Agent Paranoid Android¶
English · Русский
Create repeatable test datasets with useful relationships and business rules, without copying source rows. Start with a file, safe database metadata, or an explicit specification, then review the plan before generating fresh values.
Stable 1.6.0 is the recommended release.
Use the CLI below or the runnable
Python API example.
This source guide targets candidate 1.7.0rc1; the commands below select it explicitly.
See candidate status for publication and acceptance.
Five-Minute Check¶
python3 -m pip install "agent-paranoid-android==1.7.0rc1"
test-data-agent doctor
test-data-agent demo --output out/demo
The demo uses a bundled fictional fixture. After installation it needs no network, database or AI provider. A healthy installation reports:
quickstart smoke: ok
doctor passed
Choose Your Starting Point¶
| I have | Start here |
|---|---|
| One CSV file | First CSV Dataset |
| Related CSV files | Related Tables |
| PostgreSQL | PostgreSQL Workflow |
| Trino | Trino Workflow |
| A safe profile or a plan | Profiles And Specs |
Generation Or Transformation?¶
Synthetic generation creates new rows from a reviewed DatasetSpec. Use it
for valid, boundary, load or controlled-invalid test cases. The normal process
is profile, review the specification, generate, validate, then
review the output.
Selective transformation keeps one-to-one correspondence with input rows and applies explicit field actions. Its result has mixed origin. Source-value preservation requires a non-sensitive field decision and fresh local-terminal approval of the exact inputs and plan; agents and MCP cannot issue it. Start with Transform Local Data.
Rules And Formats¶
Use business rules for formulas, field conditions, foreign keys and aggregate checks. The source/output matrix shows routes to CSV, JSON, Parquet and generated SQL. SQL export writes a file, not a database.
Advanced Integrations¶
AI, MCP, Containers, and Custom providers
Before Sharing A Dataset¶
Review the specification, validation reports and generation_manifest.json.
For a valid synthetic dataset expect synthetic: true,
source_rows_copied: false and validation_valid: true.
These checks do not certify statistical anonymity or detect every sensitive
value. Explicit mixed/negative datasets can intentionally fail validation.
Database access remains allowlisted, read-only and bounded. AI receives safe metadata, never raw PII, secrets or source rows. See the Safety Model and Troubleshooting.
For offline reading use Downloads. For another release use Documentation Versions. Maintainer and release records live in History And Maintenance.