---
name: adaption-invent
description: Generate synthetic datasets using Adaption's Invent-a-Dataset. Use when 
  creating post-training datasets from scratch out of natural language descriptions, 
  to add new capabilities to a language model — especially in a zero-data regime 
  with no seed data available.
---

# Adaption Invent Skill

Generate synthetic training datasets using Adaption's Invent-a-Dataset feature.

## When to use

- Generate synthetic training datasets from natural language descriptions
- Create domain-specific datasets for post-training language models without any
  seed data to start from
- Estimate costs before generating data

## Available MCP Tools

| Tool | Description |
|------|-------------|
| `list_invent_domains` | List valid domain and qualified subdomain codes for dataset generation |
| `generate_dataset` | Estimate or launch generation of a new invented dataset (launches consume credits) |

After generation starts, use dataset tools to track progress:
| Tool | Description |
|------|-------------|
| `get_dataset_status` | Poll processing status of the generated dataset |
| `get_dataset` | Get details of the generated dataset |

## Example Workflows

### Generate domain-specific training data

Steps 1 and 2 are optional — a `dataset_prompt` on its own is enough, and the
taxonomy is inferred from it. Supply the codes when you want more control over
what ends up in the dataset.

1. Optionally call `list_invent_domains` to explore available domains and subdomains
2. Optionally choose domain and subdomain codes matching your use case
3. Call `generate_dataset` with `estimate: true` to preview cost:
   ```json
   {
     "domains": ["technology"],
     "subdomains": ["technology.software_applications"],
     "rows": 1000,
     "estimate": true
   }
   ```
4. Review estimate, then launch with `estimate: false`
5. Poll `get_dataset_status` with returned dataset ID until complete
6. Generated dataset is ready for training — an invented dataset is already a
   type of adaptive dataset, so it does not need adaptation

### Generate with custom specifications

```json
{
  "name": "Customer Support QA",
  "training_type": "instruction_dataset",
  "domains": ["corporate_business"],
  "subdomains": ["corporate_business.customer_service"],
  "rows": 500,
  "dataset_prompt": "Generate realistic customer questions about technical software product support scenarios"
}
```

### Control the output through `dataset_prompt`

`dataset_prompt` also carries constraints on output format, tone, and structure,
which is how you pin the shape of the generated rows. Both examples below omit
the taxonomy entirely and let it be inferred:

```json
{
  "rows": 1000,
  "dataset_prompt": "Question-answer pairs focusing on legal advice, factual explanations, and comparative analysis across diverse topics such as copyright law, government types, and liability. Questions should offer four choices; answers are limited to A, B, C, or D with no additional text."
}
```

```json
{
  "rows": 500,
  "dataset_prompt": "Summaries of used car advertisements in Serbian, featuring detailed descriptions of vehicle conditions, service histories, and equipment packages. Each prompt is an ad summary, and each response is a JSON object containing vehicle_description, service_history, and equipment_packages."
}
```

### Generate with language expansion

`language_expansion` is either a `translate` or a `localize` spec, and `type` is
required. `sample_rate` (0.01-1) is the share of rows expanded. Credits are
billed on the post-expansion row count, so this quotes above `rows`.

```json
{
  "domains": ["academic_education"],
  "subdomains": ["academic_education.stem"],
  "rows": 1000,
  "language_expansion": {
    "type": "translate",
    "languages": ["es", "fr", "de"],
    "sample_rate": 0.2
  }
}
```

To localize for country/language pairs instead:

```json
{
  "language_expansion": {
    "type": "localize",
    "pairs": [{ "country": "RS", "language": "sr" }],
    "sample_rate": 0.2
  }
}
```

## Domains and Subdomains

Domain codes are flat lowercase strings such as `technology`, `medical`, `legal`,
`code`, `corporate_business`, `academic_education`, and `personal_finance`.
Subdomain codes are always qualified with their domain as `domain.subdomain`,
for example `medical.symptoms_diagnosis` — a bare code can belong to more than
one domain, so an unqualified value is rejected.

Call `list_invent_domains` for the full catalog with the exact codes. A subdomain
naming a domain that is not in your `domains` array is rejected rather than
silently added.

## Parameters

`rows` is always required, plus at least one of `dataset_prompt`, `domains`, or
`subdomains`. With both taxonomy fields omitted, `dataset_prompt` is required and
the taxonomy is inferred from it; if nothing can be inferred the request is
rejected rather than generating across every domain. Supplying `domains` or
`subdomains` disables that inference.

| Parameter | Description |
|-----------|-------------|
| `rows` | Required number of rows to generate, subject to your plan's per-launch cap |
| `dataset_prompt` | Free-text description of the data you want, up to 10,000 characters |
| `domains` | Optional array of domain codes |
| `subdomains` | Optional array of qualified `domain.subdomain` codes |
| `training_type` | Shape of the generated data: `instruction_dataset` (default) or `preference_pairs` |
| `name` | Optional name for the generated dataset, up to 255 characters |
| `language_expansion` | Optional `translate` or `localize` expansion config |
| `estimate` | Set `true` to preview cost without launching |
| `idempotency_key` | Optional key for safe retries |

`training_type` here is the shape of the DATA, not the training method:
`instruction_dataset` produces prompt/completion rows, `preference_pairs`
produces ranked completion pairs. The `training_type` inside `hyperparams` on a
training run is a different field meaning LoRA versus full fine-tuning.

Generate with `preference_pairs` when the dataset will feed an alignment training
run — see the `adaption-training` skill, where that run needs
`training_method: "alignment"`.

## Tips

- Start with smaller row counts (100-500) to validate quality
- Use a specific `dataset_prompt` for better results
- Once an invented dataset covers the target capability, expand it with more
  domain-specific or general samples via `augment_dataset` to increase size and
  diversity before training
- Use `estimate: true` to check credits before launching
- Launch responses carry a `next_action` naming the tool to call next — follow it
- Use `idempotency_key` for safe retries if generation fails