Dataset Builder Agent Skill

Turn field artefacts into a labelled dataset and 20+ golden cases — the seed of the eval suite that gates the build.

Context cost
11.5k tokensestimated from the bundle, loaded when it triggers
Bundle
13 files · 45.9 kBtext throughout, nothing executable
Licence
Apache-2.0the author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Turn field artefacts into a labelled dataset and 20+ golden cases — the seed of the eval suite that gates the build. Attaches a ground-truth label to every real artefact, scores each label on the evidence ladder, and clears each case through four gates (provenance, ground-truth, unreachable, held-out) so a foundation model cannot pre-pass its own memorised test. Fires on "make our own dataset", "create golden cases", "label this", "turn our field logs into a test set the model hasn't seen", "seed the eval suite".

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data science

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md14.0 kB · preview
--- name: dataset-builder type: generator supersedes: none ---
8## What it does
9
10Takes the field artefacts a fellow already holds — the moat-seed rows yoda-data-sourcing surfaced — and turns them into two things: a labelled dataset (every artefact paired with a ground-truth label and its provenance) and the ≥20 golden cases carved out of it. A case earns "golden" only by clearing four gates: it cites the real artefact it came from, its label is ground truth on the evidence ladder (not a rater's opinion), a foundation model has never seen the input and cannot narrate the label from training, and it is held out from the build so the model cannot be tuned against it. The artefact is the filled template.md: the labelling ledger plus the golden seed. That seed is the honest test — the data a model cannot pre-pass — and it is what eval-first-spec composes into the scoreable v1 spec. A dataset a model could reproduce is not shipped; it measures nothing.
11
12## The Icarus reframe
13
14A dataset a foundation model can already reproduce cannot tell you whether your product works — the model passes its own memorised test and you learn nothing. So a golden case is golden only when it clears two gates a generic labelling job skips. First, the input is unreachable: a model has never seen it and cannot reconstruct the label from training — the YODA test, aimed this time at the eval set, not the moat. Second, the label is ground truth on the evidence ladder — what actually happened, not what a rater thinks; an opinion label scores 0.1 and is a hypothesis to resolve, not a golden case. The labelled dataset is the moat made measurable: the one test a competitor's model cannot pre-pass.
15
16## When to use / When NOT
17
18Use once a fellow holds real field artefacts (from yoda-data-sourcing's P rows, from probes, from historical records) and needs to turn them into a labelled dataset and the golden cases that will gate the build. Trigger phrases: "make our own dataset", "create golden cases", "label this", "turn our field logs into a test set the model hasn't seen", "seed the eval suite".
19
20Do not use when:
21
22| Request | Belongs to |
23|---|---|
24| "Which of our candidate sources are actually proprietary / where is our moat data?" | yoda-data-sourcing (section 03). It finds and bands the sources; this labels the artefacts those sources produce. Run it first; this second. |
25| "I have my 20 labelled cases — now compose them into the typical/edge/adversarial spread, pick the autonomy level, and set the cost-per-outcome budget." | eval-first-spec (section 07). This skill hands it the labelled seed; that skill writes the job line, the band composition, the pass/fail contract, autonomy, and cost. Do not poach the spec. |
26| "Which of the expert's knowledge is teachable procedure vs show-only judgment?" | explicit-vs-tacit-capture (section 03). This skill uses inter-rater disagreement to flag an unsettled label; deciding what is codifiable at all is that skill's job. |
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,370 words
Files13
LicenceApache-2.0
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈240
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
11,235
on trigger
The instruction body and 12 supporting files, read only when the skill fires.
5.7%
of a 200k window
Ten skills this size would take about 57% of the window before you open a file.
050k100k150k200k context window

11.5k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

13 files, 45.9 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md14.0 kB
  • template.md4.0 kB
  • examples/sample.md7.3 kB
  • tests/RESULTS.md5.3 kB
  • tests/rubric.json1.1 kB
  • tests/adversarial/01.md1.2 kB
  • tests/adversarial/02.md1.6 kB
  • tests/adversarial/03.md1.5 kB
  • tests/golden/01.md2.2 kB
  • tests/golden/02.md2.0 kB
  • tests/golden/03.md2.0 kB
  • tests/golden/04.md1.9 kB
  • tests/golden/05.md1.8 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 13 files you can review in full before installing. The Apache-2.0 licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Dataset Builder · 11.5k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/dataset-builder/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/dataset-builder" ] && { [ ! -e .claude/skills/dataset-builder ] || { mv .claude/skills/dataset-builder "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/dataset-builder" .claude/skills/ && echo "Installed .claude/skills/dataset-builder"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add the-utopia-studio/dataset-builder

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/dataset-builder/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/dataset-builder/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/dataset-builder" ] && { [ ! -e ~/.claude/skills/dataset-builder ] || { mv ~/.claude/skills/dataset-builder "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/dataset-builder" ~/.claude/skills/ && echo "Installed ~/.claude/skills/dataset-builder" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/dataset-builder/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencethe-utopia-studio/dataset-builder

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

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