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BD tiktok-android-bot

使用 ADB 自动化 TikTok 互动。支持 AI 智能评论（Claude/GPT-4/OpenRouter 视觉分析）、搜索话题、评论、点赞、收藏视频、发布内容。无需网页抓取，无 CAPTCHA，智能 UI 识别实现 100% 成功率。

ClawHub Agent Skills author: MoLin-g v1.0.0 MIT-0 17 files body ≈ 1 279 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMarketingAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
72
Run on models
none yet
Process rating
D
41/100
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv setup.py:259
    Reads a .env file
    with open(".env", 'r') as f:
  • low Exfiltration read-dotenv setup.py:272
    Reads a .env file
    with open(".env", 'w') as f:

Files scanned: 17. 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 41/100

  • 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
  • 40Consistency. Frontmatter name (tiktok-android-bot) differs from the folder (tiktok-android-720p)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 58 steps
  • 100Execution cost. Instruction body is 1279 tokens
  • 100Running it twice. No mutating operations
  • low 14 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 120: enough signal without eating the budget
  • +4Structure: 41 headings
  • +3Step-by-step instructions: 58 items
  • +4Has examples (18 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This skill does what it claims by automating TikTok through Android ADB, but it has review-worthy risks: it can post publicly, upload screenshots to AI providers, and delete videos from a phone camera folder without strong safeguards.
LLM: suspicious (high) · VirusTotal: · 29 May 2026