Declarative language tool for creating complex test data with support for constraints and cross-references.
Install
mkdir -p .claude/skills/vague && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12325" && unzip -o skill.zip -d .claude/skills/vague && rm skill.zipInstalls to .claude/skills/vague
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when writing Vague (.vague) files - a declarative language for generating realistic test data with superposition, constraints, and cross-referencesKey capabilities
- →Define data schemas with various types
- →Specify data superposition for weighted values
- →Set ranges for integer and date fields
- →Define collections with specific item counts
- →Apply constraints to data fields
- →Generate unique and private data fields
How it works
The skill uses a declarative language in .vague files to define schemas, types, superposition, ranges, and constraints, which are then used to generate realistic test data.
Inputs & outputs
When to use vague
- →Generating mock data
- →Creating test datasets
- →Defining data schemas
- →Validating data structures
About this skill
Vague Language
Quick Start
schema Invoice {
id: unique int in 1000..9999,
status: "draft" | "sent" | "paid",
total: decimal in 100.00..5000.00,
line_items: 1..5 of LineItem,
tax: round(total * 0.2, 2),
assume total > 0
}
dataset TestData {
invoices: 100 of Invoice
}
Core Syntax
- Types:
string,int,decimal,boolean,date - Superposition:
"a" | "b"or weighted0.7: "a" | 0.3: "b" - Ranges:
int in 1..100,date in 2020..2024 - Collections:
1..5 of Itemor100 of Item - Let bindings:
let statuses = "active" | "pending"(reusable values) - Computed:
total: sum(items.amount),median(),first(),last(),product() - Constraints:
assume due_date >= issued_date - Invariants:
invariant amount > 0 "message"(never violated, even in violating mode) - Contracts:
contract Name { invariant ... }applied viaschema X implements Name - Refine:
} refine { if type == "A" { field: int in 1..10 } } - Match:
match status { "a" => "Alpha", "b" => "Beta" } - References:
any of companies where .active == true - Parent ref:
= ^parent_field - Nullable:
string?orstring | null - Unique:
id: unique int in 1..1000 - Private:
age: private int(generated but excluded from output) - Ordered:
[48, 52, 55, 60](cycles through values) - Conditional:
field: string when condition(field only exists if condition is true)
Reference Files
- references/syntax.md - Complete syntax
- references/functions.md - Built-in functions
- references/plugins.md - All plugins (Faker, Issuer, Regex, Date, HTTP, SQL, GraphQL)
- references/cli.md - CLI usage
When not to use it
- →When generating data without schemas or constraints
- →When simple random data generation is sufficient
- →When working with data that does not require cross-references
Limitations
- →Requires knowledge of Vague language syntax
- →Focuses on generating test data
- →Does not infer data relationships not explicitly defined
How it compares
This skill provides a declarative and structured way to generate complex test data with specific constraints and relationships, unlike simple data generation tools.
Compared to similar skills
vague side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| vague (this skill) | 0 | 7mo | No flags | Intermediate |
| backtesting-frameworks | 17 | 2mo | No flags | Advanced |
| evaluating-machine-learning-models | 1 | 27d | Review | Intermediate |
| evaluate-environments | 1 | 26d | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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