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Get Started Free →This skill should be used when the user asks to "enhance srt subtitles", "optimize srt with original script", "fix srt typos from markdown", "compare srt with origin.md", "improve subtitle accuracy", "add spaces to srt", "優化 srt 逐字稿", "比對原稿修正字幕", or needs to refine SRT subtitle files by comparing them with an original markdown reference document.
.claude/skills/bilal140202-srt-enhancer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 287% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 98% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 84% | 0% |
This skill provides an AI-driven workflow for enhancing SRT subtitle files by comparing them with an original reference document (origin.md). The enhancement process corrects typos, standardizes proper nouns, and adds proper spacing around English text and numbers while preserving the original timeline and structure.
Enhance SRT subtitle files by:
origin.md)Use this skill when:
origin.md as the source of truth for spelling and terminology ) around English words and numbers in Chinese textFind origin.md in the same directory as the input SRT file:
origin.md in the SRT file's directoryLoad and parse the input SRT file:
Analyze origin.md to extract:
Reference references/enhancement-rules.md for detailed extraction strategies.
For each subtitle segment:
origin.md using semantic matchingFor each identified issue:
origin.mdBefore finalizing each correction:
Save the enhanced SRT as enhanced.srt:
這是一個example範例這是一個 example 範例總共有3個步驟總共有 3 個步驟使用Python3.9版本使用 Python 3.9 版本machine learning (keep as is)123,456 (keep as is)Reference references/spacing-examples.md for comprehensive examples.
Use AI to match SRT content with origin.md:
Apply corrections in this order:
Before generating output:
enhanced.srtorigin.md:
markdown今天要介紹 Python 3.9 的新功能。首先是 match case 語句,這是一個強大的模式匹配工具。
Input SRT:
1
00:00:00,000 --> 00:00:05,000
今天要介紹Python3.9的新功能
2
00:00:05,000 --> 00:00:10,000
首先是match case語句這是一個強大的模式匹配工俱Output SRT (enhanced.srt):
1
00:00:00,000 --> 00:00:05,000
今天要介紹 Python 3.9 的新功能
2
00:00:05,000 --> 00:00:10,000
首先是 match case 語句,這是一個強大的模式匹配工具Changes Made:
Python, 3.9, match case工俱 → 工具 (typo fix from reference)origin.md that doesn't exist in SRTenhanced.srtorigin.md, leave unchangedreferences/enhancement-rules.md - Detailed rules for typo detection, proper noun extraction, and semantic matching strategiesreferences/spacing-examples.md - Comprehensive examples of spacing rules with edge casesTo enhance an SRT file with reference document:
origin.md in the same directory as the SRT fileorigin.md (proper nouns, terminology, correct spellings)enhanced.srtFocus on semantic understanding and conservative corrections. The goal is to refine existing subtitle content using the reference document as a guide, not to rewrite or add new content. Maintain the integrity of the original SRT structure while improving accuracy and formatting.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,854 | 2,652 | -55% | 1 | 1 | 0% | 891 | 2,131 | +139% | 0 | 0 | — |
case-02 | pass→pass | 8,477 | 4,575 | -46% | 1 | 1 | 0% | 1,363 | 2,535 | +86% | 0 | 0 | — |
case-03 | pass→pass | 9,577 | 3,849 | -60% | 1 | 1 | 0% | 1,563 | 2,490 | +59% | 0 | 0 | — |
case-04 | pass→pass | 7,322 | 2,483 | -66% | 1 | 1 | 0% | 1,107 | 2,225 | +101% | 0 | 0 | — |
case-05 | fail→pass | 9,349 | 1,992 | -79% | 1 | 1 | 0% | 1,379 | 2,081 | +51% | 0 | 0 | — |
case-06 | pass→pass | 10,197 | 3,585 | -65% | 1 | 1 | 0% | 1,546 | 2,398 | +55% | 0 | 0 | — |
case-07 | pass→pass | 10,733 | 5,825 | -46% | 1 | 1 | 0% | 1,774 | 2,802 | +58% | 0 | 0 | — |
case-08 | pass→pass | 11,175 | 6,098 | -45% | 1 | 1 | 0% | 1,769 | 2,873 | +62% | 0 | 0 | — |
case-09 | pass→pass | 7,023 | 2,770 | -61% | 1 | 1 | 0% | 955 | 2,212 | +132% | 0 | 0 | — |
case-10 | pass→pass | 3,079 | 2,569 | -17% | 1 | 1 | 0% | 498 | 2,241 | +350% | 0 | 0 | — |
case-11 | pass→pass | 3,891 | 4,030 | +4% | 1 | 1 | 0% | 631 | 2,487 | +294% | 0 | 0 | — |
case-12 | fail→fail | 6,407 | 3,621 | -43% | 1 | 1 | 0% | 1,005 | 2,423 | +141% | 0 | 0 | — |
case-13 | fail→pass | 4,577 | 3,394 | -26% | 1 | 1 | 0% | 603 | 2,336 | +287% | 0 | 0 | — |
case-14 | fail→pass | 16,066 | 6,667 | -59% | 1 | 1 | 0% | 2,688 | 2,756 | +3% | 0 | 0 | — |
case-15 | pass→pass | 3,960 | 2,142 | -46% | 1 | 1 | 0% | 601 | 2,060 | +243% | 0 | 0 | — |
case-16 | fail→pass | 7,761 | 2,800 | -64% | 1 | 1 | 0% | 1,137 | 2,251 | +98% | 0 | 0 | — |
case-17 | fail→pass | 10,041 | 6,575 | -35% | 1 | 1 | 0% | 1,535 | 2,824 | +84% | 0 | 0 | — |
case-18 | pass→pass | 12,144 | 3,877 | -68% | 1 | 1 | 0% | 1,806 | 2,532 | +40% | 0 | 0 | — |
case-19 | fail→pass | 8,300 | 5,386 | -35% | 1 | 1 | 0% | 1,221 | 2,652 | +117% | 0 | 0 | — |
case-20 | pass→pass | 3,340 | 2,776 | -17% | 1 | 1 | 0% | 433 | 2,110 | +387% | 0 | 0 | — |
case-21 | fail→fail | 12,399 | 7,152 | -42% | 1 | 1 | 0% | 2,291 | 3,130 | +37% | 0 | 0 | — |
case-22 | fail→fail | 16,034 | 5,977 | -63% | 1 | 1 | 0% | 2,638 | 2,669 | +1% | 0 | 0 | — |
case-23 | fail→fail | 10,033 | 8,358 | -17% | 1 | 1 | 0% | 2,119 | 3,225 | +52% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +26 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.