--- name: build-teaql-app description: "Build or change a TeaQL application in Java, Rust, Go, Swift, Python, C#/.NET, or TypeScript, including Kotlin/JVM applications that consume Java-generated libraries. Mandatory order: first draft and save a complete KSML model, then verify the client and evaluate that saved model, repair it through repeated evaluation rounds, and generate only after evaluation reaches zero errors. Use for KSML modeling, seven-language TeaQL generation, generated assist APIs, auditable business logic, application verification, or parallel human review." --- # Build TeaQL App Turn a business requirement into a KSML contract and a verified TeaQL application. Never run model evaluation before the first complete KSML model has been written and saved. ## Mandatory Workflow Order Do not reorder these stages: 1. Understand the requirement and choose the model target. 2. Draft and save the first complete KSML model. 3. Only after the model exists, verify the TeaQL client. 4. Evaluate the saved model. 5. Repair from the report and re-evaluate repeatedly. 6. At zero Errors, signal Model Ready and continue without waiting. 7. Generate, implement, compile, test, run, and report. ## Prepare the Model Target 1. Read the target repository's nearest `AGENTS.md`. 2. Capture the original business requirement, model target path, requested language and outputs, and runnable or testable outcome. Supported language families are Java, Rust, Go, Swift, Python, C#/.NET, and TypeScript. A Kotlin/JVM application uses the Java-generated library and Java runtime; do not look for a separate Kotlin generator. Swift uses the `swift-lib-core` target for a generated Swift package with local SQLite and TFP client support. C++, Dart, Ruby, and other unlisted smaller language ecosystems are not supported. 3. Keep a compact evidence ledger while working. Record commands, evaluation counts, generated guides, assist calls, policy checks, tests, and artifact paths as they occur. Do not load a full KSML rule catalog before modeling. Use [`references/golden-example.xml`](references/golden-example.xml) as the grammar example and adapt its structure—not its pet-clinic concepts—to the domain. The golden example exists only to demonstrate KSML syntax. Never carry over its domain names (clinic, pet, appointment, species, microchipped) or its organizational structure into a different business domain. <!--BLOCK_ID:phase_modeling--> ## Model First Create and save a complete KSML model before running any TeaQL command. Do not evaluate an absent, empty, or placeholder model target. ### Preserve model identity and declared actor provenance On the entry `<root>`, write `model_uuid`, `agent`, `agent_version`, `llm_provider`, `llm_model`, and `skill_version` before the first evaluation. Generate one UUID for a newly created independent model (UUIDv7 preferred; a valid random UUID is
Build or change a TeaQL application in Java, Rust, Go, Swift, Python, C#/.NET, or TypeScript, including Kotlin/JVM applications that consume Java-generated libraries. Mandatory order: first draft and save a complete KSML model, then verify the client and evaluate that saved model, repair it through repeated evaluation rounds, and generate only after evaluation reaches zero errors. Use for KSML modeling, seven-language TeaQL generation, generated assist APIs, auditable business logic, application Оригинален текст · няма потвърден превод на български Теми: agentic-coding · ai-coding · code-generation