SKILLEMALL.ai

BC douyin-comments-sentiment

抖音短视频运营增长助手用于回答「抖音短视频怎么运营」、分析评论反馈、看情绪和舆情风险、看用户画像和意图,适合内容创作者、运营、品牌方、电商在明确业务目标、内容材料或分析对象后调用。 它会结合视频/笔记链接、粘贴抖音分享链接等输入,整理关键上下文,并输出情绪和舆情判断、用户画像和意图信号、运营建议和回复建议,便于继续执行、复盘或交付。支持四大能力:(1) 关键词搜索视频/图文,可按点赞数、发布时间、视频时长、内容类型筛选排序;(2) 实时热榜查询,获取抖音热搜词条与热度数据;(3) 博主作品抓取,按主页链接或 sec_uid 获取公开作品列表;(4) 视频评论分析,按视频链接或 aweme_id 获取评论内容与互动数据。

ClawHub Agent Skills author: why20261 v0.1.0 MIT-0 35 files · 21 scripts body ≈ 1 139 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

抖音短视频运营增长助手用于回答「抖音短视频怎么运营」、分析评论反馈、看情绪和舆情风险、看用户画像和意图,适合内容创作者、运营、品牌方、电商在明确业务目标、内容材料或分析对象后调用。…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationMedia and videoAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 35. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 53 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1139 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
  • -221 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 313: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The skill appears to perform the advertised Douyin public-data lookups, but it automatically saves full result datasets to local log files without opt-in or retention controls.
LLM: suspicious (high) · VirusTotal: · 16 Sept 2026