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Get Started Free →Integrates with Atlassian products to manage project tracking and documentation via MCP protocol, including Jira issue management, Confluence page editing, sprint and backlog management.
.claude/skills/paperclipai-atlassian-mcp/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 12% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 6% | 0% |
name: atlassian-mcp description: > Integrates with Atlassian products to manage project tracking and documentation via MCP protocol, including Jira issue management, Confluence page editing, sprint and backlog management. metadata: sources:
repo: jeffallan/claude-skills path: skills/atlassian-mcp/SKILL.md commit: 3bf9a24b76a7c122f1fc05e83929fbc84e1c207a attribution: Jeffallan license: MIT usage: referenced
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-23 | pass→pass | 7,273 | 3,935 | -46% | 1 | 1 | 0% | 1,441 | 809 | -44% | 0 | 0 | — |
case-01 | fail→fail | 7,907 | 5,079 | -36% | 1 | 1 | 0% | 1,020 | 1,105 | +8% | 0 | 0 | — |
case-02 | fail→fail | 5,730 | 5,264 | -8% | 1 | 1 | 0% | 951 | 992 | +4% | 0 | 0 | — |
case-03 | fail→fail | 4,874 | 4,552 | -7% | 1 | 1 | 0% | 713 | 945 | +33% | 0 | 0 | — |
case-04 | pass→pass | 4,266 | 3,070 | -28% | 1 | 1 | 0% | 710 | 728 | +3% | 0 | 0 | — |
case-05 | fail→pass | 3,770 | 9,362 | +148% | 1 | 1 | 0% | 654 | 1,032 | +58% | 0 | 0 | — |
case-06 | pass→fail | 4,725 | 4,109 | -13% | 1 | 1 | 0% | 790 | 837 | +6% | 0 | 0 | — |
case-07 | fail→pass | 4,581 | 6,701 | +46% | 1 | 1 | 0% | 748 | 839 | +12% | 0 | 0 | — |
case-08 | fail→fail | 3,643 | 4,898 | +34% | 1 | 1 | 0% | 472 | 926 | +96% | 0 | 0 | — |
case-09 | fail→fail | 8,051 | 5,990 | -26% | 1 | 1 | 0% | 1,448 | 1,238 | -15% | 0 | 0 | — |
case-10 | pass→fail | 7,509 | 4,062 | -46% | 1 | 1 | 0% | 1,233 | 907 | -26% | 0 | 0 | — |
case-11 | fail→pass | 10,720 | 3,909 | -64% | 1 | 1 | 0% | 1,580 | 737 | -53% | 0 | 0 | — |
case-12 | pass→pass | 7,024 | 6,619 | -6% | 1 | 1 | 0% | 1,049 | 1,279 | +22% | 0 | 0 | — |
case-13 | fail→fail | 3,759 | 8,680 | +131% | 1 | 1 | 0% | 570 | 1,442 | +153% | 0 | 0 | — |
case-14 | fail→fail | 4,445 | 4,976 | +12% | 1 | 1 | 0% | 753 | 859 | +14% | 0 | 0 | — |
case-15 | pass→fail | 6,041 | 4,441 | -26% | 1 | 1 | 0% | 1,135 | 706 | -38% | 0 | 0 | — |
case-16 | fail→fail | 4,701 | 8,212 | +75% | 1 | 1 | 0% | 614 | 730 | +19% | 0 | 0 | — |
case-17 | pass→pass | 5,899 | 4,155 | -30% | 1 | 1 | 0% | 1,249 | 857 | -31% | 0 | 0 | — |
case-18 | pass→fail | 6,022 | 74,405 | +1136% | 1 | 1 | 0% | 898 | 496 | -45% | 0 | 0 | — |
case-19 | pass→pass | 5,410 | 4,246 | -22% | 1 | 1 | 0% | 840 | 848 | +1% | 0 | 0 | — |
case-20 | fail→pass | 5,060 | 5,361 | +6% | 1 | 1 | 0% | 636 | 856 | +35% | 0 | 0 | — |
case-21 | fail→fail | 3,077 | 3,435 | +12% | 1 | 1 | 0% | 479 | 764 | +59% | 0 | 0 | — |
case-22 | fail→fail | 8,632 | 5,724 | -34% | 1 | 1 | 0% | 1,777 | 1,274 | -28% | 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. 23 cases were attempted, and 22 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 0 percentage points is the difference between those two pass rates over the 22 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.