AF seedance-2-prompt-engineering-video-gen
Design production English prompts for Seedance 2.0 then generate text-to-video or image-to-video on WeryAI (`SEEDANCE_2_0`), using bundled recipes (A–K), mode-to-JSON mapping, camera vocabulary, and pre-flight checklists. Use when you need JiMeng-grade prompt control translated to WeryAI submit-* flows with explicit pre-submit confirmation. SEO: Seedance 2.0 prompt engineering; Seedance text and image to video; recipe library.
As a process F 52/100 · Will not run — References files that are not bundled: url
How to improve
- The text references files that are not there: add them or drop the references.
- 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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: url
Process rating: all ten parameters 52/100
- 0Tools and files. 1 referenced file(s) missing: url
- 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
- 40Consistency. Frontmatter name (seedance-2-prompt-engineering-video-gen) differs from the folder (seedance-2-prompt-engineering-to-video)
- 50When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 42 steps, 1 vague phrases
- 100Failures and branches. 5 branches, has a failure section
- 100Execution cost. Instruction body is 3547 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 tags): a typed call is more reliable
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
- +1No license
- +2Single-language instructions
- +3Description length 430: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 42 items
- +4Has examples (0 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.