DD xiaoyue-companion
小跃虚拟伴侣 - 使用智谱 AI 提供温暖的对话陪伴和静态图片分享
小跃虚拟伴侣 - 使用智谱 AI 提供温暖的对话陪伴和静态图片分享
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 skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
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.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 4
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high Dangerous commands
cmd-pipe-to-shellantinet-agentteams/CLOUD_STUDIO_DEPLOY.md:75Downloads and executes remote code from an unrecognised host (pipe to shell)bash <(curl -sSL https://raw.githubusercontent.com/agentscope-ai/AgentTeams/main/install/agentteams-install.sh)
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high Dangerous commands
cmd-pipe-to-shellantinet-agentteams/docs/track1/µ£¼գ¦þ+ôÕÉêþѵôþÑåզôÞ+ÉÞíîիû¢.md:66Downloads and executes remote code from an unrecognised host (pipe to shell)bash <(curl -sSL https://higress.ai/hiclaw/install.sh)
Medium and low: 2
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medium Exfiltration
net-redirectable-api-keyantinet-agentteams/core/common/llm_client.py:35Helper 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 Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bash(curl:*)allowed-tools: Bash(node:*) Bash(npm:*) Bash(openclaw:*) Bash(curl:*) Read Write
Files scanned: 79. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-shortdescription under 40 chars: too little signal for triggering - warning
description-no-whendescription 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
- 0When it triggers. No condition that starts the skill
- 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
- 40Consistency. Frontmatter name (xiaoyue-companion) differs from the folder (skill)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 36 steps
- 100Execution cost. Instruction body is 1207 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)
- +3Description length 33: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 24 headings
- +3Step-by-step instructions: 36 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.