AC kubernetes-encyclopedia
Kubernetes documentation-first workflow for Kubernetes-specific questions, troubleshooting, command planning, cluster operations, workload/resource behavior, networking, storage, scheduling, security, and diagnostics. Use when the request is clearly about Kubernetes itself: the `kubectl` CLI, Kubernetes API objects, pods, deployments, services, ingress, config maps, secrets, volumes, nodes, scheduling, RBAC, controllers, CRDs, Helm-free core Kubernetes behavior, or cluster/runtime behavior where Kubernetes- specific semantics matter. Do not use for generic Linux administration, generic container theory, generic cloud architecture, or Docker-only questions unless the Kubernetes layer is specifically what is being discussed or debugged.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, 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: 7. 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 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 55 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1809 tokens
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
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 744: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 55 items
- +4Reference files are cited in the instructions (3 of 3)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.