From diagnostics-report-analyzer
Analyzes AvdLee Diagnostics HTML reports for customer support and debugging. Inspects embedded JSON, session logs, crash diagnostics, and app/system metadata.
How this skill is triggered — by the user, by Claude, or both
Slash command
/diagnostics-report-analyzer:diagnostics-report-analyzer-skillThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this skill to analyze single-file reports generated by the AvdLee Diagnostics framework. These reports are usually emailed as `.html` attachments and may contain either structured JSON plus a browser renderer or older pre-rendered HTML only.
Use this skill to analyze single-file reports generated by the AvdLee Diagnostics framework. These reports are usually emailed as .html attachments and may contain either structured JSON plus a browser renderer or older pre-rendered HTML only.
Treat the report as diagnostic evidence, not as a webpage to summarize. Newer reports are intentionally designed for agents: they contain a structured JSON payload that is more precise and token-efficient than the rendered HTML.
If the report contains this element, extract and parse its text content:
<script id="diagnostics-report-data" type="application/json">
Use that JSON as the source of truth. Ignore CSS, JavaScript, navigation markup, and rendered HTML unless a chapter explicitly uses legacyHTML.
Expected top-level fields:
schemaVersion: report schema version.title: human-readable report title.agentHints: producer-provided analysis hints.chapters: report sections.Expected chapter shapes:
showTitle: browser rendering hint; does not affect analysis.kind: "logs" with data.sessions.kind: "table" with data.rows.kind: "text" or kind: "preformatted" with data.value.kind: "legacyHTML" for custom diagnostics or fallback content.legacyHTML: optional display HTML for chapters that need custom browser rendering. Prefer data for analysis unless data.type is legacyHTML or the structured fields do not answer the question.If the JSON script tag is absent, parse the HTML report directly:
#log-sessions..collapsible-session details.<summary><div class="session-header">.....debug, .error, .system, and .crash.system lines such as SYSTEM: *** Terminating app due to uncaught exception... or SYSTEM: *** First throw call stack:.<pre> blocks.Keep fallback parsing focused. Do not load or restate the entire HTML file when a small section answers the question.
Analyze in this order:
agentHints if present.crash events, then error events, MetricKit diagnostics, and unusual system logs.crash event, treat crash-like system lines as legacy crash evidence, but prefer structured crash events when both exist.Unless the user asks for a different format, respond with:
## Summary
One or two sentences describing what most likely happened.
## Evidence
- Specific session, timestamp, event level, metadata row, or chapter detail that supports the conclusion.
- Another relevant detail if needed.
## Recommended Next Steps
- Concrete debugging or support action.
- Code/config area to inspect if apparent.
Lead with the most likely explanation. If the evidence is inconclusive, say so and list the missing signal that would disambiguate it.
Diagnostics reports can be large. Stay selective:
Newer Diagnostics reports may include structured sessions and legacy sessions in the same payload because users can update an app after historic logs have already been persisted on device. Treat both as valid evidence.
Legacy HTML chapters may contain trusted report markup created by the Diagnostics framework. Use them when structured data is unavailable, but keep the JSON payload as the preferred machine-readable interface whenever present.
npx claudepluginhub avdlee/diagnostics --plugin diagnostics-report-analyzerParses error logs, stack traces, and crash reports into a structured root cause diagnosis with fix suggestions and debugging steps.
Analyzes sosreport archives for error patterns, kernel panics, OOM events, service failures, and crashes in journald logs and traditional system/application files to identify root causes.
Detects and diagnoses application failures by analyzing error logs, stack traces, and system state. Useful when debugging crashes or production incidents.