29 件 ・ 関連度順
概要と使いどころ
Create consistent nn_cfd W&B experiment comparison reports from selected run IDs using wandb and wandb_workspaces. Use this whenever the user asks to report experiment results, create a W&B Report, compare CFD training/eval runs, document baseline-vs-variant outcomes, reproduce the project experiment report template, or publish a leaderboard-style report for milieu/nn_cfd.
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。
Comprehensive primary skill for agents working with Weights & Biases. Covers both the W&B SDK (training runs, metrics, artifacts, sweeps) and the Weave SDK (GenAI traces, evaluations, scorers). Includes helper libraries, gotcha tables, and data analysis patterns. Use this skill whenever the user asks about W&B runs, Weave traces, evaluations, training metrics, loss curves, model comparisons, or any Weights & Biases data — even if they don't say "W&B" explicitly.
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。
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
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。
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
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。
Periodically check WandB metrics during training to catch problems early (NaN, loss divergence, idle GPUs). Avoids wasting GPU hours on broken runs. Use when training is running and you want automated health checks.
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。
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
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。
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
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。
Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability. Triggers: "Weights and Biases", "W&B", "WeightsAndBiases", "ML experiment tracking", "model registry", "experiment management", "wandb".
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。
Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.
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。
Belastbaren Produktmarketing-Kontext für Positionierung, Zielgruppen, Botschaften, Einwände und Nachweise erstellen, prüfen und aktualisieren. Nutze diesen Skill, wenn die gemeinsame Grundlage fehlt, auf die sich Texte, Content, Vergleiche und Kampagnen stützen sollen: ICP, Jobs-to-be-Done, Nutzenversprechen, Differenzierung, Preislogik, Einwandbehandlung und Belegklassen. Legt das Ergebnis als `.agents/product-marketing.md` im Projekt ab und kennzeichnet, was belegt, was Anbieterangabe und was Annahme ist. Ersetzt weder Kundenforschung noch Rechtsprüfung.
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。
Review one terminal experiment and choose the next step: merge a reproducible winner, close a useful negative or dead end, or request missing evidence.
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。
Choose, configure, launch, and collect bounded subagents for research and engineering decisions. Root agents and delegation-capable subagents should read this before delegating: every task requires an explicit model tier, and high-leverage work such as research ideation, round planning, plateau pivots, large research reviews, hard optimization, disputed evidence, and expensive experiment portfolios requires frontier judgment.
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。
Open GitHub Issues for human input and respond to the researcher team. Use this skill whenever you need to handle a human_issue event, respond to human issues, ask humans a question, or check team communications. Also triggers for: "any human messages?", "check issues", "respond to humans".
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。
Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. This is faster and more reliable than trying to read a raw PDF.
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。
Search the general web or scholarly publications through Exa. Use for current public information, official documentation, source code, release notes, papers, preprints, journals, or literature research.
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。
Create a typed assignment branch and draft PR for one student. Use when the advisor has a concrete hypothesis and the student has no open `status:wip` or `status:review` assignment.
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。
Maintain and publish the advisor-owned current research state, durable research-ideas syntheses, and dataset analysis. Use when evidence or human guidance changes the research focus or direction, durable research ideas should be recorded, or dataset understanding changes.
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。
Audit recent tool use across local Senpai OpenHands root and recursively delegated conversations without exposing tool arguments. Use for provider comparisons, tool-loop diagnosis, status-poll analysis, error rates, and latency reports.
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。
Operator-side guide for generating a target-specific training curve chart for a legacy experiment PR. Use when auditing or presenting historical runs.
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。
Operator-side inventory of experiment PRs for auditing a Senpai research track. It outputs merged winners, a compact results table, and full details for deep dives.
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。
Coach the user through a rigorous, repository-grounded interview to create or improve program.md. Use when a target repository lacks program.md, its goals, metrics, data, or guardrails are unclear, or the user asks to be grilled about an autoresearch setup.
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。
Developer reference for Senpai's retired direct-GitHub experiment logging workflow. Use only when auditing or migrating legacy research tracks.
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。
Operator-side analysis of historical ML experiment PRs in Senpai research tracks. Use this skill whenever the user asks to: analyze experiments, categorize PRs, bucket experiments, summarize what's been tried, understand experiment history, review merged vs closed results, or asks "what experiments have we run / worked / failed". Also triggers for: "pull the latest experiments", "what's been tried so far", "category breakdown of PRs", "which experiments succeeded", "noam track analysis". When a branch name is mentioned (e.g. "noam branch", "on the noam branch"), pass it as the base branch to scope the fetch to just those PRs.
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。
Post material interim assignment updates, then commit and submit a terminal experiment result through the typed Senpai GitHub tools.
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。