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Found 27 Skills
Analyze MSBuild binary logs to diagnose build failures by replaying binlogs to searchable text logs. Only activate in MSBuild/.NET build context. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, investigating common errors like CS0246 (type not found), MSB4019 (imported project not found), NU1605 (package downgrade), MSB3277 (version conflicts), and ResolveProjectReferences failures. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), build performance analysis (use build-perf-diagnostics), non-MSBuild build systems. INVOKES: dotnet msbuild binlog replay, grep, cat, head, tail for log analysis.
Generate MSBuild binary logs (binlogs) for build diagnostics and analysis. Only activate in MSBuild/.NET build context. USE FOR: adding /bl:{} to any dotnet build, test, pack, publish, or restore command to capture a full build execution trace, prerequisite for binlog-failure-analysis and build-perf-diagnostics skills, enabling post-build investigation of errors or performance. Requires MSBuild 17.8+ / .NET 8 SDK+ for {} placeholder; PowerShell needs -bl:{{}}. DO NOT USE FOR: non-MSBuild build systems (npm, Maven, CMake), analyzing an existing binlog (use binlog-failure-analysis instead). INVOKES: shell commands (dotnet build /bl:{}).
Diagnose dotnet-msbuild binlog MCP startup failures. USE FOR: CONNECTION_CLOSED, binlog server won't connect, missing binlog tools, Microsoft.AITools.BinlogMcp not found, dnx launch errors. Checks workspace SDK, feeds, and source mapping without bypassing policy. DO NOT USE FOR: errors inside a binlog (use binlog-failure-analysis), capturing binlogs (use binlog-generation), unrelated MCP servers.
DevOps workflows for Playwright - CI failure analysis, workflow debugging, and release operations.
Configure Harness AI-powered operations (AIDA) via MCP. Set up predictive failure analysis with ML models for memory leaks, disk exhaustion, connection pool saturation, and latency degradation. Configure intelligent alert correlation and noise reduction to reduce alert volume. Use when asked to set up predictive failure analysis, configure AI-powered alerting, reduce alert noise, or enable ML-based anomaly detection. Do NOT use for pipeline debugging (use debug-pipeline instead) or SLO management (use manage-slos instead). Trigger phrases: AIDA, predictive failure, alert correlation, noise reduction, anomaly detection, AI ops, predictive analysis, alert fatigue, ML alerting, intelligent alerting.
Analyzes DevOps Center test failures and Code Analyzer violations in plain language — failure category, offending file/class/method/line, rule violated, fix direction, and prioritized improvement suggestions (test-code vs production-code) — then optionally creates a tracked fix WorkItem on explicit request. Analysis is pure reasoning; work-item creation is a confirmation-gated write. Use this skill to explain failures or improvement suggestions, translate Code Analyzer violations, or track a fix as a work item. TRIGGER when: a run failed and the user wants root cause; a quality gate failure needs explaining; violations need translating; the user shares a failure payload and asks how to address it; wants to strengthen tests; or wants to create a fix work item, log a remediation, or assign a failure. DO NOT TRIGGER when: the user wants fix code written (use platform-apex-generate) or new test classes authored (use platform-apex-test-generate).
Executes the project's test suite, analyzes failures, and provides fix recommendations.
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.
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.
Analyze failed GitHub Action jobs for a pull request.
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.
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.