Name: app-store-optimization
Rating: 65.2 (1 reviews)
Author: administrakt0r

app-store-optimization

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

1.22x

Quality

48%

Does it follow best practices?

Impact

94%

1.22x

Average score across 3 eval scenarios

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/app-store-optimization/SKILL.md

Quality

Content

36%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 content provides useful concrete reference data (JSON I/O formats, platform character limits) but is undermined by a large section describing scripts that do not exist, no sequenced workflows with validation checkpoints, and a monolithic structure with no actual bundle files. It reads more as a padded capability brochure than lean, executable guidance.

Suggestions

Remove or actually provide the referenced Python scripts — currently ~80 lines describe eight scripts with function signatures that have no backing files, which both pads the document and promises unactionable tooling.

Replace the 'How to Use' example-prompt blocks with a numbered workflow per capability that includes validation checkpoints (e.g. after metadata edits, validate character limits before submission).

Move the Best Practices and detailed script documentation into separate reference files (e.g. references/best-practices.md) and keep SKILL.md as a lean overview, which would improve both conciseness and progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body is noticeably verbose: roughly 80 lines enumerate eight scripts (keyword_analyzer.py, metadata_optimizer.py, etc.) with fictional function signatures, and the 'Capabilities' section duplicates the platform-specific detail given later — clearly padded sections that could be trimmed.	2 / 5
Actionability	Concrete JSON input/output schemas and exact platform character limits are genuinely actionable, but the entire 'Scripts' execution layer it points to does not exist (no scripts/ directory), leaving the promised executable guidance incomplete.	3 / 5
Workflow Clarity	The body is a capability catalog with example natural-language prompts rather than a sequenced workflow; there are no numbered steps and no validation/checkpoint steps for the metadata changes it directs, which the rubric treats as a validation gap.	2 / 5
Progressive Disclosure	Section headers give the 400-line monolith some structure, but no bundle files exist (references/, scripts/, assets/ are absent) and the detailed script docs and best-practice lists that belong in separate files are inlined, so organization is only partial.	3 / 5
	Total	10 / 20 Passed

Description

61%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 clearly identifies a distinct, well-named ASO niche with good natural trigger terms, but it stops at a clear 'what' without any explicit 'when to use' guidance, capping completeness. Specificity is adequate but stays at category-level actions rather than concrete capabilities.

Suggestions

Add an explicit 'Use when...' clause naming concrete triggers, e.g. 'Use when launching or optimizing a mobile app's Apple App Store or Google Play listing, tracking keyword rankings, or analyzing review sentiment.'

Replace the broad verbs ('researching, optimizing, and tracking') with 3-4 concrete capabilities users would name (e.g. keyword research, metadata optimization, competitor analysis, ASO scoring) to lift specificity.

Dimension	Reasoning	Score
Specificity	Names the ASO domain plus three broad action verbs ('researching, optimizing, and tracking mobile app performance'), matching the 'names domain and 1-2 concrete actions but not comprehensive' anchor; the actions are category-level rather than the granular list (keyword research, metadata, reviews) the body later reveals.	3 / 5
Completeness	Has a clear 'what' but no 'Use when...' clause or equivalent explicit trigger guidance, which the rubric caps at 3; the 'when' is only weakly implied by the domain framing.	3 / 5
Trigger Term Quality	Includes strong natural terms users actually say — 'App Store Optimization (ASO)', 'Apple App Store', 'Google Play Store' — with the recognized acronym; a few natural synonyms (e.g. 'app ranking', 'store listing') are missing, so it sits just below comprehensive.	4 / 5
Distinctiveness Conflict Risk	The 'App Store Optimization (ASO)' niche plus platform names ('Apple App Store', 'Google Play Store') make it mostly distinct from general SEO/marketing skills, with only minor overlap risk.	4 / 5
	Total	14 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/app-store-optimization/SKILL.md
Commit: 900c504

Reviewed: 7 hours ago

Table of Contents

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