CC douyin-automation
Puppeteer-based Douyin (TikTok China) video uploader. Automate login, upload and publish videos to Douyin Creator Platform using headless browser. 抖音视频自动化发布工具。Use when uploading video to Douyin, logging in to Douyin, or checking Douyin login status.
Puppeteer-based Douyin (TikTok China) video uploader.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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
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high Exfiltration
intent-browser-credential-storeskill-card.md:31Accesses a browser credential / cookie storeMitigation: Keep the skill directory private, do not commit douyin-cookies.json or chrome-user-data, and use the clear command to remove saved login data when it is no longer needed.
Medium and low: 1
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low Dangerous commands
cmd-privilegedouyin-uploader.js:405Privilege escalation / world-writable permissions (string literal in code, not executed)'--disable-setuid-sandbox',
code literal
Files scanned: 7. 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 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. Tools declared in frontmatter
- 100Steps. 12 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1109 tokens
- 100Running it twice. Mutating operations check current state
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
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +3Description length 249: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 12 items
- +4Has examples (10 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.