phdata

Add or verify a JDBC connection and datasource in toolkit.conf for any supported type — Snowflake, SQL Server, Oracle, Postgres, MySQL, Hive, Databricks, Athena — including JDBC driver installation for non-bundled types and verification via a scoped ds scan. Use when configuring data sources, fixing connection errors, or when toolkit-check reports no datasource.

Configure the optional llmClient block in toolkit.conf so toolkit agent commands (discovery, pipeline-build, fix-sql, codegen ask) can call an LLM — Amazon Bedrock, OpenAI, or Anthropic. Use when agent commands fail for lack of an LLM, when switching providers or models, or when onboarding to the toolkit agent workflows.

Install and configure the phData Toolkit CLI end to end — verify Java, get the toolkit binary on PATH, authenticate, initialize a toolkit project, and extract Data Source tool defaults. Use when the toolkit is missing, toolkit-check fails, or a user is onboarding to phData Toolkit workflows.

Show a doctor-style report of the phData Toolkit CLI installation — binary, Java, project, datasources, and optional connectivity. Use when the user asks whether the toolkit is set up, why a toolkit command might be failing, or wants a health check before starting toolkit work.

Run toolkit agent pipeline-build to generate pipeline code from approved data contracts — SQL, dbt, or PySpark transforms plus data-quality tests and a synthetic-data (datagen) spec. Use after discovery produces contracts, or when the user wants to generate, regenerate, apply, or test pipelines from contracts.

Run toolkit agent discovery to resolve a dataspec against a live datasource into a reviewable data contract, then walk the human-review resolve loop until the contract is approved. Use when the user has (or needs) a dataspec and wants source-to-target mappings, or mentions data contracts, discovery, or humanReviewItems.

Author or validate a DiscoveryConfig dataspec — the YAML describing a target table (columns, grain, business rules, load strategy) consumed by toolkit agent discovery. Use when the user wants to define a new pipeline target, has a dataspec to check, or asks what a dataspec needs to contain.

Plant subtle multi-file bugs in a HOL lab app. Bugs span 2-3 files, are diagnosable from symptoms, have a clear 'aha' moment, and can be caught by a test.

Build a complete HOL lab repo from a brief. Reads a PDF/doc/text brief describing the company and app concept, asks clarifying questions, then generates a fully working Python/FastAPI/SQLite app with planted bugs, seed data, and feature exercises.

Propose 3-5 feature ideas for a HOL spec-driven build exercise. Features touch multiple layers, are buildable in ~30 min with Claude Code, and are visually demo-able.

Validate a HOL lab by simulating a participant. Walks through all 5 Acts (Explore, Fix, Spec, Build, Show & Tell) and produces a validation report with pass/fail, timing, and facilitator recommendations.

Rebrand a lab app to match a company profile. Updates app name, domain language, API paths, seed data names, and UI copy to feel authentic to the target company and industry.

Generate realistic synthetic seed data for a HOL lab app. Reads the app's models and creates 20-50 records per entity with industry-authentic values, realistic relationships, and useful edge cases.

Validate a HOL lab by simulating a participant. Walks through all 5 Acts (Explore, Fix, Spec, Build, Show & Tell) and produces a validation report with pass/fail, timing, and facilitator recommendations.

Validate a HOL lab by simulating a participant. Walks through all 5 Acts (Explore, Fix, Spec, Build, Show & Tell) and produces a validation report with pass/fail, timing, and facilitator recommendations.