SKILLEMALL.ai

BD qinghu-shortvideo-data-engine

青虎AI 短视频数据引擎:批量传入抖音、小红书、B 站的视频长链接,自动抓取每条视频的播放、点赞、分享、收藏、评论等全维度数据并导出 Excel,替代人工统计,用于监测自有与竞品带货视频的热度和转化表现。当用户要批量统计短视频数据、追踪视频每日表现、监测竞品视频、导出视频数据表、对比多条视频数据时必须触发。关键词:青虎AI、短视频数据、视频数据统计、抖音、小红书、B站、播放量、点赞、收藏、评论、Excel、竞品监测、数据引擎。

ClawHub Agent Skills author: AutoAGC v0.1.5 MIT-0 2 files body ≈ 1 245 tokens Open the sourceclawhub.ai analyzed 2 d ago

青虎AI 短视频数据引擎:批量传入抖音、小红书、B 站的视频长链接,自动抓取每条视频的播放、点赞、分享、收藏、评论等全维度数据并导出…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
43/100
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: 2. 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 "homepage"

Process rating: all ten parameters 43/100

  • 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. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 100Steps. 27 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1245 tokens

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 215: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
The skill appears to do its advertised video analytics job, but it needs review because it installs and upgrades an unpinned third-party command-line tool that handles tokens, paid jobs, and submitted video links.
LLM: suspicious (high) · 8 Sept 2026