BA zhiyierxing-auto-phone
Product-grade deployment and operation skill for Zhipu AutoGLM-Phone / Open-AutoGLM.Use when the user wants a complete, runnable local setup: clone the GitHub repo onto the computer, prepare Android/HarmonyOS/iPhone devices, install ADB Keyboard, enable developer options and USB debugging, configure model endpoints, start the agent, verify deployment, and troubleshoot failures with exact user instructions.
As a process A 81/100 · Runs to the end — weak spots: result and completion
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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
- 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
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medium Exfiltration
net-redirectable-api-keyscripts/ensure_and_run_task.py:130Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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medium Exfiltration
net-redirectable-api-keyscripts/run_phone_task.py:34Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
Files scanned: 31. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "emoji"
Process rating: all ten parameters 81/100
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 147 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 8 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3586 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- 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
- -2localhost URLs: will not work for another user
- -33 of 24 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 409: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 147 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (3 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.