Use the selected reliable GPU executor from the legacy Workflow.
日本語の概要は準備中です。原文の説明を表示しています。
Record research Directions and Experiments, query saved evidence, and submit a validated Episode Report through the Supervisor.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
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ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Use the selected reliable GPU executor from the legacy Workflow.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。