows (what rate limiting is, what hallucinations are, basic retry patterns). Redundant sections (e.g., 'Capabilities' and 'Expertise' overlap heavily, model selection table appears twice with slightly different data). The 'Role' preamble and personality description waste tokens. Many patterns like exponential backoff and request queuing are standard knowledge for Claude.

1 / 3

Actionability

Provides fully executable JavaScript code examples throughout — API calls, cost tracking, retry logic, streaming, caching, queue management, and output validation are all copy-paste ready with real library imports (Anthropic SDK, p-queue). Concrete patterns with specific implementation details.

3 / 3

Workflow Clarity

The 'Wrapper Stack' diagram provides a clear high-level sequence, and the Sharp Edges sections have good problem-solution structure. However, there's no overarching build workflow with validation checkpoints — the collaboration workflows at the bottom are just numbered lists without verification steps. For a product involving API keys, cost management, and destructive billing potential, explicit validation gates are missing.

2 / 3

Progressive Disclosure

Monolithic wall of text with no bundle files to reference. All content — architecture, prompt engineering, cost management, differentiation strategy, sharp edges, validation checks, collaboration workflows — is crammed into a single file. No references to external files for detailed topics like prompt engineering patterns or cost management guides. Content would benefit enormously from splitting into focused reference files.

1 / 3

Total

7

/

12

Passed

Description

22%Scale 1-3

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

The description reads more like a marketing tagline than a functional skill description. It lacks concrete actions, has no 'Use when...' clause, and relies on vague language like 'focused tools people will pay for' rather than specifying what the skill actually does. The mention of specific API providers (OpenAI, Anthropic) provides some trigger value but is insufficient to make this a well-functioning skill description.

Suggestions

Add a 'Use when...' clause with explicit triggers, e.g., 'Use when the user wants to build a SaaS product, AI wrapper, or commercial tool using OpenAI, Anthropic, or other LLM APIs.'

Replace vague language with specific concrete actions, e.g., 'Designs API integration architectures, creates prompt pipelines, structures pricing/billing models, builds rate limiting and caching layers, and plans go-to-market strategies for AI-powered products.'

Add more natural trigger terms users would say, such as 'SaaS', 'MVP', 'wrapper app', 'monetize AI', 'LLM product', 'API costs', 'prompt management.'

DimensionReasoningScore

Specificity

The description uses vague language like 'building products' and 'focused tools' without listing concrete actions. It describes a philosophy ('not just ChatGPT but different') rather than specific capabilities like 'designs API integration architectures, creates pricing models, builds prompt pipelines.'

1 / 3

Completeness

The 'what' is vaguely stated (building products that wrap AI APIs) but lacks specifics, and there is no 'when' clause or explicit trigger guidance at all. The missing 'Use when...' clause caps this at 2 per the rubric, but the 'what' is also weak enough to warrant a 1.

1 / 3

Trigger Term Quality

It includes some relevant keywords like 'AI APIs', 'OpenAI', 'Anthropic', and 'products' that users might mention, but misses many natural variations like 'SaaS', 'wrapper', 'API integration', 'monetize', 'startup', 'MVP', or 'prompt engineering.'

2 / 3

Distinctiveness Conflict Risk

The AI API wrapper product niche is somewhat specific, distinguishing it from general coding or general AI skills, but 'building products' and 'AI' are broad enough to overlap with many other skills related to software development, API integration, or AI assistance.

2 / 3

Total

6

/

12

Passed

Validation

81%

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

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (689 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
popey/claude-code-skills
Reviewed

Table of Contents

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