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视频号爆款拆解流水线。给一条或多条视频号分享链接,自动下载视频→提取音频→Whisper转录文案→提取元数据(标题/标签/互动数据/账号信息),然后按分析框架拆解爆款逻辑(标题公式/标签漏斗/内容结构/互动数据/可复用策略),输出综合HTML报告。触发词:视频号拆解、爆款分析、视频号文案提取、视频号账号分析、sph拆解、视频号爆款。

ClawHub Agent Skills author: mikogeyu-cell v1.0.0 MIT-0 5 files body ≈ 773 tokens Open the sourceclawhub.ai analyzed 3 d ago

视频号爆款拆解流水线。给一条或多条视频号分享链接,自动下载视频→提取音频→Whisper转录文案→提取元数据(标题/标签/互动数据/账号信息),然后按分析框架拆解爆款逻辑(标题公式/标签漏斗/内容结构/互动数据/可复用策略),输出综合HTML报告。触发词:视频号拆解、爆款分析、视频号文案提取、视频号账号分析、sph拆…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentInfrastructureMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
D
46/100
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 description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 773 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
  • +2Single-language instructions
  • +3Description length 167: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
The skill is a disclosed WeChat Channels video analysis pipeline that downloads user-provided videos, transcribes them locally, and writes reports without evidence of hidden exfiltration or destructive behavior.
LLM: benign (high) · VirusTotal: · 13 Aug 2026