crash-analytics
by EronredThe crash-analytics skill helps you triage, prioritize, and reduce app crashes using Crashlytics, App Store Connect, and Xcode Organizer. Use it to identify which crash to fix first, interpret crash-free sessions, and assess how crash rate affects retention, ratings, and App Store performance. Ideal for crash-analytics for Data Analysis and release triage.
This skill scores 74/100, which means it is listable for directory users but best framed as a useful, somewhat limited operational guide rather than a fully packaged workflow. The repo gives a clear crash-triage trigger, concrete crash tools, and a substantive body of guidance, but it lacks support files and install-time scaffolding that would reduce adoption guesswork further.
- Strong triggerability: the frontmatter explicitly covers crash, Crashlytics, ANR, crash-free sessions/users, symbolication, and crash reports.
- Substantive workflow content: the body includes crash-rate targets, tool comparisons, and guidance for triage/prioritization rather than generic advice.
- Good install decision value: it ties crash analytics to ASO outcomes like ranking, featuring, ratings, and retention, which helps users judge relevance quickly.
- No support files or scripts: the repo has no references, resources, rules, or automation, so agents must rely on the markdown alone.
- Limited operational packaging: no install command and no visible companion assets, which may slow setup or make cross-skill integration less obvious.
Overview of crash-analytics skill
The crash-analytics skill helps you diagnose, prioritize, and reduce app crashes with a focus on the decisions that affect shipping, retention, and App Store performance. It is best for teams that already have crash data and need a clearer path from noisy reports to fixes, especially when Crashlytics, App Store Connect, or Xcode Organizer are part of the workflow.
What crash-analytics is for
Use the crash-analytics skill when you need to answer practical questions like: which crash should be fixed first, whether a spike is real or release-specific, how to interpret crash-free sessions, or how crash rate may affect discoverability and reviews. It is especially useful for crash-analytics for Data Analysis when the goal is not just logging crashes, but turning crash telemetry into triage decisions.
What makes it different
The skill is not a generic monitoring prompt. It centers on crash triage, ranking impact, and the operational difference between crashes, ANRs/hangs, and symbolication quality. That makes it better for teams that need actionability, not just a definition of error logs.
Best-fit users and scenarios
This crash-analytics skill fits mobile developers, QA leads, ASO-focused operators, and product teams that want a fast read on app stability. It is a strong fit if you are working on iOS apps, Firebase Crashlytics setups, or release triage after a bad build.
How to Use crash-analytics skill
Install the skill and inspect the source
For crash-analytics install, add the skill from the repo and then read the skill file first:
npx skills add Eronred/aso-skills --skill crash-analytics
Start with skills/crash-analytics/SKILL.md. In this repo, that file is the main source of truth; there are no extra scripts, rules, or helper resources to consult.
Give the skill a concrete crash problem
Best results come from asking it to solve a specific workflow, not from asking it to “analyze crashes” in the abstract. Include the platform, the release window, the crash source, and the business question.
Good prompt shape:
- app platform: iOS or Android
- tool source: Crashlytics, App Store Connect, Xcode Organizer, MetricKit
- symptom: spike, single stack trace, launch crash, hang, or ANR
- scope: version, build number, device family, OS version
- goal: prioritize fix, explain trend, draft triage steps, or assess ASO risk
Example:
“Use crash-analytics to triage a Crashlytics spike in iOS 17.4 after release 3.8.1. Tell me whether this is likely a regression, which stack trace to fix first, and what data I should collect before filing the bug.”
Read the output in the right order
The most useful crash-analytics usage is to move from symptom to decision:
- Confirm the crash is real and scoped to a release or device group.
- Check whether the top stack trace is symbolicated and stable enough to trust.
- Identify the smallest fix that reduces the largest crash volume.
- Validate whether the issue affects first-session retention or App Store risk.
Improve the input before asking for analysis
If you only provide “our app crashes,” the skill has to guess too much. Stronger inputs include a stack trace, top crashing versions, crash-free session rate, recent release notes, and any device or OS clustering. For crash-analytics usage, this extra context usually matters more than a longer prompt.
crash-analytics skill FAQ
Is crash-analytics only for Firebase Crashlytics?
No. Crashlytics is a common input, but the skill also fits App Store Connect crash reports, Xcode Organizer logs, and MetricKit-derived stability data. Use whichever source you have; the skill is most valuable when it helps you compare and prioritize, not just read one tool.
Do I need advanced debugging knowledge?
No, but you do need enough context to describe the crash clearly. Beginners can use the crash-analytics guide effectively if they can share the app platform, a rough crash pattern, and the release that changed behavior. Without that, the analysis will be less decisive.
When should I not use this skill?
Do not use it for broad product analytics, funnel analysis, or feature adoption questions unless the crash problem is part of the issue. For general analytics setup, a broader app-analytics skill is a better fit.
How is it different from a generic prompt?
A generic prompt can summarize crash reports, but the crash-analytics skill is aimed at triage quality: what to fix first, how to interpret noisy telemetry, and what stability signals matter for App Store outcomes. That framing reduces wasted debugging time.
How to Improve crash-analytics skill
Provide the minimum evidence that changes the decision
The biggest quality jump comes from adding the data that separates a real regression from background noise. Include crash-free sessions, affected app version, top device or OS, and whether the issue started after a specific release. If you have a stack trace, symbolicated logs are far better than raw crash text.
Ask for a triage output, not just an explanation
The crash-analytics skill performs best when you ask for an action plan: likely root cause, severity, user impact, and next checks. That leads to better crash-analytics usage than asking for a generic summary of crash data.
Reduce ambiguity in the crash report
If your report mixes launch crashes, hangs, and ANRs, separate them before prompting. If you do not know the exact cause, say so and ask the skill to rank the most plausible causes from the evidence you do have. Clear boundaries produce better prioritization.
Iterate after the first pass
Use the first answer to narrow the problem, then follow up with one focused question: “Which stack trace is worth fixing first?” or “What extra data would confirm this is a release regression?” That second pass usually improves the analysis more than re-running the same prompt.