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AC ai-short-film-studio

低成本AI短剧/短片全流程制作技能。三套制作路线：①速创API+Grok Imagine直出视频（¥30-50/部）；②可灵Kling V3+Gemini Image图生视频（¥80-120/部）；③Google Flow Omni Flash免费批量文生视频（¥0/部，Chrome CDP自动化）。包含分镜脚本创作、角色参考图生成、Chrome CDP网页自动化批量生产、五层Prompt公式、视觉一致性管理、音频驱动剪辑、三层音频(旁白+SFX+BGM)、字幕叠加、审片迭代的全套SOP。适用于AI短片、短剧EP、预告片、科普视频等场景。

ClawHub Agent Skills author: 寒武纪智能Cambrian Intelligence v3.0.0 MIT-0 7 files body ≈ 4 563 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token references/sucuang_api.md:169
    High-entropy token-like string (may be an id, hash or a credential)
    - **SecretId**: AKID…yJU

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 54/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 4563 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 122 steps
  • 100Consistency. Name and required fields are in place
  • low 15 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
  • -229 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 272: enough signal without eating the budget
  • +4Structure: 71 headings
  • +3Step-by-step instructions: 122 items
  • +4Has examples (23 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The skill is mostly a disclosed video-production workflow, but it should be reviewed because it asks the agent to control a logged-in Chrome profile and includes unsafe cloud credential/storage guidance.
LLM: suspicious (high) · VirusTotal: · 22 Jun 2026