BA huawei-cloud-cdn-dns-resolution-diagnosis
Diagnose CDN domain DNS resolution issues using hcloud CLI and Python DNS probes. Query the CDN domain detail to obtain the expected CNAME, probe the actual DNS resolution of the domain via scripts/dns_resolve.py (dnspython) to collect resolved IP addresses, and verify the IP attribution against Huawei Cloud CDN via ShowIpInfo/v2 to determine whether the domain has been correctly resolved to Huawei Cloud CDN. Use this skill when the user wants to: (1) diagnose CDN DNS resolution failures, (2) check why a domain is not resolved to Huawei Cloud CDN, (3) verify DNS configuration for CDN domain access, (4) troubleshoot DNS anomaly or resolution timeout issues. Triggers include: "DNS解析诊断", "DNS异常", "域名解析失败", "CDN解析异常", "DNS配置", "DNS diagnosis", "resolution diagnosis". User utterance examples: "域名解析不到华为云CDN", "CNAME 配置了但没生效", "DNS 解析超时了", "解析出来的 IP 不是华为云的", "域名没解析到 CDN". Do NOT use for: HTTPS certificate errors, origin-pull failures, domain ownership verification, or any CDN/DNS configuration change (this skill is strictly read-only).
Diagnose CDN domain DNS resolution issues using hcloud CLI and Python DNS probes.
As a process A 80/100 · Runs to the end — weak spots: progress reporting
How to improve
- Shorten the description to 1024 characters.
- 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 · 0
✓ No critical or high findings
Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1045 chars, limit 1024 - warning
body-longSKILL.md body ≈ 6539 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "owner"
Process rating: all ten parameters 80/100
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Steps. 43 steps, 4 vague phrases
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6539 tokens
- 100Result and completion. Output format and completion criterion are stated
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- low 19 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (12 tags): a typed call is more reliable
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
- +3Description length 1044: 120–800 characters recommended
- -229 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 10 example trigger phrases
- +4Description says when NOT to use the skill
- +4Structure: 28 headings
- +3Step-by-step instructions: 43 items
- +3Output format is stated explicitly
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (12 of 12)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.