AB huawei-cloud-cdn-abnormal-status-code-analysis
Diagnose CDN business abnormal HTTP status codes (4xx/5xx) using hcloud CLI. Discover and quantify 4xx/5xx volume, localize the exact status code and time window, fork edge-generated vs origin-generated via back-to-source status statistics, correlate top-N distribution, narrow root cause on CDN edge config or origin side, and取证 per-request access logs. Use this skill when the user wants to: (1) diagnose CDN abnormal 4xx/5xx status codes, (2) root-cause a 403/404/5xx spike on a CDN domain, (3) tell whether abnormal codes are generated by the CDN edge or returned by the origin, (4) troubleshoot CDN business exception status codes during daily inspection or incidents. Triggers include: 状态码异常, 业务异常码, 4xx, 5xx, 403, 404, 502, 503, 504, status code, abnormal status, CDN异常, 边缘/回源, 限流, status code analysis, edge vs origin
Diagnose CDN business abnormal HTTP status codes (4xx/5xx) using hcloud CLI.
As a process B 71/100 · Nearly there — weak spots: result and completion, running it twice
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 · 0
✓ No critical or high findings
Files scanned: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 71/100
- 30Running it twice. 13 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 19 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3439 tokens
- 100Progress reporting. Reports progress
- low 11 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (19 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
- +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 825: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -218 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Structure: 19 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (14 of 14)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.