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rulesgen-dsl-authoring

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Atualizado28 de maio de 2026 às 09:05

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

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Fonte

tdspora

tdspora/tdm_rulesgen

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

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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

Identify the target column(s), input data shape, and whether the rule is deterministic or LLM-assisted.
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.
Compile and validate locally via rulesgen.library (or uv run pytest-based fixtures) before sending to the LLM gateway.
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.
Never embed:
- Real customer data.
- Real credentials.
- Real LLM endpoint URLs. in committed sample rules, prompts, or test fixtures.
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.

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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

Identify the target column(s), input data shape, and whether the rule is deterministic or LLM-assisted.
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.
Compile and validate locally via rulesgen.library (or uv run pytest-based fixtures) before sending to the LLM gateway.
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.
Never embed:
- Real customer data.
- Real credentials.
- Real LLM endpoint URLs. in committed sample rules, prompts, or test fixtures.
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.