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Practical mastering steps for TTS audio: cleanup, loudness normalization, alignment, and delivery specs.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/benchflow-ai/skillsbench --skill text-to-speechコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SkillsBench contribution workflow. Use when: (1) Creating benchmark tasks, (2) Understanding repo structure, (3) Preparing PRs for task submission.
SkillsBench task authoring — walk a contributor from idea to submission-ready task following CONTRIBUTING.md and the task-implementation rubric. Use when the user wants to create a new SkillsBench task, scaffold a task from an existing workflow (notebook, Excel workbook, document, dataset), convert a prompt or a benchmark item into a SkillsBench task, write skills for a task, or prepare a SkillsBench PR. Pairs with `task-review` (run that as a self-check before submitting).
SkillsBench task PR review — classifies the task track (standard / research / multimodal), runs static policy checks against the track-specific rubric, benchmarks the task across oracle plus Claude and Codex (with and without skills), audits trajectories for cheating and skill invocation, and produces a `pr-N-task-timestamp-run.txt` review report alongside a `prN.zip` bundle of trajectories. Use when reviewing a SkillsBench task PR (by number, branch, or local task path), when the user asks to review a task, run benchmarks on a PR, audit a submission, classify a task as research or multimodal track, or prepare a comment to post on a SkillsBench PR.
SKILL.md を表示中
SOC 職業分類に基づく
| name | text-to-speech |
| 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.