AB alibabacloud-pai-dlc-job-diagnostics
PAI-DLC job diagnostics and health inspection. Queuing-stuck root cause analysis, failed-job localization, cluster health checks. Companion to the `alibabacloud-pai-dlc-job` skill (read-only — no writes). Triggers: "diagnose", "diagnose job", "job stuck", "why queuing", "queue stuck", "stuck in queue", "job failed", "failure reason", "healthcheck", "health check", "inspect job", "inspection".
PAI-DLC job diagnostics and health inspection.
As a process B 67/100 · Nearly there — weak spots: result and completion
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 67/100
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 60Steps. 44 steps, 5 vague phrases
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4314 tokens
- 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
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -212 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 12 example trigger phrases
- +3Description length 395: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 44 items
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.