AC ai-multilingual-dubbing
Create multilingual voice-over audio from prepared scripts for videos, product launches, e-learning, training libraries, creator content, and international campaigns. This AI multilingual dubbing workflow organizes every market and segment, helps choose a locale-ready voice, pilots pronunciation and timing, and delivers reviewable narration by language. Use it for AI video dubbing audio, AI video translation audio, video localization voice-over, training video localization, global marketing narration, text to speech in multiple languages, or Chinese, English, and Japanese dubbing. Receive narration tracks organized by language, market, and segment, ready for localization editing, lip-sync production, training libraries, and campaign assembly.
Create multilingual voice-over audio from prepared scripts for videos, product launches, e-learning, training libraries, creator content, and international…
As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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 58/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 5 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2068 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 752: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (1 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.