BC alibabacloud-mtr-network-diagnosis-customer
Public network MTR diagnosis tool. Supports both manual guidance and Cloud Assistant automation modes. Manual mode guides users to run MTR tools locally (macOS/Linux/Android/Windows) and analyze result screenshots. Automated mode remotely executes mtr/ping/curl diagnostics on ECS instances via Alibaba Cloud Cloud Assistant. Applicable to public network access failures, high latency, packet loss, SLB health check failures, NAT outbound packet loss, etc. Use this skill when users encounter network connectivity issues, need to troubleshoot public network link quality, or analyze MTR screenshots.
Public network MTR diagnosis tool.
As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 3
✓ No critical or high findings
Medium and low: 3
-
low Exfiltration
net-credential-usescripts/check-write-operation.sh:74Credential used in a network call (verify the destination is the intended service) (detector / deny-list definition)DECISION_REASON="Detected remote command execution API call (${keyword}). This will execute a script remotely on the ECS instance and requires user confirmation."detector -
low Exfiltration
net-credential-usescripts/check-write-operation.sh:77Credential used in a network call (verify the destination is the intended service) (detector / deny-list definition)DECISION_REASON="Detected write operation API call (${keyword}). This may modify cloud resource configuration and requires user confirmation."detector -
low Dangerous commands
cmd-privilegescripts/mtr_common.py:331Privilege escalation / world-writable permissions (string literal in code, not executed)"linux": "curl -fsSL https://aliyuncli.alicdn.com/aliy…tgz | tar xz && sudo mv aliyun /usr/local/bin/",
code literal
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6262 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6262 tokens
- 85Steps. 64 steps, 2 vague phrases
- 100Failures and branches. 5 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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
- -31 of 5 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 599: enough signal without eating the budget
- +4Structure: 45 headings
- +3Step-by-step instructions: 64 items
- +4Has examples (21 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.