How does the Data Agent Eval Factory differ from standard prompt testing?

This skill automates the creation of high-coverage JSONL test suites and provides a framework to execute these tests against your agents, scoring them for accuracy, safety, and hallucinations rather than just output formatting.

Which AI agent architectures and frameworks are supported?

The skill is framework-agnostic and works with any Python-based SQL, RAG, or tool-calling agent that can be interfaced via a local sandbox command or standard I/O.

Does this skill help with security and safety testing?

Each generated test suite includes a 30% adversarial mix specifically designed to trigger edge cases like SQL injection, permission escalation, and retrieval grounding failures that manual testing often misses.

What exactly is included in the purchase package?

You will receive the core evaluation engine, a library of failure taxonomies, templates for test suite generation, and automated reporting scripts to generate release-readiness documents.

Are there any specific environment requirements or limitations for execution?

To maintain safety and prevent accidental data mutations, the skill requires a local sandbox runner; it is not designed to perform live writes to production databases or external APIs.

Data Agent Eval Factory

Example session with this skill installed

Generate and run a 20-case eval for a SQL copilot using schema.sql. The agent translates natural language to Postgres. Sandbox command is 'node ./dist/runner.js'.

Read your context and instructions
Compiled the data agent eval

I have generated 20 test cases, executed them against your local runner, and scored the results. The evaluation includes 6 adversarial cases (30%) testing for unauthorized schema modifications and prompt injections.

SQL Agent Evaluation Report

Metric	Value	Result
Total Cases	20	—
Pass Rate	85%	17/20
Adversarial Pass Rate	100%	6/6
SQL Correctness	80%	12/15
Safety Refusals	100%	Correctly blocked `DROP TABLE` attempts
Recommendation	CONDITIONAL RELEASE	Requires review of complex JOIN failures

Top Failure Analysis

Case #14 (High): Failed to correctly join orders and line_items for "total revenue by category" query. SQL hallucinated a non-existent category_id on the orders table.

Case #18 (Medium): Syntax error on Postgres-specific ILIKE operator; agent used standard LIKE instead.

Generated Artifacts

evals/cases_20231027.jsonl: Full test suite.
evals/results_20231027.jsonl: Raw agent responses.
evals/scores_20231027.json: Weighted scoring breakdown.

Next steps

Review the generated report.md for specific SQL join failure patterns.
Refine the agent system prompt to prioritize Postgres-specific operators.
Re-run the eval suite after fixes to confirm the 100% pass rate on core queries.

Connects securely to your tools. The creator never sees your data.

About this skill

The problem

Data agents often fail silently through SQL hallucinations, incorrect tool selection, or unsafe data mutations. Manual testing is too slow to catch edge cases, and generic prompt lists don't validate execution accuracy or safety boundaries.

What it does

Generates balanced JSONL test suites for SQL, RAG, and tool-calling agents.
Includes a 30% adversarial mix to test prompt injection and boundary refusals.
Executes deterministic evaluations against local sandboxes via stdin/stdout.
Scores observable agent behavior and classifies failures by severity.
Produces automated reports with release recommendations based on pass rates.

Frameworks & tools

Python, JSONL datasets, and local sandbox runners. Compatible with SQL databases, RAG systems, and tool-calling LLM architectures.

Why this beats prompting it yourself

Writing individual test cases is tedious and lacks coverage. This skill applies structured failure taxonomies to ensure your agent handles joins, retrieval grounding, and permission escalation attempts correctly every time, not just in happy-path scenarios.

Use cases

Regression testing for SQL copilots after schema updates.
Evaluating RAG grounding to prevent hallucinations in customer support bots.
Validating tool-calling logic against adversarial prompt injections.
Generating release-readiness reports for stakeholder sign-off.

Known limitations

Requires a local sandbox command for execution mode. Does not support production writes or external side effects due to safety constraints.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Data Agent Eval Factory