Wandb Eval Tables

Organization

wandb

wandb-eval-tables

Convert W&B Table artifacts into non-destructive EvalTable previews with scan-first planning, typed input/output/score columns, bounded batches, verification, and safe removal. Use when a coding agent needs to create, inspect, compare, verify, or remove W&B EvalTable previews.

Overview

Publisher	wandb
Repository	`skills`
Skill name	`wandb-eval-tables`
Stars	74
Forks	3
Bundled files	3
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by wandb on GitHub. Read the source before you install it.

Installation

Install the Wandb Eval Tables AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/wandb/skills/tree/main/skills/wandb-eval-tables
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/wandb/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/wandb-eval-tables .claude/skills/wandb-eval-tables

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wandb Eval Tables in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wandb Eval Tables on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wandb Eval Tables is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

W&B EvalTable previews

Use the bundled helper to preview existing wandb.Table data as wandb.EvalTable data. A preview creates new runs and keys; it never overwrites the source runs or tables.

Read references/EVAL_TABLES.md before converting. It is the canonical source for EvalTable semantics, source selection, limits, column roles, verification, and removal.

Environment

Run the helper from an environment that provides a W&B SDK with wandb.EvalTable and Weave. Use uv to supply missing dependencies:

bash
uv run --with 'wandb[workspaces]>=0.28.1' --with weave \
  python skills/wandb-eval-tables/scripts/table_artifact_to_eval_table.py --help

Credentials and default scope may come from WANDB_API_KEY, WANDB_ENTITY, and WANDB_PROJECT; otherwise pass explicit entity/project arguments. Downloads use the operating system's temporary directory, not a fixed current working directory.

Required workflow

Choose the narrowest source that matches the request: exact artifact, one run, one sweep, or a project/workspace. Do not broaden an explicitly named source.
Run scan. This is read-only preflight and is not proof of conversion.
Review eligible_summary_by_table_key, table shapes, truncations, warnings, and existing-preview metadata. Select one table key unless the user asks for a broader batch.
Classify columns only when confident. Input tuples must uniquely identify rows; score columns must be numeric or boolean; free-text labels and rationales are outputs. Leave ambiguous tables untyped.
Run preview for exactly one --table-key per invocation. Keep the target in the source project unless the user requests another writable project.
If runs were created, run verify-preview on the final created run and the exact logged key. Report success only when it returns verified: true.
Report created and skipped sources plus every truncation or failed check.

bash
T=skills/wandb-eval-tables/scripts/table_artifact_to_eval_table.py

uv run python "$T" scan \
  --source-workspace ENTITY/PROJECT --max-runs 4

uv run python "$T" preview \
  --source-workspace ENTITY/PROJECT \
  --target-project ENTITY/PROJECT \
  --max-runs 4 \
  --table-key predictions

uv run python "$T" verify-preview \
  --run ENTITY/PROJECT/PREVIEW_RUN_ID \
  --table-key predictions_preview

Safety

scan and preview --dry-run are read-only. preview creates W&B runs, EvalTables, and Weave evaluations; run it only when the user requested a conversion.
The helper caps tables at 10,000 rows and 100 columns, warns above 1,000 rows, and refuses more than 50 eligible table artifacts for one key. Surface every cap; never imply a truncated preview is complete.
Do not bypass helper failures with ad-hoc logging. Narrow or correct helper arguments, then retry only when the change is safe.
Removal deletes both Weave evaluations and preview runs. Run delete-preview --dry-run first, verify every target is helper-created, and obtain confirmation before the irreversible invocation. Never delete source runs.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wandb Eval Tables AI skill do?

Why use Wandb Eval Tables on TypingMind?

Because you install it once and use it with any model. Wandb Eval Tables is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wandb Eval Tables in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wandb/skills/tree/main/skills/wandb-eval-tables. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wandb Eval Tables?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wandb Eval Tables?

As many as you like. As long as a model supports skills, you can use Wandb Eval Tables with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wandb Eval Tables AI skill free?

Yes. It is published on GitHub by wandb under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

W&B EvalTable previews

Environment

Required workflow

Safety

Bundled files

Frequently asked questions

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