caption-qa
Skill41k starsChanged 6 days agoMITView on GitHub
Check SRT or WebVTT captions and subtitles against published standards (Netflix English, BBC subtitle guidelines, DCMP Captioning Key) for reading speed, line length, line count, time on screen, gaps and overlaps, then fix failures by retiming before rewording. Use when asked to QA, lint or validate captions, check whether subtitles read too fast, prepare captions for Netflix, broadcast or educational delivery, or add a caption check to CI.
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 3 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
If the user names no target, use Netflix English (adult) and say that you did.
- Very specific: line 38
Read both timestamp forms: 00:00:01,000 (SRT) and 00:00:01.000 or 01.000 (WebVTT).
- Distinctive: line 47
Start the cue earlier if the previous cue ends well before it.
- Distinctive: line 52
Measure again: after fixing. A retime can create a new overlap or a gap under the minimum.
- Very specific: line 117
Run it as python3 check_captions.py captions/*.srt.
- Very specific: line 129
Only SRT and WebVTT are covered.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Caption QA
Captions fail delivery and accessibility review on numbers: characters a second, characters a line, time on screen and the gap between captions. Each standard publishes its own limits, and they differ, so "are these captions OK?" has no answer until you know where they are going. This skill picks the right set of limits, measures every cue the same way the published limits are written, and fixes failures without cutting the speaker's words.
When to Use This Skill
- The user has an
.srtor.vttfile and asks whether it is ready to ship, or why a platform rejected it. - The user asks whether captions or subtitles read too fast, or have lines that are too long.
- Captions are headed for Netflix, a broadcaster, or US educational media, and must meet that spec.
- The user wants caption checks in CI so a bad file fails the build.
Pick the Standard
If the user names no target, use Netflix English (adult) and say that you did.
| Standard | Reading speed | Line length | Lines | Time on screen | Gap between captions |
| Netflix English, adult | 20 characters a second | 42 characters | 2 | 5/6 s to 7 s | At least 2 frames; close gaps under 0.5 s to 2 frames |
| Netflix English, children's | 17 characters a second | 42 characters | 2 | 5/6 s to 7 s | Same as adult |
| BBC subtitle guidelines | 160-180 words a minute; aim for about 0.3 s a word | 37 characters (broadcast); online, 68% of the width of a 16:9 frame | 2 | - | If you leave a gap, at least 1 s, preferably 1.5 s |
| DCMP Captioning Key (US educational media) | 130 / 140 / 160 words a minute for lower / middle / upper level | - | 2 (preferred) | 40 frames (1 s 10 frames) to 6 s | - |
Sources: Netflix English (USA) Timed Text Style Guide, Netflix subtitle timing guidelines, BBC subtitle guidelines, DCMP Captioning Key: text and presentation rate. The BBC's per-word time and minimum gap are guidance, so report them as suggestions, not failures.
Measure Every Cue the Same Way
- Characters are letters, digits, spaces and punctuation in the displayed text. Don't count line breaks, formatting tags (
<i>,<b>,{\an8}) or the cue number. - Reading speed is characters divided by duration (out-time minus in-time), in seconds.
- Words a minute is words divided by duration, times 60. A word is a run of characters that holds a letter or a digit, so a lone dash isn't one.
- Gap runs from one cue's out-time to the next cue's in-time. Frame limits depend on the frame rate: 2 frames is 0.083 s at 24 fps but 0.067 s at 30 fps. Ask for the frame rate, or state that you assumed 24 fps.
- Overlap: a cue that ends after the next one starts is always a failure, under every standard.
- Read both timestamp forms:
00:00:01,000(SRT) and00:00:01.000or01.000(WebVTT). Skip WebVTTNOTE,STYLEandREGIONblocks, and treat cue settings after the timestamps (align:start) as part of the timing line.
Workflow
- Find the target (platform, broadcaster or audience level) and pick the standard from the table.
- Measure every cue, or run the command-line check below and read its output.
- Report with a count line first ("212 of 240 captions meet Netflix English, adult"). Then group failures by rule, with cue numbers and timecodes. Show the first few cues of each kind instead of hundreds of lines.
- Fix, keeping the words, in this order:
- Extend the out-time into the following gap, keeping the minimum gap.
- Start the cue earlier if the previous cue ends well before it.
- Split a long cue into two at a phrase boundary, each one meeting the minimum time on screen.
- Merge a flash cue (under the minimum time) into its neighbor.
- Re-break lines: break after punctuation or before a conjunction or preposition, and keep an article with its noun and a first name with a last name.
- Edit the wording only when the user asks for edited (not verbatim) captions.
