data-engineering-guide

Data engineering expertise for pipelines, schemas, data quality, SQL, lakehouse, and streaming. Use PROACTIVELY when the user discusses data pipelines, ETL/ELT, schema design, dimensional modeling, data quality checks, SQL optimization, dbt models, Spark jobs, Airflow DAGs, streaming pipelines, lakehouse architecture, or data contracts.

الانتقال إلى التثبيت

المستودع: luanmorenommaciel/agentspec
آخر نشاط في المصدر: ١٤ أبريل ٢٠٢٦ في ١٩:٠٨
لغة SKILL.md المكتشفة: الإنجليزية
النجوم: ٢٤٣
التفرعات: ١٢٦

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

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

يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.

npx skills add https://github.com/luanmorenommaciel/agentspec --skill data-engineering-guide

يبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.

تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

SKILL.md

تعليمات المصدر · معاينة للقراءة فقط

العودة إلى البحث العودة إلى التثبيت

sdd-define

Phase 1 of the SDD workflow: capture and validate requirements in one pass. Owns the full Define methodology — classify the input (BRAINSTORM document, meeting notes, email thread, conversation, or direct request), extract entities (problem, users, MoSCoW goals, success criteria, acceptance tests, constraints, out of scope, assumptions), gather technical and data engineering context, calculate the clarity score against the 12/15 gate, fill gaps with targeted questions, and generate the DEFINE document from the shared template with correct status transitions. Use when the user wants to capture requirements, define the feature, structure project scope, or compute a clarity score — "capture requirements", "define the feature", "clarity score", "Phase 1", /define. Do not use for open-ended exploration of ideas and approaches (that is sdd-brainstorm, Phase 0) or for architecture and technical specification (that is sdd-design, Phase 2).

2026-07-15

sdd-design

Create the architecture and technical specification for a defined feature — Phase 2 of the SDD workflow. Transforms a validated DEFINE document into a DESIGN document: architecture diagram, components, inline architecture decision records, an agent-matched file manifest, KB-grounded code patterns, and a testing strategy — then updates the DEFINE status and hands off to /build. Use when requirements are captured and technical design is needed — "design the architecture", "technical design", "Phase 2", or /design pointed at a DEFINE_*.md file. Not for capturing or clarifying requirements (that is sdd-define, Phase 1) and not for implementing code (that is sdd-build, Phase 3).

2026-07-15

sdd-iterate

Update an existing SDD phase document (BRAINSTORM/DEFINE/DESIGN) mid-stream with full cascade awareness: classify the change (additive, modifying, removing, architectural), apply it with a version bump and change note, walk the cascade rules for downstream impact, and confirm with the user before applying any cascading edits. Owns the SDD change-management methodology: cascade rules, version-tracking and status-field obligations, and the iterate-vs-restart thresholds. Use when the user wants to update the requirements or design mid-stream — "requirements changed", "the design needs to change", "cascade the change" — or any existing phase document must change after downstream work has started. Do not use for a brand-new feature or a fundamentally different problem: a different problem, different users, or a change beyond roughly half the document calls for sdd-brainstorm/sdd-define instead of an iterate.

2026-07-15

عرض الكل من هذا المستودع

name	data-engineering-guide
description	Data engineering expertise for pipelines, schemas, data quality, SQL, lakehouse, and streaming. Use PROACTIVELY when the user discusses data pipelines, ETL/ELT, schema design, dimensional modeling, data quality checks, SQL optimization, dbt models, Spark jobs, Airflow DAGs, streaming pipelines, lakehouse architecture, or data contracts.