SKILLEMALL.ai

AB youtube-caption-studio

Turn a YouTube link or a pasted transcript into a Chinese spoken script and a remake structure. This video captions and caption extract workflow reads YouTube captions and optional comments, or works from the transcript you already copied, then writes spoken Chinese with the remake beats that follow the original. Use it for video captions, caption extract, YouTube captions, and YouTube transcript work when you need a spoken remake from what the video already said.

ClawHub Agent Skills author: beatra-ai v0.1.3 MIT-0 16 files body ≈ 1 866 tokens Open the sourceclawhub.ai analyzed 2 d ago

Turn a YouTube link or a pasted transcript into a Chinese spoken script and a remake structure.

As a process B 66/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureYouTubeMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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: 16. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 66/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 14 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1866 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
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 468: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (10 of 10)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    The skill does the advertised caption-to-Chinese-script work, but it also asks for broad Beatra account authority and silently updates its own package by default.
    LLM: suspicious (high) · 28 Aug 2026