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BD wangcut

秦丝智能视频剪辑APP的API集成，用于创建和管理AI视频剪辑任务。 TRIGGER when: 用户请求创建视频、生成视频剪辑、查看视频任务列表、下载剪辑结果、等待任务完成、配置旺剪账号。 触发词: "创建视频"、"视频剪辑"、"生成视频"、"查看任务"、"下载视频"、"等待视频"、"配置旺剪"、"旺剪账号"、"wangcut"。 支持功能: (1) 根据文案创建视频剪辑任务，自动随机选择7个素材 (2) 查看任务列表和详情 (3) 等待任务完成并下载视频到本地 (4) 自动检测配置状态并引导用户配置账号密码

ClawHub Agent Skills author: HuangJT v1.0.1 MIT-0 5 files body ≈ 676 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 44/100 · Unfinished process — weak spots: steps, result and completion, 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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
74
Run on models
none yet
Process rating
D
44/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

    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-password-literal scripts/wangcut_api.py:115
      Hard-coded password / key literal (may be an example) (quoted — discussed, not commanded)
      password: 密码（明文，会自动MD5加密存储）
      quoted

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 44/100

    • 0Steps. Prose only: no discrete steps
    • 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
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 676 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 258: enough signal without eating the budget
    • +4Structure: 12 headings
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

    External checks

    ClawHub: suspicious
    This Wangcut skill is purpose-built for video automation, but its setup flow asks users to share an account password in chat and stores it in a local config file.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026