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Found 11 Skills
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".
Audit a whole regression suite and prune/restructure it with evidence: per-test coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human sign-off are mandatory. Use when: "audit the test suite," "prune redundant tests," "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." Not for: Judging whether an individual test is WELL-WRITTEN (smells, assertions) — that is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery. Related: ai-qa-review, coverage-analysis, test-reliability, risk-based-testing, qa-project-context.
Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues. Use when training, evaluating, exporting, or running inference for a TAO Optical Inspection model on AOI / quality-control data. Trigger phrases include "train optical inspection", "AOI defect detection", "Siamese defect classifier", "PCB / manufacturing inspection".
Review a Ralph plan and bd graph for defects via a subagent.
Provide structured code review guidance for catching defects and improving quality. This skill should be used when the user asks to 'review this code', 'check for issues', 'PR review', 'code quality check', or wants systematic code evaluation. Keywords: code review, PR, pull request, quality, defects, security, maintainability, performance.
Judge one bounded code candidate for defects, broad review, or read-only behavior parity for one stateful refactor or rewrite. Focus on exact identities, credible failure mechanisms, adversarial validation, maintainability evidence, and an evidence-backed result.
Choose the smallest valuable automated test evidence and prove it can detect the defect it guards. TRIGGER when: behavior-changing work needs a test decision; adding, changing, deleting, or reviewing tests; a test may be vacuous. SKIP when: only running existing checks or completing live post-ship verification (use test-completed-plan).
Review a change for evidenced defects and risks without editing it.
Review a code change, diff, commit, branch, or pull request for actionable defects, regressions, security risks, compatibility breaks, and missing verification. Use when code review is the primary requested outcome. Keep the review read-only unless the user explicitly asks to implement fixes; do not use as a substitute for implementation or a specialized visual audit.
Review every change on this branch against main across 9 perspectives, then review-deep's scripted collate, verify, and report chain over the same findings. Covers committed, staged, unstaged, and untracked work.
Review an existing code change set for defects, regressions, risks, and maintainability issues. Use for open-ended review requests about a diff, branch, commit, pull request, or uncommitted changes. Do not use for running tests or other checks, verifying named requirements or claims, or diagnosing a specific known failure.