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Found 19 Skills
Analyze failed GitHub Action jobs for a pull request.
Analyzes the conversation and tool usage to propose improvements to skills or store user preferences.
Search across all timelines in an Antithesis test run to find events, correlate property failures, and answer temporal questions about ordering and causation (e.g., did event A always precede failure B? do failures occur even without a preceding fault?).
Run and create integration/E2E tests by exploring the live app with the configured browser provider, preserving repository-native runners, reusing the shared test environment, and diagnosing failures from concrete artifacts.
Review and triage pull requests on GitHub and Forgejo, including Codeberg, with first-class replies inside GitHub inline review threads. Use when asked to review a PR, inspect a GitHub PR with gh, respond to code-line feedback in its existing thread, inspect a Forgejo PR with fj or its REST API, analyze CI failures, classify unresolved reviewer feedback, prepare fixes, submit an overall review, or resolve review threads.
Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.
Opt-in evidence-first causal diagnosis for bugs, browser or app/device failures, flaky behavior, and performance regressions. Activate only when the user explicitly invokes `$diagnose` or explicitly asks to use the named diagnose skill. Do not activate merely because the user mentions a bug, asks why something failed, requests debugging or a fix, or describes unexpected behavior. Once explicitly invoked, locate likely causes, instrument relevant boundaries with extensive structured logging, reproduce the issue, analyze the collected logs, rank causes with confidence scores, and pursue 100% operational confidence while probes can increase confidence.