extract-test-set
Extracts raw price data for isolated unit tests, generating associated parquet files and test modules.
Install
mkdir -p .claude/skills/extract-test-set && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4363" && unzip -o skill.zip -d .claude/skills/extract-test-set && rm skill.zipInstalls to .claude/skills/extract-test-set
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.
Extract raw price dataframe for a test caseKey capabilities
- →Generate Pytest test modules
- →Isolate raw price data for specific vaults
- →Create companion parquet files
- →Link blockchain explorer metadata to tests
How it works
Runs an ad-hoc script against a central price database to extract a slice of time-series data into a schema-ready format.
Inputs & outputs
When to use extract-test-set
- →Creating vault price tests
- →Extracting test data for simulations
- →Generating price parquet files
About this skill
Extract test set from raw prices
This is a skill to extract price data from the raw prices for an isolated unit test.
Inputs
- Smart contract address and a blockchain as a blockchain explorer link
- Test case name
Relevant files
Seek metadata and Parquet information here:
Ad-hoc script
Create an ad-hoc Python script that reads
Script inputs
- chain id (numeric) - address tuple
- test case name
Scripts
- Extracts the price series from
DEFAULT_UNCLEANED_PRICE_DATABASE
Script outputs
- Pytest test module with a single test case
- Related Parquet file containing price data only for this vault
Write test case
Then the script creates test_xxx file, stores metadata there inline and creates corresponding test_xxx_price.parquet file for the test case to read.
- Include only a single test function, do not generate excessive tests
Run the script
- After running the script, run the generated test case
When not to use it
- →General data analysis
- →Large-scale backtesting across multiple chains
Prerequisites
Limitations
- →Requires database connectivity
- →Limited to predefined price sources
How it compares
It eliminates manual data trimming and boilerplate creation by generating specialized test fixtures directly from production state.
Compared to similar skills
extract-test-set side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| extract-test-set (this skill) | 1 | 6mo | No flags | Intermediate |
| senior-data-engineer | 21 | 7mo | Review | Advanced |
| hugging-face-datasets | 1 | 6mo | Review | Intermediate |
| bsl-model-builder | 0 | 8mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by tradingstrategy-ai
View all by tradingstrategy-ai →You might also like
senior-data-engineer
davila7
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.
hugging-face-datasets
patchy631
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
bsl-model-builder
boringdata
Build BSL semantic models with dimensions, measures, joins, and YAML config. Use for creating/modifying data models.
data-quality-frameworks
Anhvu1107
ALWAYS use this when the request matches Data Quality Frameworks: Implement data quality validation with Great Expectations, dbt tests, and data contracts.
df-basic-stats
qmakescl
>
add-etf
michaellaret7
Add an ETF to the database with proper classification. Handles web research, DB format matching, confirmation, and execution.