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。
Produce a fresh, read-only status report for a Senpai research track from GitHub, W&B, and the current agent's local controller state.
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。
Report progress against the configured program.md and fleet activity now. Keep scientific progress separate from infrastructure liveness, and distinguish observed evidence from inference.
Resolve these values from the environment before collecting evidence:
GH_REPOADVISOR_BRANCHRESEARCH_TAGWANDB_ENTITYWANDB_PROJECTTARGET_WORKDIRSENPAI_OPENHANDS_STATE_DIRIf a required value is absent, record an evidence gap. Never substitute a remembered repository, branch, cluster, project, dataset, or metric.
Use the immutable program.md snapshot embedded in your system prompt for its goals, metric contracts, benchmarks, training constraints, and permitted reporting paths. Derive every metric and benchmark in the report from that snapshot or live evidence.
wandb-primary skill against
$WANDB_ENTITY/$WANDB_PROJECT. Start with run IDs linked from active PRs,
then inspect only the recent runs needed to establish the test frontier.get_prs for the configured $GH_REPO and
$ADVISOR_BRANCH. Check assignment, workflow state, update time, linked W&B
runs, and results. Keep the query bounded to relevant open and recently
completed work.$SENPAI_OPENHANDS_STATE_DIR/training/*.json for supervised training state.
Treat this as local evidence for the current advisor or student only. Do not
infer another node's process state; its GitHub transitions and W&B run state
are the portable evidence boundary.Cross-check timestamps, branch assignments, terminal training records, W&B run state, and PR claims. State contradictions explicitly. Missing access or absent records are evidence gaps, not evidence that nothing happened. Senpai uses no cross-node RPC or cluster API for status collection.
This workflow is observational. Do not mutate GitHub, Kubernetes, W&B, local state, or agent sessions.
Return a compact report with:
Put paper-facing test metrics before validation metrics unless program.md
defines a different publication contract. Pair every claimed result with its
source and caveat; never promote a validation-only or partial-evaluation result
to a test result.
If program.md permits a repository status artifact and the user requested
one, write:
analysis/STATUS_<YYYY-MM-DD-HHMM>_<branch>_fleet.md
Otherwise, return the report in the conversation without creating files.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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。
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。
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。
Create or improve a Senpai target repository's program.md. Use this skill whenever the user wants to point Senpai at a fresh ML or research target repository, define the research objective, primary metric, benchmark contract, allowed edit boundaries, W&B reporting contract, or prepare a repo for autonomous advisor/student experiment loops.
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。
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。