Use the selected reliable GPU executor from the legacy Workflow.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Record research Directions and Experiments, query saved evidence, and submit a validated Episode Report through the Supervisor.
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 Long Horizon Episode.
Framework Baseline has its own smoke-and-exit procedure, not an Episode report.
All Episode outcomes require valid report fields and no in-progress Directions.
New Long Horizon Episodes use Runtime Journal. Do not call legacy append/finalize, manage Git,
or write control handoffs. Leave the selected measured source in kernel.py; the Supervisor
creates the commit and decides acceptance independently.
gateway_record_ids; separate measured
evidence from your analysis. Measurements are automatically saved; interpretation is not.episode-report.
Correct validation errors and resubmit. An accepted report is a handoff, not promotion.The link is Direction → Experiment → Gateway Record → exact Kernel. Propose as many useful
nonduplicate ideas as needed; start at most three distinct Directions per Episode and only one at
a time. List exports are snapshots under scratch/; rerun list to refresh. Current and finalized
earlier Runtime Journals are visible through the tools, including unsuccessful explorations.
Legacy Journals are not automatically converted into Direction/Experiment IDs.
skills/gpu-measurement/SKILL.md; for knowledge queries use
skills/KernelWiki/SKILL.md.Never invent IDs or measured results. Read an existing result instead of repeating an identical task. If a transport failure leaves a mutation's outcome unknown, inspect records before retrying. Identical accepted reports can be resubmitted to repair handoff publication; different terminal reports and further Journal mutations are rejected.
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.