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Get Started Free →Combine two AI agent skills into one. Trigger phrases: /fuse, "fuse these skills", "combine skills", "merge these two skills", "create a fusion skill"
.claude/skills/gaia-research-fuse/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -41% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -71% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -69% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -72% | 0% |
A sequential routing pipeline to compose two installed skills into a single integrated agent capability.
.agents/skills/, .claude/skills/, or .cursor/rules/ to locate the target skills directory..gaia/ directory is present in the workspace root.Execute these steps in order to perform a skill fusion:
| Step | Action | Reference | |---|---|---| | 1 | Detect -- find and confirm the two source skills | reference/detect.md | | 2 | Compose -- read sources and apply the fusion prompt | reference/compose.md | | 3 | Name -- propose, validate, and select a target name | reference/name.md | | 4 | Register -- write the files and log the fusion event | reference/register.md |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,925 | 3,014 | -72% | 1 | 1 | 0% | 2,390 | 416 | -83% | 0 | 0 | — |
case-02 | fail→fail | 6,499 | 3,333 | -49% | 1 | 1 | 0% | 502 | 498 | -1% | 0 | 0 | — |
case-03 | fail→fail | 10,838 | 5,271 | -51% | 1 | 1 | 0% | 2,555 | 625 | -76% | 0 | 0 | — |
case-04 | fail→pass | 5,294 | 2,361 | -55% | 1 | 1 | 0% | 1,043 | 615 | -41% | 0 | 0 | — |
case-05 | fail→pass | 8,654 | 2,106 | -76% | 1 | 1 | 0% | 1,508 | 654 | -57% | 0 | 0 | — |
case-06 | fail→pass | 10,024 | 4,187 | -58% | 1 | 1 | 0% | 1,699 | 501 | -71% | 0 | 0 | — |
case-11 | pass→pass | 10,803 | 2,144 | -80% | 1 | 1 | 0% | 1,849 | 589 | -68% | 0 | 0 | — |
case-07 | fail→pass | 11,206 | 2,151 | -81% | 1 | 1 | 0% | 1,891 | 580 | -69% | 0 | 0 | — |
case-08 | fail→pass | 11,230 | 1,862 | -83% | 1 | 1 | 0% | 1,982 | 559 | -72% | 0 | 0 | — |
case-09 | fail→pass | 6,610 | 2,107 | -68% | 1 | 1 | 0% | 1,022 | 593 | -42% | 0 | 0 | — |
case-10 | fail→pass | 7,533 | 1,338 | -82% | 1 | 1 | 0% | 1,485 | 419 | -72% | 0 | 0 | — |
case-12 | pass→pass | 4,267 | 1,823 | -57% | 1 | 1 | 0% | 701 | 489 | -30% | 0 | 0 | — |
case-13 | fail→pass | 5,688 | 1,512 | -73% | 1 | 1 | 0% | 1,037 | 443 | -57% | 0 | 0 | — |
case-14 | fail→fail | 6,781 | 1,444 | -79% | 1 | 1 | 0% | 1,035 | 408 | -61% | 0 | 0 | — |
case-15 | fail→fail | 8,719 | 1,922 | -78% | 1 | 1 | 0% | 1,518 | 492 | -68% | 0 | 0 | — |
case-20 | pass→pass | 3,452 | 10,618 | +208% | 1 | 1 | 0% | 551 | 1,850 | +236% | 0 | 0 | — |
case-16 | fail→fail | 8,273 | 1,974 | -76% | 1 | 1 | 0% | 1,236 | 567 | -54% | 0 | 0 | — |
case-17 | fail→fail | 7,915 | 2,350 | -70% | 1 | 1 | 0% | 1,275 | 581 | -54% | 0 | 0 | — |
case-18 | fail→pass | 10,447 | 3,318 | -68% | 1 | 1 | 0% | 1,922 | 911 | -53% | 0 | 0 | — |
case-19 | pass→pass | 5,647 | 1,579 | -72% | 1 | 1 | 0% | 925 | 480 | -48% | 0 | 0 | — |
case-21 | fail→fail | 3,235 | 14,771 | +357% | 1 | 1 | 0% | 191 | 2,902 | +1419% | 0 | 0 | — |
case-22 | fail→fail | 2,605 | 4,513 | +73% | 1 | 1 | 0% | 308 | 994 | +223% | 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 18 counted toward the lift figure. The other 4 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 18 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.