asc-metrics

When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.

Quality

83%

Does it follow best practices?

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Quality

Content

71%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.

A token-efficient, well-structured analytics skill with genuinely valuable domain benchmarks and analysis heuristics, but its core fetch steps are not executable as written and all detail is inlined with only one out-of-bundle reference. Adding an executable invocation example and an in-bundle API reference file would close the main gaps.

Suggestions

Show one concrete executable request (e.g., a curl example or the exact tool call with base URL and auth) for /v1/connect/metrics so the fetch steps are copy-paste runnable instead of ```bash GET /path``` pseudocode.

Move the detailed endpoint/response documentation into a reference file inside the skill bundle (e.g., references/appeeky-connect.md) and keep only the three core endpoints inline, so the single reference no longer points outside the skill directory ('../../tools/integrations/appeeky-connect.md').

Add a short error-handling step to the Fetching Data workflow (what to do on API error, empty response, or a date range beyond the 90-day history limit mentioned in Prerequisites).

Dimension	Reasoning	Score
Conciseness	Lean throughout: no concept explanations, tight tables ('ARPD \| Revenue / Downloads \| > $0.05 good'), and dense domain-specific insight ('Trials rising but conversions flat → paywall or onboarding issue') that Claude could not infer. Every section earns its tokens.	5 / 5
Actionability	Endpoints and parameters are specific ('GET /v1/connect/metrics/apps/:appId?from=YYYY-MM-DD&to=YYYY-MM-DD') but are presented in ```bash blocks without base URL, auth, or invocation mechanism — pseudocode rather than executable commands. The one pointer to full detail ('See full API reference: appeeky-connect.md') resolves outside the skill's own directory.	3 / 5
Workflow Clarity	Clear sequence: Initial Assessment (context file + three questions) → list apps → match app_apple_id → fetch overview/detail → analyze → format output, with a precondition check ('If ASC is not connected, prompt the user… and return'). Minor gap: no handling of API errors or empty/partial data windows; read-only analytics so destructive-operation validation caps do not apply.	4 / 5
Progressive Disclosure	Sections are well organized, but the ~157-line body is monolithic: no in-bundle reference files exist (no references/, scripts/, assets/), and the single external reference points two levels up ('../../tools/integrations/appeeky-connect.md') outside the skill directory. The endpoint specs and output templates arguably belong in or alongside that reference rather than inline.	3 / 5
	Total	15 / 20 Passed

Description

95%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.

A strong description: third-person, specific about the data domain, with an explicit 'Use when…' trigger clause rich in natural user phrasings, and explicit routing to sibling skills that keeps conflict risk minimal. The only soft spot is that capability verbs reduce to 'analyze'/'compare' while the enumerated items are data types rather than distinct actions.

Dimension	Reasoning	Score
Specificity	Names the domain precisely and enumerates concrete data capabilities — 'real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect' plus 'compare periods or top markets'. The verbs are mostly a single 'analyze', so it sits just below anchor 5's multiple distinct concrete actions.	4 / 5
Completeness	Explicitly answers both what ('analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns') and when ('Use when the user asks about…') with concrete trigger phrases, matching the anchor-5 example structure.	5 / 5
Trigger Term Quality	Comprehensive natural-phrase coverage including synonyms and the abbreviation: 'my downloads', 'my revenue', 'how is my app performing', 'ASC data', 'sales and trends', 'my subscription numbers', 'App Store Connect metrics', 'compare periods', 'top markets'. These are exactly the phrases a user would say.	5 / 5
Distinctiveness Conflict Risk	Clear niche (first-party ASC data via Appeeky Connect) with explicit boundary routing — 'For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy' — which minimizes overlap with adjacent skills.	5 / 5
	Total	19 / 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
relative_links	Relative link issues: 1 suspicious	Warning

	Total	15 / 16 Passed

Repository: Eronred/aso-skills
Path: skills/asc-metrics/SKILL.md
Commit: 8de1ee1

Reviewed: 5 days ago

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