Use the selected reliable GPU executor from the legacy Workflow.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Query saved measurement evidence and submit an Episode Report without Direction or Experiment recording.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use python3 tools/sandbox.py inside a controller-registered Episode. Direction and research
Experiment recording are disabled; do not use update-direction or record-experiment.
This does not disable automatic measurement persistence, duplicate detection, correctness checks,
or independent ABBA promotion. Framework Baseline has its own smoke-and-exit procedure.
Inspect saved measurement evidence and optional canonical memory/v*.json summaries when present.
Implement and measure a candidate, then leave its exact source in place (kernel.py or the
contracted source tree from WORKSPACE.md) and cite real Gateway Record IDs in episode-report.
No synthetic Direction, Experiment, separate plan/profile document, or Git operation is required.
skills/gpu-measurement/SKILL.md for new measurements.Read-only Direction/Experiment history queries remain available for earlier recorded Episodes; they do not create records or change the current policy. Never invent IDs or measured results. Reuse saved results rather than repeating identical tasks. Accepted reports can be replayed to repair publication, but different terminal reports are rejected. Report acceptance is not promotion.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Use the selected reliable GPU executor from the legacy Workflow.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prepare requests for the selected AKA Execution entry point, consult Gateway or SSH guidance, and preserve complete execution output and artifacts. Use for handing prepared GPU commands, evaluation, or profiling work to AKA Execution; experiment planning and result interpretation belong to their callers.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Let AKA autonomously add, run, inspect, and revise intra-kernel timeline probes for standalone CUDA/inline PTX or CuTe DSL when ordinary benchmark, NSYS, or NCU evidence cannot answer a specific kernel-internal timing question.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate a structured implementation plan from an evidence draft. Validate paths, obtain configured independent Codex and Qoder reviews, synthesize available advice against repository evidence, preserve the draft, and produce testable acceptance criteria and validation steps.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Learn the target framework from enabled knowledge tools and implement a baseline GPU kernel. Use this skill to understand compute semantics, determine the target platform and framework, search reference implementations, and produce a correct V0 baseline with performance records for later profile-driven optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run the evidence loop of one long-horizon GPU kernel optimization episode. Use this skill to reconstruct the incumbent, profile and localize a bottleneck, research progressively, plan one coherent direction, implement and repair, validate development correctness and performance, and record every decisive experiment in the episode journal.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.