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app-store-optimization

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.

93

1.18x
Quality

91%

Does it follow best practices?

Impact

95%

1.18x

Average score across 6 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

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

The body is highly actionable, with concrete commands, formulas, templates, and data tables, and every workflow carries an explicit validation checkpoint. Its weaknesses are length-driven: detail that duplicates the reference files is inlined in SKILL.md, workflows lack failure/retry loops, and a boilerplate 'Communication' section adds little. Moving duplicated detail into the existing references and adding 'if validation fails' handling would lift it to top marks.

Suggestions

Move detail duplicated in the references out of SKILL.md — e.g., the platform character-limits table and description-structure template overlap references/platform-requirements.md and references/aso-best-practices.md; keep only the workflow steps and a 'See:' pointer inline.

Add error-recovery feedback loops to the workflows: after each 'Validation' checkpoint, state what to do on failure (e.g., 'if character limits are exceeded, trim modifiers and re-validate').

Trim or concretize the closing 'Communication' section — phrases like 'confidence scoring' and emoji tags are undefined boilerplate; either specify the exact output format or drop the section.

DimensionReasoningScore

Conciseness

The body is dense and free of conceptual filler (no 'what is ASO' explanations), but at ~470 lines it inlines detail that duplicates the reference files (description-structure template, before/after examples, keyword evaluation criteria) and ends with a vague 'Communication' boilerplate section. This sits between the efficient (4) and lean (5) anchors, with trimmable over-explanation.

4 / 5

Actionability

Guidance is fully executable: exact script invocations ('python keyword_analyzer.py --keywords "todo,task,planner"'), a title formula, an A/B hypothesis template, sample-size quick-reference tables, and platform character limits. Copy-paste ready commands and specific templates cover the common cases.

5 / 5

Workflow Clarity

All five workflows are clearly numbered with explicit bolded 'Validation:' checkpoints, exceeding anchor 4's 'most checkpoints present'. However, no error-recovery feedback loops are described (nothing on what to do when a validation fails, e.g. 'fix and re-validate'), which anchor 5 requires.

4 / 5

Progressive Disclosure

References are well signaled ('See:' links plus a Tools and References table), all referenced files exist, and they are one level deep (internal anchors only). But substantial detail (character-limit tables, description structure, before/after examples) lives inline in SKILL.md rather than being split into the existing references, leaving minor organization gaps per anchor 4.

4 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

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 is a model example: it states concrete capabilities in third person, gives an explicit 'Use when...' clause with natural trigger phrases covering both platforms, and occupies a distinct ASO niche. The only minor nit is that the third sentence ('Supports...') partially restates the first, adding slight redundancy without adding information.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility' plus 'keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists' — in third-person voice, with comprehensive coverage and no significant gaps, matching the top anchor.

5 / 5

Completeness

It explicitly answers 'what' ('App Store Optimization (ASO) toolkit for researching keywords...') and 'when' ('Use when the user asks about ASO, app store rankings...') with concrete trigger phrases, exactly matching the anchor-5 example pattern.

5 / 5

Trigger Term Quality

'ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, mobile app marketing on iOS or Android' comprehensively covers natural user phrasings including synonyms for both Apple and Google stores, matching the top anchor.

5 / 5

Distinctiveness Conflict Risk

A clear niche anchored to 'Apple App Store and Google Play Store' with distinct ASO-specific triggers; only negligible breadth from 'mobile app marketing', so conflict risk is minimal.

5 / 5

Total

20

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 3 suspicious

Warning

Total

14

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

marketing-skill/skills/app-store-optimization/SKILL.md

Reviewed

Table of Contents

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