BD mentor
Turn any public figure into your private AI mentor. Give a name — auto-collect their real posts, speeches, and content from social platforms, extract their thinking framework and communication style, generate a MENTOR.md that lets AI think and advise like them. Not generic advice — advice filtered through THEIR values, THEIR experience, THEIR way of seeing the world. 把任何公众人物变成你的私人 AI 导师。说一个名字，自动从社交平台采集 TA 的真实发言和内容，提取思维框架和表达风格，生成 MENTOR.md 让 AI 像 TA 一样思考和给建议。不是通用建议——是经过 TA 的价值观、TA 的经验、TA 的世界观过滤过的建议。
As a process D 46/100 · Unfinished process — 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.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
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high Exfiltration
intent-browser-credential-storeguides/VIDEO_SUBTITLE.md:199Accesses a browser credential / cookie storeyt-dlp --cookies-from-browser chrome --proxy http://127.0.0.1:7897 \
Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "depends"
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 (web) that frontmatter does not declare
- 100Steps. 61 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2195 tokens
- 100Running it twice. No mutating operations
- 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
- -233 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 503: enough signal without eating the budget
- +4Structure: 40 headings
- +3Step-by-step instructions: 61 items
- +4Has examples (16 code blocks)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.