SKILLEMALL.ai

AC douyin-subscribe-analysis

抖音账号订阅追踪 — 通过抖音号订阅账号,Agent 每日 9:00 自动拉取并生成报告。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、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。 Use when: 用户需要搜抖音视频、查抖音热榜/热搜、抓取博主作品列表、分析视频评论区、做短视频选题/竞品分析/舆情监控/热点追踪/爆款挖掘/抖音运营数据分析。 Do NOT use when: 平台非抖音(小红书/B站/微博→对应技能)、需登录态或私密数据、仅要文案创作不需数据查询、既无关键词也无可识别链接且目标不明(先追问)。 触发词:抖音搜索、抖音热榜、抖音评论、抖音博主、抖音竞品分析、短视频选题、抖音舆情、抖音数据分析、douyin search、douyin analytics、douyin comment。

ClawHub Agent Skills author: why20261 v0.1.0 MIT-0 35 files body ≈ 1 113 tokens Open the sourceclawhub.ai analyzed 12 h ago

抖音账号订阅追踪 — 通过抖音号订阅账号,Agent 每日 9:00 自动拉取并生成报告。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、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 secuid 获取公开作品列表; (4)…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerAI and agentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
57/100
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

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 57/100

    • 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
    • 50When 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 1113 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
    • +3Output format is not stated: the model decides each time
    • -221 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 458: 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: 91.

    External checks

    ClawHub: suspicious
    The skill largely matches its Douyin data-analysis purpose, but it handles an API token insecurely and automatically saves collected public user/comment data locally, so it needs review before installation.
    LLM: suspicious (high) · 16 Sept 2026