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Get Started Free →Unified pipeline telemetry — collects per-stage performance metrics (CPU, memory, I/O) and diagnostic events into structured reports (JSON, text).
.claude/skills/docxology-telemetry/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | -73% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -58% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -54% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -29% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -73% | 0% |
This module provides unified pipeline telemetry for the infrastructure layer. It bridges StagePerformanceTracker resource metrics with DiagnosticReporter validation events into a single TelemetryCollector.
| Symbol | Type | Purpose | | --- | --- | --- | | TelemetryConfig | dataclass | Configuration with YAML-loadable from_dict() | | TelemetryCollector | class | Main lifecycle: start_stage() → end_stage() → finalize() | | StageTelemetry | dataclass | Per-stage record | | PipelineTelemetry | dataclass | Full report with warnings | | PerformanceWarning | dataclass | Individual anomaly record |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-09 | pass→pass | 6,421 | 1,388 | -78% | 1 | 1 | 0% | 1,133 | 407 | -64% | 0 | 0 | — |
case-15 | fail→pass | 12,840 | 2,512 | -80% | 1 | 1 | 0% | 2,021 | 548 | -73% | 0 | 0 | — |
case-20 | pass→pass | 20,752 | 11,575 | -44% | 1 | 1 | 0% | 2,500 | 2,305 | -8% | 0 | 0 | — |
case-01 | fail→pass | 20,663 | 8,066 | -61% | 1 | 1 | 0% | 4,190 | 1,778 | -58% | 0 | 0 | — |
case-02 | fail→pass | 17,177 | 8,220 | -52% | 1 | 1 | 0% | 3,755 | 1,715 | -54% | 0 | 0 | — |
case-03 | fail→pass | 15,395 | 7,259 | -53% | 1 | 1 | 0% | 2,482 | 1,762 | -29% | 0 | 0 | — |
case-08 | pass→pass | 9,315 | 1,716 | -82% | 1 | 1 | 0% | 1,774 | 488 | -72% | 0 | 0 | — |
case-04 | fail→pass | 10,371 | 1,938 | -81% | 1 | 1 | 0% | 1,985 | 540 | -73% | 0 | 0 | — |
case-05 | fail→pass | 10,139 | 1,910 | -81% | 1 | 1 | 0% | 1,663 | 523 | -69% | 0 | 0 | — |
case-06 | fail→pass | 7,924 | 2,188 | -72% | 1 | 1 | 0% | 1,533 | 509 | -67% | 0 | 0 | — |
case-07 | pass→pass | 7,826 | 1,748 | -78% | 1 | 1 | 0% | 1,395 | 503 | -64% | 0 | 0 | — |
case-10 | fail→pass | 10,856 | 1,648 | -85% | 1 | 1 | 0% | 2,003 | 408 | -80% | 0 | 0 | — |
case-11 | fail→pass | 7,909 | 2,259 | -71% | 1 | 1 | 0% | 1,081 | 525 | -51% | 0 | 0 | — |
case-12 | fail→pass | 12,839 | 4,839 | -62% | 1 | 1 | 0% | 2,096 | 973 | -54% | 0 | 0 | — |
case-13 | fail→fail | 33,423 | 12,314 | -63% | 1 | 1 | 0% | 3,727 | 2,022 | -46% | 0 | 0 | — |
case-14 | pass→pass | 5,836 | 1,944 | -67% | 1 | 1 | 0% | 1,045 | 548 | -48% | 0 | 0 | — |
case-16 | fail→pass | 15,374 | 5,287 | -66% | 1 | 1 | 0% | 2,608 | 1,035 | -60% | 0 | 0 | — |
case-17 | fail→pass | 12,375 | 4,310 | -65% | 1 | 1 | 0% | 2,107 | 1,017 | -52% | 0 | 0 | — |
case-18 | fail→pass | 11,652 | 2,088 | -82% | 1 | 1 | 0% | 1,806 | 457 | -75% | 0 | 0 | — |
case-19 | fail→pass | 11,949 | 4,338 | -64% | 1 | 1 | 0% | 2,051 | 911 | -56% | 0 | 0 | — |
case-21 | fail→pass | 29,458 | 9,797 | -67% | 1 | 1 | 0% | 3,338 | 2,024 | -39% | 0 | 0 | — |
case-22 | pass→pass | 16,034 | 15,371 | -4% | 1 | 1 | 0% | 3,057 | 3,217 | +5% | 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. The headline lift of +68 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.