How do I install the Wandb Autoresearch skill?

Run npx skillmds add wandb/wandb-autoresearch in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Wandb Autoresearch safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Wandb Autoresearch?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Wandb Autoresearch free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Wandb Autoresearch?

wandb (@wandb) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Wandb Autoresearch by wandb — Agent Skill

Name: Wandb Autoresearch
Rating: 4.2 (5 reviews)
Author: wandb

Wandb Autoresearch

Run bounded, evidence-driven training research through W&B Launch: assess project readiness, establish launchable code and queue capacity, smoke-test real jobs, execute serial trials, compare metrics, and persist resumable research state. Use when a coding agent is asked to autonomously test training hypotheses or tune a real W&B-tracked workload.

wandb 5a1b213 6 files · 13.9 KB Updated Sep 9, 2026

File contents

W&B autoresearch

Use W&B Launch to execute real training experiments, read their results, and choose the next bounded trial. Do not create a local wandb.init() run as a substitute for compute that was supposed to run through Launch.

The Launch implementation lives in skills/wandb-primary:

skills/wandb-primary/scripts/launch_helpers.py
the Launch section in skills/wandb-primary/SKILL.md

Install missing packages explicitly with uv; do not assume a fixed working directory or preinstalled environment.

Flow

Read only the reference for the current stage:

Stage	Entry condition	Read
Understand	Always first; inspect launchable code, queue capacity, data location, and prior state	`references/understand.md`
Set up	Launchable code or usable queue capacity is missing	`references/setup.md`
Search	Launchable code and queue capacity both exist	`references/search.md`

The readiness gate is launchable code + queue with usable capacity. A dataset may be a versioned Artifact or may be supplied by the code/image; its storage form alone does not determine readiness.

Experiment records

Use each W&B object for one job:

Runs hold trial config, metrics, system metrics, status, and code/job lineage.
Job or code Artifacts make code relaunchable.
Dataset Artifacts provide versioned inputs when the workload needs them.
Launch queues and agents provide compute.
A small local markdown file holds the hypothesis, trial tally, decisions, queue, and next step.

Keep local state at a user-visible path in the current project or a path the user supplied. Sync it to a W&B Artifact only when cross-session resume is useful and the user authorizes the write:

S=skills/wandb-autoresearch/scripts/autoresearch_state.py
uv run --with wandb python "$S" load ENTITY PROJECT --dest .
uv run --with wandb python "$S" save ENTITY PROJECT --path AUTORESEARCH_STATE.md

Compute safety

Confirm the queue, metric direction, search axis, and trial/compute budget before the first non-smoke launch. Do not exceed the agreed budget.
Smoke-test the real job at a deliberately small setting before full trials.
Change only config fields the program actually reads. Use a code edit and a new job Artifact for architecture or pipeline changes.
Run trials serially unless the user explicitly authorizes parallel compute. Inspect each completed result before selecting the next trial.
Report failures, hardware utilization, and uncertainty. Re-run a likely winner with another seed when the apparent gap could be noise.
Never fabricate a run, metric, queue result, or completion state.

---name: wandb-autoresearchdescription: Run bounded, evidence-driven training research through W&B Launch: assess project readiness, establish launchable code and queue capacity, smoke-test real jobs, execute serial trials, compare metrics, and persist resumable research state. Use when a coding agent is asked to autonomously test training hypotheses or tune a real W&B-tracked workload.--- # W&B autoresearch Use W&B Launch to execute real training experiments, read their results, and choose the next bounded trial. Do not create a local `wandb.init()` run as a substitute for compute that was supposed to run through Launch. The Launch implementation lives in `skills/wandb-primary`: - `skills/wandb-primary/scripts/launch_helpers.py` - the `Launch` section in `skills/wandb-primary/SKILL.md` Install missing packages explicitly with `uv`; do not assume a fixed working directory or preinstalled environment. ## Flow Read only the reference for the current stage: | Stage | Entry condition | Read | |---|---|---| | Understand | Always first; inspect launchable code, queue capacity, data location, and prior state | `references/understand.md` | | Set up | Launchable code or usable queue capacity is missing | `references/setup.md` | | Search | Launchable code and queue capacity both exist | `references/search.md` | The readiness gate is `launchable code + queue with usable capacity`. A dataset may be a versioned Artifact or may be supplied by the code/image; its storage form alone does not determine readiness. ## Experiment records Use each W&B object for one job: - Runs hold trial config, metrics, system metrics, status, and code/job lineage. - Job or code Artifacts make code relaunchable. - Dataset Artifacts provide versioned inputs when the workload needs them. - Launch queues and agents provide compute. - A small local markdown file holds the hypothesis, trial tally, decisions, queue, and next step. Keep local state at a user-visible path in the current project or a path the user supplied. Sync it to a W&B Artifact only when cross-session resume is useful and the user authorizes the write: ```bash S=skills/wandb-autoresearch/scripts/autoresearch_state.py uv run --with wandb python "$S" load ENTITY PROJECT --dest . uv run --with wandb python "$S" save ENTITY PROJECT --path AUTORESEARCH_STATE.md ``` ## Compute safety - Confirm the queue, metric direction, search axis, and trial/compute budget before the first non-smoke launch. Do not exceed the agreed budget. - Smoke-test the real job at a deliberately small setting before full trials. - Change only config fields the program actually reads. Use a code edit and a new job Artifact for architecture or pipeline changes. - Run trials serially unless the user explicitly authorizes parallel compute. Inspect each completed result before selecting the next trial. - Report failures, hardware utilization, and uncertainty. Re-run a likely winner with another seed when the apparent gap could be noise. - Never fabricate a run, metric, queue result, or completion state.

Frequently asked questions