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Classify failures in CI, automated checks run during development, to distinguish code regressions, temporary infrastructure problems, security test failures, and inconsistent results. Record each failure's category, suggested next action, and attempt count in a structured report.
Investigate failing macOS tests in Xcode or SwiftPM by running a focused test scope, classifying errors, and separating setup issues from code regressions. Get a concise explanation of likely causes and the next verification step.
Checks whether a new version of a repository preserves the behavior observed by tests on the old version. Use this skill when comparing two versions of code to detect regressions, verify refactoring safety, validate bug fixes don't break existing functionality, or ensure backward compatibility. Detects differences in function outputs, exceptions, observable states, and performance between versions. Generates reports highlighting potential regressions (critical, high, medium, low severity), improvements, and areas requiring verification. Triggers when users ask to check for regressions between versions, compare test behavior across versions, verify behavior preservation, or validate that changes don't break existing tests.
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Measure web page speed, API response times, and build or test duration. Save baseline measurements as JSON tracked in Git, then compare results before and after changes to identify performance regressions and assess launch targets.
Investigate unclear failures, intermittent behavior, and performance regressions before changing code. Separate symptoms from assumptions, trace how failures arise, and rank possible causes by evidence and ease of testing, then report the cause and supporting proof.
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.
Check a codebase for known dependency vulnerabilities, hard-coded secrets, and risky code patterns using Ruflo security tools. Choose a scan depth, inspect matching lines, and report verified concerns with a clear account of what was checked.
Track documented code fixes across releases with a signed manifest, a list of fixes and their identifying text. Verify that markers remain in source files, inspect file changes, and use recorded history to identify commits associated with regressions.
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Investigate SGLang serving failures by collecting server state, preserving failing requests, and replaying them on a clean target. Summarize evidence and likely causes, then choose the next debugging tool for crashes, stalls, or performance regressions.
Fix defects in Composio SDK through focused reproduction, root-cause analysis, regression tests that check for recurrence, and targeted verification. Use it for reported bugs, failing tests, CI regressions, runtime errors, or incorrect SDK behavior.
Re-review a pull request after new commits arrive. Check every previous finding, inspect the new changes for regressions, and review the full current change. Run relevant verification and publish a test report and review verdict within Factory.
Trace failing cmux Swift package tests to the commits that introduced them by running older revisions in CI, the automated testing environment. It helps distinguish outdated expectations, code regressions, and intermittent failures, then guides focused fixes and verification.
Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.
Triage incorrect MNN outputs and regressions by matching symptoms to focused debugging guides. Use backend comparisons, minimal reproductions, and controlled experiments to investigate memory issues, quantization, concurrency, numeric limits, caches, and inconsistent results across runs.
Review a branch, pull request, or uncommitted changes for interface problems involving accessibility, layout, text, typography, and color. The report distinguishes newly introduced issues, regressions, and existing problems, including effects on screens that use changed components.
Compares code relationships before and after a branch, pull request, or release change using Trailmark. Flags structural security regressions, such as removed authorization checks or newly reachable sensitive operations, and provides evidence for reviewers.
Validate security patches by comparing vulnerable and patched code against exploit tests, related attack variants, and normal behavior. Produce reproducible logs showing remaining flaws, regressions, newly introduced security failures, and gaps that still need testing.
Improve code through repeated changes, checks, and decisions to keep or discard each result. Start with a goal or measurable target, limit iteration counts, and review logs for progress, stalled improvements, or regressions.
Diagnose difficult bugs and performance regressions by first building a repeatable check for the exact symptom. Minimize the reproduction, test ranked hypotheses, apply a fix, and verify the original scenario with regression tests where suitable.
Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.
Improve R code used in empirical economics by finding slow operations, reducing memory use, and optimizing data processing and regressions. It covers large panel datasets and repeated calculations, with checks that optimized code preserves the original results.
Find STATA code examples for empirical accounting research from published replication files. Adapt regression, matching, event study, and table export syntax to your variables, with citations to the papers behind each example.