rulesgen-dsl-authoring

name	rulesgen-dsl-authoring
description	Use for authoring, reviewing, or correcting natural-language → Python generation DSL rules for Rulesgen.

Rulesgen DSL Authoring

Source of truth

• requirements/NL-to-Python-Generation-DSL.md — formal grammar and accepted forms.
• requirements/NL-to-Python-Generation-Overview.md — intent, motivating examples, expected pipeline behavior.
• src/rulesgen/compiler/parser.py and src/rulesgen/compiler/validator.py — actual accepted forms (treat as authoritative when docs disagree, and file a docs fix).
• samples/ — committed example rules.

Workflow

1. Identify the target column(s), input data shape, and whether the rule is deterministic or LLM-assisted.
2. Draft the rule using only accepted DSL constructs. Avoid:
   • Free imports.
   • Attribute access on builtins.
   • Dunder methods.
   • Network or filesystem I/O. The AST validator will reject these — and rightly so.
3. Compile and validate locally via rulesgen.library (or uv run pytest-based fixtures) before sending to the LLM gateway.
4. For LLM-assisted rules, verify:
   • Prompt template (src/rulesgen/infra/prompt_templates.py) handles the rule type.
   • The LLM endpoint is configured via env var, not hardcoded.
   • The semantic cache key derivation hashes any sensitive input.
5. Never embed:
   • Real customer data.
   • Real credentials.
   • Real LLM endpoint URLs. in committed sample rules, prompts, or test fixtures.
6. Add a unit test in tests/unit/test_compiler.py (positive + negative) for any new DSL construct.

Review checklist

• The rule parses with the current parser.
• The rule passes the validator (or is correctly rejected with a clear Problem Details response).
• The RuntimeSpec shape matches the documented invariants.
• The rule produces deterministic output on the local execution backend given the same input + seed.
• Sample data is small, synthetic, and committable.