- Measure again after fixing. A retime can create a new overlap or a gap under the minimum.
Reference Check Script
When the user wants a repeatable check (a folder of files, or CI), write this script, or adapt it to the chosen standard. It applies the Netflix adult limits from the table, prints each failing caption and exits with 1 when any caption fails, so it can gate a build. It needs only Python 3.
import re
import sys
# Netflix English, adult programs. Change these for another standard.
LIMITS = {"cps": 20, "cpl": 42, "lines": 2, "min_s": 5 / 6, "max_s": 7, "gap_frames": 2}
FPS = 24
T = r"(?:(\d+):)?(\d{1,2}):(\d{1,2})[,.](\d{1,3})"
TIMING = re.compile(T + r"\s*-->\s*" + T)
TAGS = re.compile(r"<[^>]+>|\{\\[^}]*\}")
def seconds(h, m, s, ms):
return int(h or 0) * 3600 + int(m) * 60 + int(s) + int((ms + "00")[:3]) / 1000
def read_cues(path):
text = open(path, encoding="utf-8-sig").read().replace("\r\n", "\n")
for block in re.split(r"\n[ \t]*\n", text):
lines = block.strip().split("\n")
for i, line in enumerate(lines[:3]):
m = TIMING.search(line)
if m:
body = [TAGS.sub("", t).rstrip() for t in lines[i + 1:]]
g = m.groups()
yield seconds(*g[:4]), seconds(*g[4:]), [t for t in body if t.strip()]
break
def check(path, lim=LIMITS, fps=FPS):
cues, failed = list(read_cues(path)), 0
for n, (start, end, text) in enumerate(cues, 1):
dur, chars, issues = end - start, sum(len(t) for t in text), []
if dur > 0 and chars / dur > lim["cps"]:
issues.append(f"{chars / dur:.1f} characters a second")
issues += [f"line {j} is {len(t)} characters"
for j, t in enumerate(text, 1) if len(t) > lim["cpl"]]
if len(text) > lim["lines"]:
issues.append(f"{len(text)} lines")
if dur < lim["min_s"] or dur > lim["max_s"]:
issues.append(f"on screen {dur:.2f} s")
if n < len(cues):
gap = cues[n][0] - end
if gap < 0:
issues.append(f"overlaps the next caption by {-gap:.2f} s")
elif gap * fps < lim["gap_frames"] - 0.01:
issues.append(f"gap of {gap * fps:.0f} frames at {fps} fps")
if issues:
failed += 1
print(f"{path} #{n} {start:.3f} -> {end:.3f}: " + "; ".join(issues))
print(f"{path}: {len(cues) - failed} of {len(cues)} captions pass")
return failed == 0
if __name__ == "__main__":
results = [check(p) for p in sys.argv[1:]]
sys.exit(0 if results and all(results) else 1)Run it as python3 check_captions.py captions/*.srt. For another standard, change LIMITS, and add a words-a-minute check (words divided by duration, times 60) for the BBC and DCMP. The script doesn't check the advice-level rules (chaining short gaps, the BBC's per-word time); report those separately.
In GitHub Actions:
- run: python3 check_captions.py captions/*.srtGotchas
- Meeting these numbers doesn't certify a file. Netflix and broadcasters also review style: italics, speaker labels, sound descriptions and position.
- The limits were read from each standard's own page in October 2026. Check the source before quoting a limit as current.
- Only SRT and WebVTT are covered. Convert ASS first with
ffmpeg -i subs.ass subs.srt, which keeps the timing and wraps the text in font tags that the character count ignores.
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On Hacker News · 5
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- Oct 26, 2026github/awesome-copilot agent skills - QAI — google.com
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In this repository475 other files it gives its agents.
- AGENTS.mdAGENTS.md
- skills/foundry-agentic-loop/evals/fixtures/improve-history/AGENTS.mdAGENTS.md
- skills/foundry-agentic-loop/evals/fixtures/rapid-locked/AGENTS.mdAGENTS.md
- skills/foundry-agentic-loop/evals/fixtures/sdd-deployed/AGENTS.mdAGENTS.md
- skills/foundry-agentic-loop/evals/fixtures/sdd-paused/AGENTS.mdAGENTS.md
- skills/foundry-agentic-loop/evals/fixtures/team-conventions/AGENTS.mdAGENTS.md
- skills/repo-standardizer/templates/AGENTS.mdAGENTS.md
- skills/setup-my-iq/assets/templates/AGENTS.mdAGENTS.md
- skills/repo-standardizer/templates/CLAUDE.mdCLAUDE.md
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Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

