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Specialized debugging skill for distributed systems across multiple clusters, nodes, and network boundaries.
Passed 21 of 22 test cases with the skill, and 4 of 22 without it.
and 26 more in the full report
17 fixed4 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
Inter-cluster service discovery is showing stale IP mappings after a pod migration. Which auto-fix mechanism resolves stale endpoints?
| Checked | Without skill | With skill |
|---|---|---|
| Uses python main.py debug-distributed with auto-repair flag | Missed | Passed |
| Refreshes service discovery cache in remediation step | Missed | Passed |
The instructions your model reads when the skill loads.
This skill provides comprehensive debugging capabilities for distributed systems spanning multiple Kubernetes clusters, nodes, and network boundaries. It specializes in identifying and resolving issues that arise from the complexity of distributed architectures.
python main.py debug-distributed \
--clusters cluster-a,cluster-b,cluster-c \
--issue-type connectivity \
--time-range 30m
test cases passed, vs 4 / 22 without
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