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matrixone
matrixone contiene 5 skills recopiladas de matrixorigin, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
MatrixOne database kernel development - deterministic CGo build/test setup, complete test matrices, hung-test diagnosis, GPU builds (MO_CL_CUDA=1 / cuVS), operator lifecycle contracts (Call/Reset), pipeline protocol, and the vector/fulltext index-plugin framework. Use when modifying kernel packages, running CGo-transitive tests, diagnosing compile/link/load/vendor failures or silent hangs, changing colexec/process/compile pipelines, or adding/editing index algorithms.
Pre-push self-review gate for MatrixOne changes — systematic first-principles review of the complete diff, including functional closure, unhappy paths, state/ownership models, wait-for dependencies, restart/reuse generations, and derived test matrices. Use before push/PR updates, when concurrency or lifecycle code changes, or when repeated review rounds reveal missed closure edges.
Full-codebase MatrixOne unhappy-path audit for leak, double cleanup, hung, and OOM risks using the Q1-Q3 framework plus ownership and wait-for graphs. Use for resource lifecycle, cancellation/close/fail-fast paths, goroutines, locks/channels/RPC waits, restart/reuse generations, or unbounded growth audits.
MatrixOne database kernel development - deterministic CGo build/test setup, complete test matrices, hung-test diagnosis, GPU builds (MO_CL_CUDA=1 / cuVS), operator lifecycle contracts (Call/Reset), pipeline protocol, and the vector/fulltext index-plugin framework. Use when modifying kernel packages, running CGo-transitive tests, diagnosing compile/link/load/vendor failures or silent hangs, changing colexec/process/compile pipelines, or adding/editing index algorithms.
Systematic MatrixOne kind/bug lifecycle triage — intake normalization with `needs-triage`, conservative promotion to active severity (`severity/s-1`, `severity/s0`, `severity/s1`), immediate downgrade to `deferred` with explanatory comments, optional five-level AI effort labeling (`ai-easy`, `ai-light`, `ai-medium`, `ai-heavy`, `ai-manual`) only when explicitly requested for issues/PRs, batched GitHub metadata updates, rollback-safe processing, and drift prevention. Use when asked to triage, classify, review, downgrade, defer, label, AI-effort-assess, or bulk-update MO bugs and related PRs.