Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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Practical mastering steps for TTS audio: cleanup, loudness normalization, alignment, and delivery specs.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
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Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
中文简介正在准备中。当前显示原文说明。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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Redact text from PDF documents for blind review anonymization
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Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
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Analyze failed GitHub Action jobs for a pull request.
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Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
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