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.claude/skills/kunagent-automation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 125% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | -5% | 0% |
Copyright (c) 2026 KunAgent. Licensed under the MIT License.
Create, inspect, update, delete, and run Kun scheduled tasks or saved workflows.
| Tool or skill | Use | |---|---| | mcp_gui_schedule_gui_schedule_list | List current schedules before a mutation. | | mcp_gui_schedule_gui_schedule_create | Create one-time, daily, or interval schedules. | | mcp_gui_schedule_gui_schedule_update | Update a schedule by returned task id. | | mcp_gui_schedule_gui_schedule_delete | Delete only the explicitly selected task. | | mcp_gui_schedule_list_workflows | Discover user-saved workflows. | | mcp_gui_schedule_run_workflow | Run a named saved workflow. | | task_graph | Model dependencies inside the current thread. |
Lead with the outcome, name the evidence used for verification, and disclose any real limitation that remains.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 7,515 | 6,995 | -7% | 1 | 1 | 0% | 1,079 | 808 | -25% | 0 | 0 | — |
case-02 | fail→fail | 8,799 | 3,949 | -55% | 1 | 1 | 0% | 1,697 | 617 | -64% | 0 | 0 | — |
case-03 | fail→fail | 19,442 | 4,494 | -77% | 1 | 1 | 0% | 2,404 | 639 | -73% | 0 | 0 | — |
case-04 | pass→pass | 5,721 | 7,582 | +33% | 1 | 1 | 0% | 722 | 1,622 | +125% | 0 | 0 | — |
case-05 | pass→pass | 7,003 | 4,646 | -34% | 1 | 1 | 0% | 1,179 | 1,122 | -5% | 0 | 0 | — |
case-06 | pass→pass | 10,617 | 8,965 | -16% | 1 | 1 | 0% | 1,824 | 1,880 | +3% | 0 | 0 | — |
case-07 | fail→fail | 13,701 | 5,393 | -61% | 1 | 1 | 0% | 929 | 818 | -12% | 0 | 0 | — |
case-08 | fail→fail | 10,539 | 5,774 | -45% | 1 | 1 | 0% | 1,579 | 753 | -52% | 0 | 0 | — |
case-09 | fail→fail | 9,626 | 7,689 | -20% | 1 | 1 | 0% | 323 | 1,018 | +215% | 0 | 0 | — |
case-10 | fail→fail | 5,303 | 9,160 | +73% | 1 | 1 | 0% | 850 | 1,275 | +50% | 0 | 0 | — |
case-11 | fail→fail | 9,407 | 4,758 | -49% | 1 | 1 | 0% | 1,422 | 753 | -47% | 0 | 0 | — |
case-12 | fail→fail | 7,713 | 8,566 | +11% | 1 | 1 | 0% | 1,227 | 1,065 | -13% | 0 | 0 | — |
case-13 | fail→fail | 4,549 | 4,494 | -1% | 1 | 1 | 0% | 253 | 600 | +137% | 0 | 0 | — |
case-14 | fail→fail | 9,465 | 6,481 | -32% | 1 | 1 | 0% | 1,512 | 820 | -46% | 0 | 0 | — |
case-15 | fail→fail | 14,234 | 8,970 | -37% | 1 | 1 | 0% | 2,503 | 1,039 | -58% | 0 | 0 | — |
case-16 | fail→fail | 11,317 | 7,819 | -31% | 1 | 1 | 0% | 2,013 | 1,056 | -48% | 0 | 0 | — |
case-17 | fail→fail | 8,363 | 4,332 | -48% | 1 | 1 | 0% | 719 | 873 | +21% | 0 | 0 | — |
case-18 | fail→fail | 11,817 | 8,028 | -32% | 1 | 1 | 0% | 1,741 | 1,154 | -34% | 0 | 0 | — |
case-19 | fail→pass | 17,081 | 11,313 | -34% | 1 | 1 | 0% | 2,734 | 2,102 | -23% | 0 | 0 | — |
case-20 | fail→pass | 8,402 | 4,046 | -52% | 1 | 1 | 0% | 1,529 | 1,026 | -33% | 0 | 0 | — |
case-21 | fail→pass | 9,296 | 7,830 | -16% | 1 | 1 | 0% | 1,434 | 1,057 | -26% | 0 | 0 | — |
case-22 | fail→fail | 9,801 | 6,286 | -36% | 1 | 1 | 0% | 1,460 | 945 | -35% | 0 | 0 | — |
case-23 | fail→fail | 6,298 | 8,172 | +30% | 1 | 1 | 0% | 988 | 947 | -4% | 0 | 0 | — |
case-24 | fail→fail | 19,981 | 3,889 | -81% | 1 | 1 | 0% | 2,033 | 581 | -71% | 0 | 0 | — |
case-25 | fail→fail | 3,487 | 6,468 | +85% | 1 | 1 | 0% | 436 | 796 | +83% | 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. 25 cases were attempted, and 6 counted toward the lift figure. The other 19 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 +12 percentage points is the difference between those two pass rates over the 6 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
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.