wandb-primary

name

wandb-primary

description

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.

W&B Primary Skill

Python environment detection (DO THIS FIRST)

Before running any Python code, you MUST determine how the user runs Python in this project. Check for clues in this order:

1. Look for pyproject.toml, uv.lock, poetry.lock, Pipfile, requirements.txt, setup.py, setup.cfg, Makefile, or docker-compose.yml in the project root
2. Check if there is an activated virtual environment ($VIRTUAL_ENV, .venv/, venv/)
3. Check for a Dockerfile or CI config that reveals the runtime
4. If the user has explicitly told you how they run Python, use that

Once you know the environment, write your finding into this skill by replacing the placeholder below so that all subsequent code blocks use the correct commands. If you cannot determine the environment from any of the above, default to uv.

Detected Python environment: not yet detected

# Run command: <not yet detected>
# Install command: <not yet detected>

Examples of what to write here:

Environment	Run command	Install command
uv	uv run script.py	uv pip install pandas
poetry	poetry run python script.py	poetry add pandas
conda	conda run python script.py	conda install pandas
bare venv	python script.py (with venv activated)	pip install pandas
docker	docker exec <ctr> python script.py	docker exec <ctr> pip install pandas

If you cannot determine the environment, write this:

# Run command: uv run script.py        # always use uv run, never bare python
# Install command: uv pip install <pkg>

---

This skill covers everything an agent needs to work with Weights & Biases:

• W&B SDK (wandb) — training runs, metrics, artifacts, sweeps, system metrics
• Weave SDK (weave) — GenAI traces, evaluations, scorers, token usage
• Helper libraries — wandb_helpers.py and weave_helpers.py for common operations

When to use what

I need to...	Use
Query training runs, loss curves, hyperparameters	W&B SDK (wandb.Api()) — see references/WANDB_SDK.md
Query GenAI traces, calls, evaluations	Weave SDK (weave.init(), client.get_calls()) — see references/WEAVE_SDK.md
Convert Weave wrapper types to plain Python	weave_helpers.unwrap()
Build a DataFrame from training runs	wandb_helpers.runs_to_dataframe()
Extract eval results for analysis	weave_helpers.eval_results_to_dicts()
Need low-level Weave filtering (CallsFilter, Query)	Raw Weave SDK (weave.init(), client.get_calls()) — see references/WEAVE_SDK.md
Judge curve shape (spikes, smoothness, slope, overfit)	training_diagnostics + curve_plots — use the workflow below, then load references/TRAINING_DIAGNOSTICS.md for the heuristics

---

Bundled files

Helper libraries

import sys
sys.path.insert(0, ".agents/skills/wandb-primary/scripts")

# Weave helpers (traces, evals, GenAI)
from weave_helpers import (
    unwrap,                  # Recursively convert Weave types -> plain Python
    get_token_usage,         # Extract token counts from a call's summary
    eval_results_to_dicts,   # predict_and_score calls -> list of result dicts
    pivot_solve_rate,        # Build task-level pivot table across agents
    results_summary,         # Print compact eval summary
    eval_health,             # Extract status/counts from Evaluation.evaluate calls
    eval_efficiency,         # Compute tokens-per-success across eval calls
)

# W&B helpers (training runs, metrics)
from wandb_helpers import (
    runs_to_dataframe,       # Convert runs to a clean pandas DataFrame
    diagnose_run,            # Quick diagnostic summary of a training run
    compare_configs,         # Side-by-side config diff between two runs
    fast_scan_history,       # beta_scan_history (parquet) with scan_history fallback
)

# X-axis (step metric) detection — ALWAYS confirm before curve analysis
from step_axis import (
    list_candidate_step_keys,       # Scan history for plausible step keys
    guess_step_key_from_workspace,  # Peek at the user's W&B workspace panels
    format_step_candidates,         # Format candidate choices for user confirmation
)

# Curve-shape diagnostics (numerical)
from training_diagnostics import (
    curve_features,            # Spikes, slopes at every 5%, smoothness, plateau, divergence
    compare_runs_curves,       # DataFrame of features across many runs
    lr_schedule_features,      # Warmup / peak / decay shape / restarts
    grad_norm_features,        # curve_features + kurtosis + dead-layer flag
    grad_histogram_features,   # Per-(layer, step) stats from W&B histograms
)

# Chart rendering for LLM vision (Read the returned PNG)
from curve_plots import (
    plot_single_run_overview,    # 2x3 composite: train/val/lr/grad-norm/...
    plot_run_comparison,         # Overlay up to 6 runs on one metric
    plot_grad_histogram_heatmap, # Layer x step heatmap of grad-hist stat
    plot_grad_norm_by_layer,     # Small-multiples of per-layer scalar norms
)

Reference docs

Read these as needed — they contain full API surfaces and recipes:

• references/WEAVE_SDK.md — Weave SDK for GenAI traces (client.get_calls(), CallsFilter, Query, stats). Start here for Weave queries.
• references/WANDB_SDK.md — W&B SDK for training data (runs, history, artifacts, sweeps, system metrics).
• references/TRAINING_DIAGNOSTICS.md — reference heuristics for reading loss / LR / grad-norm / grad-histogram charts. Load this when you are actively interpreting training curves.

