BD seedancer
AIGC 影视导演操作系统——从剧本解析到预生产资产到多镜头序列项目到完整制片管线的端到端工作流。整合 P0-P2 预生产管线 + 五大硬门系统 + 场景原型路由 + 摄影机-情绪同步 + 表演微节拍目录 + JSON API 输出模式 + 光源规则系统 + CINEDANCE 16-block + LIRA 图像提示词 + ACTING 表演 + GEO 空间锁定 + Style Prefix + SCALE LAW + AI 导演 + 失败诊断。基于 Seedance 2.5 / Kling 3.0 / Veo 3.1 / Wan 3.0（30秒直出/50素材/4K/局部编辑/白模绿幕）。触发词：Seedance、即梦、视频生成、提示词、Seedancer、AIGC电影、短剧、AI短片。
AIGC 影视导演操作系统——从剧本解析到预生产资产到多镜头序列项目到完整制片管线的端到端工作流。整合 P0-P2 预生产管线 + 五大硬门系统 + 场景原型路由 + 摄影机-情绪同步 + 表演微节拍目录 + JSON API 输出模式 + 光源规则系统 + CINEDANCE 16-block + LIRA…
As a process D 49/100 · 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.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 56. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 7611 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "attribution"
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 4 mutating operations with no state check
- 70Execution cost. Instruction body is 7611 tokens
- 100Tools and files. No external tools needed
- 100Steps. 214 steps
- 100Consistency. Name and required fields are in place
- low 32 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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
- -283 emoji in the instructions: noise for the model
- -31 of 1 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 350: enough signal without eating the budget
- +4Structure: 134 headings
- +3Step-by-step instructions: 214 items
- +4Has examples (20 code blocks)
- +4Reference files are cited in the instructions (31 of 47)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.