Test design, vulnerability assessment, and quality assurance (13.1K skills)
Run Isaac for Healthcare workflows in a simulator using a learned policy or rule-based controller. Verify completed episodes through the final success summary, visible behavior, and recorded data, with checkpoint compatibility checks when a checkpoint is supplied.
Guide checks of a customized Jetson system image, from file consistency to tests on a flashed device over SSH or UART. Device access is defined, while the broader validation procedure remains a skeleton.
Add and run unit or functional tests for Megatron-LM. The guide explains test organization, YAML execution recipes, reference results for numerical checks, test filters, temporary disabling, and reproducing continuous integration failures locally.
Measure video encoding or decoding throughput on Jetson, compare presets, and test concurrent worker capacity. When representative media is unavailable, produce a clearly labeled estimate from SDK documentation, keeping estimates separate from measured results.
Run quality assurance tests on Holoscan Sensor Bridge hardware from a supplied test document. Select tests that match the connected equipment, execute automatable cases, evaluate pass or fail, and produce a structured results report.
Build and run a benchmark for RDMA WRITE, a direct memory transfer initiated by a CUDA kernel. Check GPU and network adapter pairing, interpret timing columns, and assess median latency, p99, and timing variation.
Capture and decode execution traces from BlueField DPA workloads to inspect kernel events, synchronization, and communication timing. Choose capture modes, adjust file limits and recording overhead, and diagnose empty traces or decoding failures.
Measure DOCA Flow rule update and disable rates when work runs on the device's DPA processor. Configure workloads, interpret repeated measurements in thousands of operations per second, and investigate zero-operation results or failed self-tests.
Deploy and operate DOCA Argus Service on BlueField to monitor suspicious activity. This skill guides detection settings, forwarding findings to a security monitoring system, and troubleshooting missing alerts, excessive noise, or performance impact.
Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.
Check one DICOM medical imaging series before conversion or AI inference by inspecting headers only. Produce a JSON report with file inventory, image orientation, personal information flags, findings, and a pass, warn, or fail verdict.
Check a live app or site before launch for exposed secrets, database access risks, mobile layout, search visibility, and speed. The skill produces scores, a launch verdict, and a prioritized list of fixes.
Audit saved agent sessions for access to private files and evidence that secret values entered an agent's context. Get findings without displaying secrets, identify keys needing rotation, and inspect Claude Code or Codex sessions in a local report.
Audit a completed AI development phase to check whether its planned evaluation strategy was implemented. Rate coverage for each evaluation dimension and produce an EVAL-REVIEW.md report with scores, gaps, a verdict, and a remediation plan.
Review acceptance tests and verification records across development phases to find unfinished checks. Compare records with the code to identify outdated documentation, then produce a prioritized plan for tests that people need to perform.
Add unit and end-to-end browser tests to a completed development phase using its acceptance criteria, planning documents, and implementation. Review the proposed coverage, verify tests fail and then pass, and commit the generated test files.
Run an audit, separate automatically fixable findings from issues requiring manual work, and carry eligible fixes through testing and individual commits. Limit processing by severity or count, or preview classifications without changing files.
Test text watermark removal tools using samples marked with a known secret key. Measure whether the mark survives, alongside changes to meaning, facts, wording, and length, and compare recorded runs in a shared table.
Run CIAgent regression checks after changing an agent’s code, prompts, or knowledge base. Interpret failures and repeated runs to distinguish incorrect answers, inconsistent model judgments, and setup problems before committing changes.
Test local web applications with Python and Playwright, a browser automation library. Verify interface behavior, investigate problems, capture screenshots, and collect browser logs, with a helper script that manages one or more application servers.
Guide software testing with a test-first workflow, from small unit checks to complete application flows. Apply consistent test structure across supported frameworks, keep tests independent, and use meaningful checks when measuring coverage.
Set up CIAgent tests that check whether changes affect an AI agent’s behavior. Create a runner, record reference answers, add factual checks, and generate reports that distinguish agent variability from unstable evaluations.
Analyze audio, images, video, and text for AI generation or manipulation with Resemble AI. Receive detection scores with explanations, trace synthetic audio sources, apply or detect invisible watermarks, and compare speakers against registered voice profiles.
Check an agent’s test claims against local Claude Code transcripts and the git working tree. Nine rule-based checks identify gaps such as missing runs or failed tests, with timestamps and quoted evidence, without calling a model.