SKILLEMALL.ai
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BD caption-qa

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.

The skillemall take

Promises to validate subtitles against Netflix, BBC, and DCMP standards: check reading speed, line length, time on screen, gaps and overlaps, then retime before rewording. Files contain one instruction at 1974 tokens, no scripts or sandbox tests. Quality scores 79, process 38, no critical findings. Instruction exists but was never run against actual subtitles or tested for actual retiming output. Install if you already know the standards and can debug the logic yourself—treat it as reference material rather than a working tool.

github/awesome-copilot Agent Skills author: github 1 file body ≈ 1 974 tokens Open the sourcegithub.com↗ analyzed 4 h ago

Check SRT or WebVTT captions and subtitles against published standards (Netflix English, BBC subtitle guidelines, DCMP Captioning Key) for reading speed, line…

As a process D 38/100 · Unfinished process — References files that are not bundled: \d{1,3}

ReferenceMedia and videotype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
38/100
Unfinished process
References files that are not bundled: \d{1,3}
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: \d{1,3}

Process rating: all ten parameters 38/100

Will not run. References files that are not bundled: \d{1,3}
  • 0Tools and files. 1 referenced file(s) missing: \d{1,3}
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 100Steps. 24 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1974 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 444: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.