BD 多平台生成视频导演
多平台生成视频导演技能。将一张或多张参考图片或文字描述转化为可执行的视频方案、平台适配 Prompt、负向 Prompt、分镜设计、运动参数，并在有可用视频生成工具时直接生成视频。适用于：图生视频、文生视频、图片转视频、海报动效、首尾帧视频、产品展示动画、人物照片动态化、节日宣传视频、社交媒体短视频、电影感镜头运动、视频配乐、语气词配音、字幕叠加、多镜头拼接，以及提到 Runway、Kling/可灵、即梦/Jimeng、Higgsfield、Pika、Luma、Hailuo/海螺、Veo 等平台的请求。根据画面内容与用户目标自动选择或排序平台，不强制指定单一提供商。注意：本技能仅处理视频生成，不处理图片生成（用 ImageGen）、3D模型或模板特效（用3D模型与视频特效技能）。
多平台生成视频导演技能。将一张或多张参考图片或文字描述转化为可执行的视频方案、平台适配 Prompt、负向…
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 41/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (多平台生成视频导演) differs from the folder (multi-platform-video-director)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 78 steps
- 100Execution cost. Instruction body is 2029 tokens
- 100Running it twice. No mutating operations
- low 13 top-level sections: this looks like several domains in one skill
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 344: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 78 items
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.