SKILLEMALL.ai
GitHub ↗ RU Sign in

CD tiktok-account-analyzer

TikTok账号深度分析，输入TikTok主页链接、handle、secUserId或昵称即可定位账号，一次输出账号基础信息、主页作品榜与喜欢作品透视三类数据。基于红狐数据，作品含播放、点赞、评论、收藏与分享数据及作品链接，输出六维量化诊断（100分制评分+风险预警）、爆款作品拆解、喜欢偏好解读与账号诊断总结，支持翻页与 HTML 报告导出。适用于 TikTok 达人背调、竞品账号拆解、合作前评估时使用。触发词：TikTok账号分析、TikTok账号诊断、TikTok达人背调、TikTok账号深度分析、TikTok博主分析、TikTok账号数据、TikTok竞品分析。

redfox-data/redfox-community Agent Skills author: redfox-data 9 files · 4 scripts body ≈ 688 tokens Open the sourcegithub.com↗ analyzed 6 d ago

TikTok账号深度分析，输入TikTok主页链接、handle、secUserId或昵称即可定位账号，一次输出账号基础信息、主页作品榜与喜欢作品透视三类数据。基于红狐数据，作品含播放、点赞、评论、收藏与分享数据及作品链接，输出六维量化诊断（100分制评分+风险预警）、爆款作品拆解、喜欢偏好解读与账号诊断总结，支持翻…

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

AnalyzerMarketingtype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
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: 9. 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 (python) that frontmatter does not declare
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 688 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
  • +4No input/output examples
  • -34 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 287: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 32 items
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.