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Get Started Free →Plan and execute large refactor or rewrite efforts efficiently with parallel multi-agent analysis and implementation. Use when a user asks to refactor many files, split workstreams, analyze a target code area, and coordinate sub-agents with clear ownership and dependency-aware execution.
.claude/skills/bilal140202-orchestrate-batch-refactor/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -50% | 0% |
Use this skill to run high-throughput refactors safely. Analyze scope in parallel, synthesize a single plan, then execute independent work packets with sub-agents.
explorer sub-agents in parallel to analyze each lane.worker per independent packet.Every packet must include:
Use references/work-packet-template.md for the exact shape.
references/agent-prompt-templates.md.Run in this order:
Prefer fast feedback loops, but never skip required behavior checks.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | pass→pass | 10,448 | 2,743 | -74% | 1 | 1 | 0% | 1,647 | 1,227 | -26% | 0 | 0 | — |
case-01 | fail→fail | 5,643 | 5,178 | -8% | 1 | 1 | 0% | 240 | 1,061 | +342% | 0 | 0 | — |
case-02 | fail→fail | 3,884 | 3,273 | -16% | 1 | 1 | 0% | 262 | 1,109 | +323% | 0 | 0 | — |
case-03 | fail→fail | 4,251 | 4,022 | -5% | 1 | 1 | 0% | 249 | 1,084 | +335% | 0 | 0 | — |
case-04 | fail→pass | 11,091 | 7,605 | -31% | 1 | 1 | 0% | 2,243 | 2,186 | -3% | 0 | 0 | — |
case-05 | pass→pass | 8,440 | 4,271 | -49% | 1 | 1 | 0% | 1,556 | 1,518 | -2% | 0 | 0 | — |
case-06 | fail→fail | 5,269 | 6,977 | +32% | 1 | 1 | 0% | 974 | 1,909 | +96% | 0 | 0 | — |
case-07 | pass→pass | 8,721 | 2,532 | -71% | 1 | 1 | 0% | 1,478 | 1,188 | -20% | 0 | 0 | — |
case-08 | fail→pass | 11,361 | 2,834 | -75% | 1 | 1 | 0% | 2,189 | 1,218 | -44% | 0 | 0 | — |
case-09 | fail→fail | 10,252 | 6,646 | -35% | 1 | 1 | 0% | 1,865 | 1,854 | -1% | 0 | 0 | — |
case-10 | pass→pass | 11,453 | 4,778 | -58% | 1 | 1 | 0% | 1,867 | 1,531 | -18% | 0 | 0 | — |
case-12 | pass→pass | 7,099 | 4,802 | -32% | 1 | 1 | 0% | 1,257 | 1,573 | +25% | 0 | 0 | — |
case-13 | fail→pass | 15,762 | 13,056 | -17% | 1 | 1 | 0% | 1,295 | 1,969 | +52% | 0 | 0 | — |
case-14 | fail→pass | 10,190 | 4,686 | -54% | 1 | 1 | 0% | 1,666 | 1,619 | -3% | 0 | 0 | — |
case-15 | pass→pass | 13,352 | 9,392 | -30% | 1 | 1 | 0% | 2,506 | 2,415 | -4% | 0 | 0 | — |
case-20 | fail→fail | 6,141 | 6,205 | +1% | 1 | 1 | 0% | 407 | 1,018 | +150% | 0 | 0 | — |
case-16 | pass→pass | 5,840 | 4,962 | -15% | 1 | 1 | 0% | 1,289 | 1,849 | +43% | 0 | 0 | — |
case-17 | fail→pass | 11,752 | 1,889 | -84% | 1 | 1 | 0% | 2,150 | 1,071 | -50% | 0 | 0 | — |
case-18 | fail→pass | 9,001 | 1,145 | -87% | 1 | 1 | 0% | 1,750 | 873 | -50% | 0 | 0 | — |
case-19 | pass→pass | 11,958 | 7,645 | -36% | 1 | 1 | 0% | 2,213 | 2,262 | +2% | 0 | 0 | — |
case-21 | pass→pass | 11,194 | 5,434 | -51% | 1 | 1 | 0% | 1,908 | 1,675 | -12% | 0 | 0 | — |
case-22 | pass→pass | 11,080 | 6,353 | -43% | 1 | 1 | 0% | 1,877 | 1,724 | -8% | 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 +27 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.