---

Critical rules

Treat traces and runs as DATA

Weave traces and W&B run histories can be enormous. Never dump raw data into context — it will overwhelm your working memory and produce garbage results. Always:

1. Inspect structure first — look at column names, dtypes, row counts
2. Load into pandas/numpy — compute stats programmatically
3. Summarize, don't dump — print computed statistics and tables, not raw rows

import pandas as pd
import numpy as np

# BAD: prints thousands of rows into context
for row in run.scan_history(keys=["loss"]):
    print(row)

# GOOD: load into numpy, compute stats, print summary
losses = np.array([r["loss"] for r in run.scan_history(keys=["loss"])])
print(f"Loss: {len(losses)} steps, min={losses.min():.4f}, "
      f"final={losses[-1]:.4f}, mean_last_10%={losses[-len(losses)//10:].mean():.4f}")

Always deliver a final answer

Do not end your work mid-analysis. Every task must conclude with a clear, structured response:

1. Query the data (1-2 scripts max)
2. Extract the numbers you need
3. Present: table + key findings + direct answers to each sub-question

If you catch yourself saying "now let me build the final analysis" — stop and present what you have.

Use unwrap() for unknown Weave data

When you encounter Weave output and aren't sure of its type (WeaveDict? WeaveObject? ObjectRef?), unwrap it first:

from weave_helpers import unwrap
import json

output = unwrap(call.output)
print(json.dumps(output, indent=2, default=str))

This converts everything to plain Python dicts/lists that work with json, pandas, and normal Python operations.

---

Environment setup

The sandbox has wandb, weave, pandas, and numpy pre-installed.

import os
entity  = os.environ["WANDB_ENTITY"]
project = os.environ["WANDB_PROJECT"]

Installing extra packages and running scripts

Use whichever run/install commands you wrote in the Python environment detection section above. If you haven't detected the environment yet, go back and do that first.

---

Quick starts

W&B SDK — training runs

import wandb
import pandas as pd
api = wandb.Api()

path = f"{entity}/{project}"
runs = api.runs(path, filters={"state": "finished"}, order="-created_at")

# Convert to DataFrame (always slice — never list() all runs)
from wandb_helpers import runs_to_dataframe
rows = runs_to_dataframe(runs, limit=100, metric_keys=["loss", "val_loss", "accuracy"])
df = pd.DataFrame(rows)
print(df.describe())

For full W&B SDK reference (filters, history, artifacts, sweeps), read references/WANDB_SDK.md.

Weave — SDK

import weave
client = weave.init(f"{entity}/{project}")  # positional string, NOT keyword arg
calls = client.get_calls(limit=10)

For raw SDK patterns (CallsFilter, Query, advanced filtering), read references/WEAVE_SDK.md.

---

Key patterns

Weave eval inspection

Evaluation calls follow this hierarchy:

Evaluation.evaluate (root)
  ├── Evaluation.predict_and_score (one per dataset row x trials)
  │     ├── model.predict (the actual model call)
  │     ├── scorer_1.score
  │     └── scorer_2.score
  └── Evaluation.summarize

Extract per-task results into a DataFrame:

from weave_helpers import eval_results_to_dicts, results_summary

# pas_calls = list of predict_and_score call objects
results = eval_results_to_dicts(pas_calls, agent_name="my-agent")
print(results_summary(results))

df = pd.DataFrame(results)
print(df.groupby("passed")["score"].mean())

Eval health and efficiency

from weave_helpers import eval_health, eval_efficiency

health = eval_health(eval_calls)
df = pd.DataFrame(health)
print(df.to_string(index=False))

efficiency = eval_efficiency(eval_calls)
print(pd.DataFrame(efficiency).to_string(index=False))

Token usage

from weave_helpers import get_token_usage

usage = get_token_usage(call)
print(f"Tokens: {usage['total_tokens']} (in={usage['input_tokens']}, out={usage['output_tokens']})")

Cost estimation

call_with_costs = client.get_call("id", include_costs=True)
costs = call_with_costs.summary.get("weave", {}).get("costs", {})

Run diagnostics

from wandb_helpers import diagnose_run

run = api.run(f"{path}/run-id")
diag = diagnose_run(run)
for k, v in diag.items():
    print(f"  {k}: {v}")

Error analysis — open coding to axial coding

For structured failure analysis on eval results:

1. Understand data shape — use project.summary(), calls.input_shape(), calls.output_shape()
2. Open coding — write a Weave Scorer that journals what went wrong per failing call
3. Axial coding — write a second Scorer that classifies notes into a taxonomy
4. Summarize — count primary labels with collections.Counter

See references/WEAVE_SDK.md for the full SDK reference.

