BF cinematic-storyboard-generator
Generate professional, cinema-grade storyboard prompts for AI image/video generation platforms (LibTV/LibLib, 即梦, Seedance, Kling, etc.). Use when creating shot-by-shot visual prompts for short dramas, films, promotional videos, or any cinematic content. Triggers on: 写分镜, 生成分镜, 分镜提示词, storyboard, shot list, 镜头设计, 影视级提示词, 短剧分镜, AI生图提示词, AI视频提示词. Includes: seven essential elements framework, four iron rules (asset anchoring, light logic, material consistency, spatial coherence), quality checklist, scene-type-specific guidance (ancient/fantasy, modern, overseas formats), shot type reference, and transition words. Built from professional film cinematography standards — not generic prompt templates, but cinema-level methodology.
As a process F 44/100 · Will not run — References files that are not bundled: prompt-elements.md, scene-ancient.md
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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Generate professional, cinema-grade storyboard prompts for AI imag… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
missing-refreference to a missing file: prompt-elements.md - warning
missing-refreference to a missing file: scene-ancient.md
Process rating: all ten parameters 44/100
- 0Tools and files. 2 referenced file(s) missing: prompt-elements.md, scene-ancient.md
- 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
- 30Running it twice. 1 mutating operations with no state check
- 55Failures and branches. 1 branches
- 70When it triggers. States when to use, but not when not to
- 100Steps. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1348 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
- +1No license
- +2Single-language instructions
- +3Description length 733: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.