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Get Started Free →Collect CAST AI diagnostic bundle for support tickets and troubleshooting. Use when preparing a support case, collecting agent logs, or building a diagnostic snapshot of cluster state. Trigger with phrases like "cast ai debug", "cast ai support bundle", "collect cast ai diagnostics", "cast ai logs".
.claude/skills/jeremylongshore-castai-debug-bundle/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 94% | 0% |
Capture enough evidence to reproduce a CAST AI failure without exporting credentials, Secret payloads, broad cluster inventory, application data, or unbounded logs.
Use Write to record incident ID, collector, start and end time, selected namespaces, commands, excluded data classes, redaction method, checksum method, retention, and recipients.
Use Bash(castctl:_) for version information, Bash(helm:_) for release metadata and redacted values, and Bash(kubectl:\) for component images and readiness. Do not collect Helm secrets, rendered Secret objects, service-account tokens, or kubeconfig contents.
Collect status, restart counts, recent warning events, selected resource descriptions, and logs constrained by component, time, and line count. Include metrics-server status when workload recommendations are involved and pending-pod reasons when node capacity is involved.
Use Read and Grep on repository-owned Terraform, Helm values, annotations, node templates, PDBs, and HPAs. Record source paths and commit identifiers. Prefer diffs and normalized summaries over raw state files.
Scan the bundle for API keys, authorization headers, tokens, Secret data, cloud account identifiers, email addresses, private endpoints, and application payloads. Replace values consistently so relationships remain debuggable.
Write a manifest of included files, omitted evidence, timestamps, tool versions, hashes, and known gaps. Open the sanitized files before sharing and require a second-person review for external support transfer.
Use Read and Grep for source and redaction review. Use Write only for sanitized bundle artifacts and the manifest. Use Bash(kubectl:_), Bash(helm:_), and Bash(castctl:\) for non-mutating, bounded inspection; never request Secret contents or stream logs indefinitely.
A workload-autoscaler incident includes component versions, a 15-minute warning-event window, PDB status, policy name, and sanitized logs. It excludes Secret objects, all-namespace inventory, Terraform state, and unrelated application logs.
| Failure | Response | | --------------------------------- | ------------------------------------------------------------------- | | Context or time window is unknown | Stop and resolve scope before collection | | A command would reveal a Secret | Omit it and document the evidence gap | | Redaction cannot preserve safety | Keep the bundle local and share a summary | | Bundle exceeds approved scope | Delete the excess from the exact bundle and regenerate its manifest |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 18,139 | 8,131 | -55% | 1 | 1 | 0% | 3,902 | 2,946 | -25% | 0 | 0 | — |
case-02 | fail→pass | 14,656 | 10,142 | -31% | 1 | 1 | 0% | 3,076 | 3,173 | +3% | 0 | 0 | — |
case-03 | fail→fail | 17,004 | 17,994 | +6% | 1 | 1 | 0% | 3,457 | 4,464 | +29% | 0 | 0 | — |
case-04 | fail→pass | 13,569 | 6,879 | -49% | 1 | 1 | 0% | 2,517 | 2,321 | -8% | 0 | 0 | — |
case-05 | pass→pass | 12,299 | 3,247 | -74% | 1 | 1 | 0% | 1,958 | 1,744 | -11% | 0 | 0 | — |
case-11 | fail→pass | 21,251 | 3,208 | -85% | 1 | 1 | 0% | 1,050 | 1,627 | +55% | 0 | 0 | — |
case-06 | fail→pass | 4,542 | 2,951 | -35% | 1 | 1 | 0% | 855 | 1,659 | +94% | 0 | 0 | — |
case-07 | pass→pass | 13,207 | 7,473 | -43% | 1 | 1 | 0% | 2,755 | 2,625 | -5% | 0 | 0 | — |
case-08 | pass→pass | 5,860 | 2,974 | -49% | 1 | 1 | 0% | 1,086 | 1,580 | +45% | 0 | 0 | — |
case-09 | pass→pass | 7,403 | 2,448 | -67% | 1 | 1 | 0% | 1,308 | 1,502 | +15% | 0 | 0 | — |
case-10 | fail→pass | 10,544 | 5,824 | -45% | 1 | 1 | 0% | 1,933 | 1,565 | -19% | 0 | 0 | — |
case-12 | pass→pass | 8,744 | 6,436 | -26% | 1 | 1 | 0% | 1,621 | 2,192 | +35% | 0 | 0 | — |
case-13 | pass→pass | 9,399 | 4,052 | -57% | 1 | 1 | 0% | 1,575 | 1,813 | +15% | 0 | 0 | — |
case-14 | fail→pass | 10,874 | 2,866 | -74% | 1 | 1 | 0% | 2,006 | 1,557 | -22% | 0 | 0 | — |
case-15 | fail→pass | 6,916 | 2,541 | -63% | 1 | 1 | 0% | 1,053 | 1,525 | +45% | 0 | 0 | — |
case-16 | fail→fail | 13,452 | 13,441 | -0% | 1 | 1 | 0% | 2,101 | 3,240 | +54% | 0 | 0 | — |
case-21 | pass→pass | 12,598 | 11,384 | -10% | 1 | 1 | 0% | 2,146 | 3,114 | +45% | 0 | 0 | — |
case-17 | fail→pass | 11,562 | 2,657 | -77% | 1 | 1 | 0% | 1,989 | 1,507 | -24% | 0 | 0 | — |
case-18 | fail→fail | 16,939 | 9,331 | -45% | 1 | 1 | 0% | 2,777 | 2,808 | +1% | 0 | 0 | — |
case-19 | pass→pass | 10,852 | 10,191 | -6% | 1 | 1 | 0% | 1,784 | 2,988 | +67% | 0 | 0 | — |
case-20 | pass→pass | 16,207 | 17,483 | +8% | 1 | 1 | 0% | 3,118 | 4,855 | +56% | 0 | 0 | — |
case-22 | pass→pass | 11,122 | 10,288 | -7% | 1 | 1 | 0% | 1,941 | 2,982 | +54% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +41 percentage points is the difference between those two pass rates over the 21 comparable cases.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.