wbagent

name	wbagent
description	Use this skill for querying and analyzing Weights & Biases projects through the W&B SDK and the local `wandb_helpers.py` query helpers. Covers run discovery, filtered run-table queries, selected summary metrics, config comparisons, exact run counts, artifacts, sweeps, reports, and bounded history scans. This is the preferred way to query W&B from the wandb-driven-dev plugin.

W&B Query Agent Skill

This skill is the W&B query surface for wandb-driven-dev. Use it when a task needs live W&B data, run metadata, summary metrics, config values, histories, artifacts, sweeps, or Reports SDK details.

Do not write broad ad hoc W&B loops first. Start with scripts/wandb_helpers.py, then use direct W&B SDK calls only when the helper does not cover the query shape.

Available Files

• scripts/wandb_helpers.py: primary helper module for W&B querying.
• references/WANDB_SDK.md: W&B SDK usage notes and query patterns.
• references/WANDB_CONCEPTS.md: entity/project/run/config/history concepts.
• references/REPORTS.md: W&B Reports authoring guide — recipe, runnable skeleton, Runset filters, RunComparer, and rendering gotchas.

Standard Preamble

Use this import pattern in one-off scripts:

import sys
sys.path.insert(0, "skills/wbagent/scripts")

from wandb_helpers import (
    get_api,
    probe_project,
    build_filters,
    fetch_runs,
    fetch_run_summaries,
    count_runs,
    runs_to_dataframe,
    compare_configs,
    scan_history,
    scan_history_until_step,
    compare_runs_at_step,
)

Create clients with get_api() rather than bare wandb.Api() unless there is a specific reason. It sets a larger timeout for real projects.

Query Rules

• Use server-side filters for run searches: state, display_name, tags, config.KEY, summary_metrics.KEY, created_at, group, and job_type.
• Use fetch_runs() for run tables. It uses selected GraphQL summaryMetrics(keys=...) and avoids materializing wide SDK run objects.
• Use count_runs() for exact counts. It uses lazy api.runs(..., per_page=1) and does not load all runs.
• Use fetch_run_summaries() for a small set of known run IDs when setup or review needs selected summary keys.
• Always pass explicit keys=[...] to history scans. Never call run.history() or run.scan_history() without keys on large projects.
• Use scan_history_until_step() or compare_runs_at_step() for at-budget comparisons so scans stop once the selected step key passes the target.
• Use pandas/numpy for analysis. Do not dump raw histories or long run lists into the response.
• For unfamiliar projects, call probe_project(api, "entity/project") before guessing metric names.

Common Recipes

Exact Counts

api = get_api()
path = "entity/project"

total = count_runs(api, path)
finished = count_runs(api, path, {"state": "finished"})
crashed = count_runs(api, path, {"state": "crashed"})

print({"total": total, "finished": finished, "crashed": crashed})

Filtered Run Table

api = get_api()
rows = fetch_runs(
    api,
    "entity/project",
    metric_keys=["val/loss", "train/global_step"],
    filters={
        "state": "finished",
        "config.model.name": "baseline",
        "summary_metrics.val/loss": {"$lt": 0.2},
    },
    config_keys=["model.name", "lr", "batch_size"],
    order="+summary_metrics.val/loss",
    limit=20,
)

fetch_runs() returns flat rows with id, name, display_name, state, created_at, requested metrics, and requested config values such as config.model.name.

CLI-Friendly Filters

filters = build_filters(
    [
        "config.max_steps=20000",
        "summary_metrics.val/loss<0.2",
        "created_at>=2026-05-01",
    ],
    default_state="finished",
)
rows = fetch_runs(api, "entity/project", metric_keys=["val/loss"], filters=filters)

Known Run Summaries

rows = fetch_run_summaries(
    api,
    "entity/project",
    run_ids=["abc123", "def456"],
    summary_keys=["val/loss", "train/global_step"],
)

At-Step Comparison

comparison = compare_runs_at_step(
    api,
    "entity/project",
    run_ids=["abc123", "def456"],
    step=10_000,
    step_key="train/global_step",
    metrics=["train/loss", "val/loss"],
)

This groups metrics by namespace and retries sparse metrics one by one when a combined scan misses rows.

Direct History Scan

run = api.run("entity/project/abc123")
rows = scan_history(
    run,
    keys=["_step", "train/global_step", "train/loss"],
    max_rows=10_000,
)

scan_history() automatically uses beta_scan_history() for large runs when available.

Output Guidance

When answering a W&B data question, report:

• the project path queried
• filters and metric/config keys used
• sample size or count method
• compact tables or aggregates
• any missing metrics or incomplete runs

For large analyses, save reusable query scripts near the experiment context when useful, but keep final answers concise and number-backed.

When To Read References

• Read WANDB_SDK.md for SDK method signatures, filters, history scans, artifacts, and sweeps.
• Read WANDB_CONCEPTS.md when entity/project/run/history/summary semantics matter.
• Read REPORTS.md to author or update a W&B Report (recipe, skeleton, Runset filters, RunComparer, rendering gotchas).

Do not read all references by default. Use only the one needed for the current question.