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.
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See how to installReview the instructions the agent will receive before you install.
Use this developer-only skill when inspecting a copied or mounted Senpai OpenHands state tree. It is deliberately excluded from the skills installed into advisor and student runtimes.
Run the read-only analyzer from the Senpai repository:
uv run python tools/senpai_tool_telemetry.py \
/path/to/openhands_state \
--hours 12 \
--naive-timezone UTC \
--json /tmp/senpai-tool-telemetry.json
Pass multiple state roots to compare deployments in one report. The command
recursively discovers root and delegated-child events/ directories. It
prints a compact table and writes structured JSON containing per
source/root-or-child-depth/role/model/tool calls, successes, errors, pending
calls, retained tool latency, explicit status-check volume, and identical-call
repetition signals.
OpenHands SDK v1.40 event timestamps may be naive local wall time. Give the
capture host's actual timezone with --naive-timezone, using an IANA name,
UTC, or a fixed offset such as +02:00. For copied roots from different
hosts, add a repeatable override such as
--source-timezone /path/to/cedar=+02:00. A naive timestamp without a matching
default or source override fails clearly; the analyzer never guesses UTC or
the machine running the report. Explicit offsets embedded in an event are
preserved regardless of these options.
Tool arguments are never included in the report. The analyzer creates a short-lived in-memory fingerprint only to identify repeated identical calls. Do not copy raw event bodies into a report merely to recover arguments.
Interpret the output carefully:
get_training_status and get_job_status counts are model-issued tool
calls. They do not include cheap controller-internal monitor polls.base_state.json. If a conversation changed models in place, older calls
cannot be attributed more precisely from the event store alone.Report evidence before recommendations. Separate provider behavior from runtime failures such as dead terminal sessions, missing observations, or schema errors.
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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.
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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.
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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.
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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.
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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".
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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.
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