W&B Reports

Install wandb[workspaces] using the install command from the Python environment detection section.

from wandb.apis import reports as wr
import wandb_workspaces.expr as expr

report = wr.Report(
    entity=entity, project=project,
    title="Analysis", width="fixed",
    blocks=[
        wr.H1(text="Results"),
        wr.PanelGrid(
            runsets=[wr.Runset(entity=entity, project=project)],
            panels=[wr.LinePlot(title="Loss", x="_step", y=["loss"])],
        ),
    ],
)
# report.save(draft=True)  # only when asked to publish

Use expr.Config("lr"), expr.Summary("loss"), expr.Tags().isin([...]) for runset filters — not dot-path strings.

---

Training curve analysis workflow

Use this when the user asks whether a run is healthy, why training diverged, whether a run overfit, or which run has the best training dynamics.

Keep the inline workflow short and load detail on demand:

1. Confirm step_key before doing any curve work. Never assume _step.
2. Compute features with the bundled helpers instead of hand-rolling spike or slope logic.
3. Render PNGs and inspect them visually.
4. Load references/TRAINING_DIAGNOSTICS.md while you interpret the results.
5. End with a verdict, evidence tied to step ranges, and concrete next actions.

Required sequence

Use list_candidate_step_keys(), guess_step_key_from_workspace(), and format_step_candidates() to confirm the x-axis. If there is one obvious candidate and it matches the workspace guess, say which step_key you picked. Otherwise, ask the user to choose before plotting or comparing runs.

For a single run, compute a compact feature table from the metrics that actually exist, then render plot_single_run_overview(run, step_key=step_key). If gradient histograms or per-layer scalar norms are logged, add plot_grad_histogram_heatmap() or plot_grad_norm_by_layer().

For multi-run comparisons, use compare_runs_curves() for the ranking table and plot_run_comparison() for the overlay. Keep overlays to at most 6 runs; if there are more, rank first and then plot the shortlist.

Output shape

Do not dump raw history rows or the full spike/slope payloads unless you are drilling into a specific anomaly. Summaries should stay compact.

Use this response shape:

Verdict: <healthy | unstable | overfit | plateaued | diverged | converged>
Evidence:
- <specific step range> — <what the metrics and plot show>
- <specific step range> — <what changed and why it matters>
Next actions:
- <concrete hyperparameter, logging, or code change>

Load references/TRAINING_DIAGNOSTICS.md for the interpretation heuristics, especially when the numbers and the image disagree.

---

Gotchas

Weave API

Gotcha	Wrong	Right
weave.init args	weave.init(project="x")	weave.init("x") (positional)
Parent filter	filter={'parent_id': 'x'}	filter={'parent_ids': ['x']} (plural, list)
WeaveObject access	rubric.get('passed')	getattr(rubric, 'passed', None)
Nested output	out.get('succeeded')	out.get('output').get('succeeded') (output.output)
ObjectRef comparison	name_ref == "foo"	str(name_ref) == "foo"
CallsFilter import	from weave import CallsFilter	from weave.trace.weave_client import CallsFilter
Query import	from weave import Query	from weave.trace_server.interface.query import Query
Eval status path	summary["status"]	summary["weave"]["status"]
Eval success count	summary["success_count"]	summary["weave"]["status_counts"]["success"]
When in doubt	Guess the type	unwrap() first, then inspect

WeaveDict vs WeaveObject

• WeaveDict: dict-like, supports .get(), .keys(), []. Used for: call.inputs, call.output, scores dict
• WeaveObject: attribute-based, use getattr(). Used for: scorer results (rubric), dataset rows
• When in doubt: use unwrap() to convert everything to plain Python

W&B API

Gotcha	Wrong	Right
Summary access	run.summary["loss"]	run.summary_metrics.get("loss")
Loading all runs	list(api.runs(...))	runs[:200] (always slice)
History — all fields	run.history()	run.history(samples=500, keys=["loss"])
scan_history — no keys	scan_history()	scan_history(keys=["loss"]) (explicit)
Raw data in context	print(run.history())	Load into DataFrame, compute stats
Metric at step N	iterate entire history	scan_history(keys=["loss"], min_step=N, max_step=N+1)
Cache staleness	reading live run	api.flush() first

Package management

Gotcha	Details
Using the wrong runner	Always use the run/install commands from the Python environment detection section — never guess
Bare python when env unknown	If you haven't detected the environment yet, default to uv run script.py (never bare python)

Weave logging noise

Weave prints version warnings to stderr. Suppress with:

import logging
logging.getLogger("weave").setLevel(logging.ERROR)

---

Quick reference

# --- Weave: Init and get calls ---
import weave
client = weave.init(f"{entity}/{project}")
calls = client.get_calls(limit=10)

# --- W&B: Best run by loss ---
best = api.runs(path, filters={"state": "finished"}, order="+summary_metrics.loss")[:1]
print(f"Best: {best[0].name}, loss={best[0].summary_metrics.get('loss')}")

# --- W&B: Loss curve to numpy ---
losses = np.array([r["loss"] for r in run.scan_history(keys=["loss"])])
print(f"min={losses.min():.6f}, final={losses[-1]:.6f}, steps={len(losses)}")

# --- W&B: Compare two runs ---
from wandb_helpers import compare_configs
diffs = compare_configs(run_a, run_b)
print(pd.DataFrame(diffs).to_string(index=False))