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BD 视频号作品透视

视频号单条作品的完整档案。丢入分享链接即可解析出标题、发布账号、发布时间、封面、可用媒体与互动数据；也支持 objectId/短链互相转换与分享链接生成。 适用场景：用户给一条视频号分享链接，想看这条视频的标题、账号、发布时间、封面、互动数据时使用。

ClawHub Agent Skills author: dunkong v1.0.1 MIT-0 5 files body ≈ 648 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

视频号单条作品的完整档案。丢入分享链接即可解析出标题、发布账号、发布时间、封面、可用媒体与互动数据；也支持 objectId/短链互相转换与分享链接生成。 适用场景：用户给一条视频号分享链接，想看这条视频的标题、账号、发布时间、封面、互动数据时使用。

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 0

✓ No critical or high findings

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

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"

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 (视频号作品透视) differs from the folder (wechat-channel-video-insight)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 14 steps
  • 100Execution cost. Instruction body is 648 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 125: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (4 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill mostly matches a paid WeChat video lookup workflow, but it also contains an under-disclosed local-file upload path that can send files to a remote service even though the advertised endpoints do not need uploads.
LLM: suspicious (high) · 4 Sept 2026