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rulesgen-dsl-authoring

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

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

tdspora

tdspora/tdm_rulesgen

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

SKILL.md

readonly

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.

المزيد من هذا المستودع

نفس المستودع

rulesgen-docs-publish

tdspora/tdm_rulesgen

Use when adding, renaming, reordering, or linking Rulesgen public docs that flow to the tdm-docs Docusaurus site and tdspora.ai. Covers the import pipeline, sidebar/.order mechanics, the "link locally until published" rule, and the lychee link-check and docs-contract validation battery.

2026-07-020

rulesgen-docs-authoring

tdspora/tdm_rulesgen

Use for authoring or updating Rulesgen documentation — public docs (`docs/public/`), repo-root design docs, contributor docs, sample READMEs, and the glossary. Produces testable, glossary-aligned, link-integral docs.

2026-05-280

rulesgen-docs-testing

tdspora/tdm_rulesgen

Use for testing Rulesgen documentation — executes Markdown code fences via Sybil, validates link integrity, lints glossary usage, and cross-checks docs against Settings, schemas, and routes.

2026-05-280

rulesgen-implement-feature

tdspora/tdm_rulesgen

Use for implementing production-grade Rulesgen features or non-trivial code changes.

2026-05-280

rulesgen-validation-debugging

tdspora/tdm_rulesgen

Use for Pydantic v2 validation errors, DSL parser/validator rejections, and API request validation issues in Rulesgen.

2026-05-280

rulesgen-api-debugging

tdspora/tdm_rulesgen

Use for FastAPI route, middleware, lifespan, dependency injection, or Problem Details response issues in Rulesgen.

2026-05-270

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.