build-teaql-app

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 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.

Model First

Create and save a complete KSML model before running any TeaQL command. Do not evaluate an absent, empty, or placeholder model target.

If the model is large (e.g., more than 15 objects), you MAY split it into multiple module files using <_include file="module.xml" />, but this is optional. The system supports dynamic output limits so a single-file model will also work.

• CRITICAL: To avoid keyword collisions in any of the seven generated languages, ALWAYS use two-word field names and entity names when there is any risk of conflict (e.g. use bonus_type instead of type, move_order instead of move, leave_type instead of type). Do not use bare single words like type or move as entity or field names.

Apply this minimal contract:

• Make root name the sole model name, in lowercase kebab-case ending with -service.
• Use org="example" when the user does not specify an organization.
• Put objects directly below <root> or within the included module files.
• Give every object _name, _module, and _module_key.
• Use representative literals for ordinary fields so TeaQL can infer types.
• Express a relationship as target_object().
• Do not declare id on business objects.
• Represent reusable finite states as constant objects; only constant objects contain <_value> children.
• Use generated functions such as createTime() only for their intended semantics.
• IMPORTANT: When writing large split models, create and write the files one by one using sequential tool calls. Do not attempt to output the entire schema for all modules in a single response.

Validation Pitfalls — Avoid on First Attempt

These are the most common first-attempt failures. Avoiding them saves multiple repair rounds:

1. Non-empty attribute values. EVERY attribute MUST have a non-empty representative value. Never write attr="" or attr=" ". Use a realistic example (e.g., email="[e-mail]", url="https://example.com/doc.pdf").
2. Language keyword avoidance. Entity and field names must not collide with programming language keywords (Rust: move, type, match, self, ref, mod, box, trait, impl, use, let, fn, async, yield; Java/C#: class, new, import, public, default, return; Go: type, map, range, go, select, interface; Swift: class, struct, protocol, extension, actor, associatedtype, inout, some, any, Self, init, deinit, subscript, operator; Python: class, def, from, import, lambda, yield; Kotlin/JVM application code: object, when, is, fun, val, var; TypeScript: class, interface, function, import, export, typeof; SQL: select, table, ). When a natural business concept is a single keyword (e.g., "move"), always use a instead (e.g., , , , ).

When you finish the model generation phase and evaluation passes with zero errors, output <phase-complete>model_generation</phase-complete>.

Evaluate and Repair the Saved Model

Now—and only now—load references/toolchains.md. Verify the installed client against the exact version required by the target repository or that reference. Stop and report a mismatch.

Submit the complete model target to the repository's Generation Service evaluation command. For Rust, every model-derived operation must include:

cargo teaql --input <model-file-or-directory> evaluate

IMPORTANT: If your model is split into multiple files in a directory (e.g., models/), you MUST pass the directory path (e.g., --input models/), NOT the main entry file (main.xml). Passing only the main file will cause the evaluator to fail with missing included files.

Repair from the Markdown evaluation report:

1. Fix all Errors, largest repeated pattern first.
2. Preserve Solids and already-correct model sections.
3. Re-evaluate after each repair round.
4. Resolve Warnings using the business requirement.
5. Treat Suggestions as optional.
6. Follow the report's concrete repair guidance rather than guessing or memorizing a separate rule catalog.
7. IMPORTANT: When dealing with extremely long evaluation logs (e.g., thousands of lines), DO NOT use paginated reading commands (like sed -n '1,300p') in an endless loop to read the entire file. Use targeted grep (e.g., grep -A 15 -B 5 "Error" or grep -A 10 -B 10 "error:") or read only the head/tail to quickly isolate and fix issues without wasting context.

Do not generate from a model with errors.

Repair Budget

Run at most 5 evaluation–repair rounds by default. If errors remain after 5 rounds, stop and present the current evaluation report to the user with a summary of what was fixed and what still fails. Ask the user to provide additional guidance or model corrections before continuing. Do not loop indefinitely.

On small-context models (≤ 64K tokens), evaluation may be treated as best-effort: if the evaluation report itself exceeds the available context budget, skip detailed evaluation parsing and proceed to generation with the best available model, noting the limitation in the work-complete report.

Signal Model Ready

As soon as evaluation reaches zero errors, notify the user without pausing:

Model Ready
- Model: <absolute model path>
- Evaluation: passed — <errors>, <warnings>, <suggestions>

Continue working. Human model or application review is asynchronous, not an approval gate. Incorporate review feedback when it arrives, then re-evaluate and regenerate if the domain contract changed.

CRITICAL: Do NOT output <phase-complete>evaluate_repair</phase-complete> if there are ANY ❌ Errors (Must Fix) listed in the text output of your evaluation command, even if the command execution returned success=true (which can happen if you pipe to tee). You MUST open the corresponding XML files and fix the errors (e.g., renaming fields that conflict with Rust keywords) before outputting the phase-complete tag.

When you finish the repair phase and reach zero errors, output <phase-complete>evaluate_repair</phase-complete>.

Generate and Implement

Generate only the outputs requested by the user. Use the exact language target and verification guidance in references/toolchains.md. Generate only targets actually advertised by the Generation Service; never manufacture a target name.

IMPORTANT: If your model is split into multiple files in a directory (e.g., models/), you MUST pass the directory path (e.g., --input models/), NOT the main entry file (main.xml) to the generation commands. Passing only the main file will cause the generation to fail with missing included files.

CRITICAL: Do NOT output <phase-complete>codegen</phase-complete> if ANY generation command returns an error or success=false. You MUST fix the command (e.g., incorrect directory paths) and retry until it succeeds. WARNING: When chaining commands, do NOT use cd dir && cmd && cd dir && cmd. The second cd will fail because you are already in dir. Instead, use cd dir && cmd1 && cmd2 or use subshells (cd dir && cmd1) && (cd dir && cmd2).

When generation commands complete successfully with success=true, output <phase-complete>codegen</phase-complete>.

• Never edit generated domain-library files.
• Find and read the generated local AGENTS.md before business code. If an expected generated application guide is missing, stop and report it.
• Read current model-aware help and object-specific assist for each entity/action. Never guess generated methods.
• Inspect generated source only when the local guide permits it or assist is incomplete; record the reason.
• Keep editable business logic in the generated application workspace.

Enforce the API constraint harness:

• Every query execution has purpose and comment through the generated language-specific API. Comment may be added earlier in the query chain; purpose is the capability boundary that exposes execution.
• Every save/update has an audit reason through the generated language-specific API.
• Read assist or generated source before using those APIs. Java and Rust spellings are examples, not names to copy into Go, Swift, Python, .NET, or TypeScript.
• Use the identity/request context required by the generated API.

Compile, test, and smoke-test the requested result. Repair model-derived problems at the model level and regenerate.

Report Work Complete

Load references/work-complete.md only after the requested result—not evaluation alone—is complete. Report actual evidence:

• original requirement and modeling result;
• model path and final evaluation counts;
• generated guide and exact assist/API facts used;
• query and write policy coverage;
• generated files left untouched;
• compile, test, and runtime results;
• token/context usage honestly, with estimates labeled;
• mechanical review work completed and business judgment still needed;
• one simple next-use command and a short chronological log.

Never invent token savings, harness evidence, or verification results.