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Get Started Free →Execute software engineer specializing in creating production-ready ADK agents with best practices, code structure, testing, and deployment automation. Use when asked to "build ADK agent", "create agent code", or "engineer ADK application". Trigger with relevant phrases based on skill purpose.
.claude/skills/jeremylongshore-adk-engineer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 33% | 0% |
Engineer production-ready Agent Development Kit (ADK) agents and multi-agent systems: clean structure, testability, safe tool usage, and deployment automation.
Use this skill to design and implement ADK agent code that is maintainable and shippable: clear module boundaries, structured tool interfaces, regression tests, and a deployment checklist (local or Agent Engine).
Example: Productionizing an existing ADK agent
src/ layout, tool boundaries, a test suite, and a deployment checklist.Example: Multi-agent workflow
${CLAUDE_SKILL_DIR}/references/SKILL.full.md000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md000-docs/6767-b-SPEC-DR-STND-claude-skills-standard.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 | 28,918 | 26,770 | -7% | 1 | 1 | 0% | 6,209 | 6,862 | +11% | 0 | 0 | — |
case-02 | fail→pass | 13,620 | 9,085 | -33% | 1 | 1 | 0% | 2,663 | 2,458 | -8% | 0 | 0 | — |
case-03 | pass→pass | 11,241 | 10,967 | -2% | 1 | 1 | 0% | 1,949 | 2,600 | +33% | 0 | 0 | — |
case-04 | pass→pass | 16,422 | 14,549 | -11% | 1 | 1 | 0% | 3,057 | 3,356 | +10% | 0 | 0 | — |
case-05 | fail→pass | 14,466 | 15,263 | +6% | 1 | 1 | 0% | 2,498 | 3,614 | +45% | 0 | 0 | — |
case-06 | pass→pass | 11,452 | 8,584 | -25% | 1 | 1 | 0% | 2,076 | 2,167 | +4% | 0 | 0 | — |
case-07 | pass→pass | 11,353 | 8,736 | -23% | 1 | 1 | 0% | 2,066 | 2,238 | +8% | 0 | 0 | — |
case-08 | fail→fail | 12,693 | 10,146 | -20% | 1 | 1 | 0% | 2,290 | 2,611 | +14% | 0 | 0 | — |
case-09 | pass→pass | 17,888 | 17,192 | -4% | 1 | 1 | 0% | 3,004 | 3,720 | +24% | 0 | 0 | — |
case-10 | pass→pass | 19,646 | 7,819 | -60% | 1 | 1 | 0% | 1,947 | 2,216 | +14% | 0 | 0 | — |
case-11 | pass→pass | 5,051 | 5,459 | +8% | 1 | 1 | 0% | 796 | 1,606 | +102% | 0 | 0 | — |
case-12 | pass→pass | 10,665 | 8,707 | -18% | 1 | 1 | 0% | 1,961 | 2,405 | +23% | 0 | 0 | — |
case-13 | pass→pass | 11,889 | 9,804 | -18% | 1 | 1 | 0% | 2,087 | 2,302 | +10% | 0 | 0 | — |
case-14 | fail→pass | 17,438 | 12,564 | -28% | 1 | 1 | 0% | 2,964 | 3,008 | +1% | 0 | 0 | — |
case-15 | pass→pass | 9,595 | 9,234 | -4% | 1 | 1 | 0% | 1,857 | 2,419 | +30% | 0 | 0 | — |
case-16 | fail→pass | 9,584 | 5,287 | -45% | 1 | 1 | 0% | 1,555 | 1,618 | +4% | 0 | 0 | — |
case-17 | fail→fail | 4,248 | 3,152 | -26% | 1 | 1 | 0% | 710 | 1,127 | +59% | 0 | 0 | — |
case-18 | fail→fail | 14,463 | 12,310 | -15% | 1 | 1 | 0% | 2,580 | 2,700 | +5% | 0 | 0 | — |
case-19 | pass→pass | 7,050 | 4,562 | -35% | 1 | 1 | 0% | 1,218 | 1,486 | +22% | 0 | 0 | — |
case-20 | pass→pass | 11,386 | 9,402 | -17% | 1 | 1 | 0% | 2,243 | 2,479 | +11% | 0 | 0 | — |
case-21 | pass→pass | 11,219 | 10,650 | -5% | 1 | 1 | 0% | 2,112 | 2,618 | +24% | 0 | 0 | — |
case-22 | pass→pass | 8,392 | 9,079 | +8% | 1 | 1 | 0% | 1,573 | 2,018 | +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. 22 cases were attempted. The headline lift of +18 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.