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TikTok 监控订阅推送工具 — 订阅关键词、指定账号与话题，每日早上 9:00 自动拉取昨日时间窗内新内容（关键词新爆款视频 / 账号新作品 / 话题新作品），按订阅项分组生成 HTML 日报推送，无需手动操作。支持三类订阅项批量管理（总上限 100 项）、按日期过滤翻页、互动数据（播放/点赞/评论/分享）展示与 AI 总结。当用户需要订阅 TikTok 关键词、追踪指定账号新作品、监控话题热度变化、获取 TikTok 昨日内容日报时使用。触发词：TikTok订阅、TikTok监控、TikTok日报、TikTok关键词订阅、TikTok账号订阅、TikTok话题订阅、TikTok订阅推送、监控订阅推送。

redfox-data/redfox-community Agent Skills author: redfox-data 6 files · 2 scripts body ≈ 2 171 tokens Open the sourcegithub.com↗ analyzed 6 d ago

TikTok 监控订阅推送工具 — 订阅关键词、指定账号与话题，每日早上 9:00 自动拉取昨日时间窗内新内容（关键词新爆款视频 / 账号新作品 / 话题新作品），按订阅项分组生成 HTML 日报推送，无需手动操作。支持三类订阅项批量管理（总上限 100…

As a process F 33/100 · No process to follow — References files that are not bundled: 作品链接

ProcedureMarketingtype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
F
33/100
No process to follow
References files that are not bundled: 作品链接
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 6. 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")
  • warning missing-ref reference to a missing file: 作品链接

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: 作品链接
  • 0Tools and files. 1 referenced file(s) missing: 作品链接
  • 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
  • 100Steps. 42 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2171 tokens
  • low 12 top-level sections: this looks like several domains in one skill

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 307: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 42 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.