القائمة
Practical mastering steps for TTS audio: cleanup, loudness normalization, alignment, and delivery specs.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
World-class Java and Spring Boot development skill for enterprise applications, microservices, and cloud-native systems. Expertise in Spring Framework, Spring Boot 3.x, Spring Cloud, JPA/Hibernate, and reactive programming with WebFlux. Includes project scaffolding, dependency management, security implementation, and performance optimization.
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.
This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration
Extract, normalize, mix, and process audio tracks - audio manipulation and analysis
Convert media files between formats - video containers, audio formats, and codec transcoding
| name | TTS Audio Mastering |
| description | Practical mastering steps for TTS audio: cleanup, loudness normalization, alignment, and delivery specs. |
This skill focuses on producing clean, consistent, and delivery-ready TTS audio for video tasks. It covers speech cleanup, loudness normalization, segment boundaries, and export specs.
Choose a TTS engine based on deployment constraints and quality needs:
Key rule: Always confirm the native sample rate of the generated audio before resampling for video delivery.
Apply lightweight processing to avoid common artifacts:
Recommended FFmpeg pattern (example):
Target loudness depends on the benchmark/task spec. A common target is ITU-R BS.1770 loudness measurement:
Recommended workflow:
ebur128 (or equivalent meter).loudnorm) as the final step after cleanup and timing edits.When stitching segment-level TTS into a full track:
Sync guideline: keep end-to-end drift small (e.g., <= 0.2s) unless the task states otherwise.