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Get Started Free →To partition large workspaces (100+ files) into scoped subagent tasks when context is insufficient.
.claude/skills/griddynamics-large-workspace-handling/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -44% | 0% |
<large_workspace_handling>
<role>
Workspace partitioning strategist. Draws scope boundaries, dispatches subagents.
</role>
<when_to_use_skill> Use when large workspaces exceed single-agent context window. Partitions into write-scopes where every file belongs to exactly one scope, and merged results address the original request completely. </when_to_use_skill>
<core_concepts>
init-workspace-discovery/SKILL.md FROM KB and EXECUTE to create ONLY CODEMAP.md# headers of CODEMAP before scopingTwo strategies (mutually exclusive):
reverse-engineering/SKILL.md FROM KB if needed for code analysisSummarize & Index keywords: understand, analyze, investigate, explore, document, explain, find, search, review, audit, learn, overviewWork distribution keywords: implement, create, add, fix, refactor, update, change, modify, delete, remove, migrate, build, writeSummarize & IndexScoping:
</core_concepts>
</large_workspace_handling>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | fail→fail | 3,350 | 18,502 | +452% | 1 | 1 | 0% | 221 | 3,094 | +1300% | 0 | 0 | — |
case-07 | fail→pass | 14,182 | 5,025 | -65% | 1 | 1 | 0% | 2,243 | 1,556 | -31% | 0 | 0 | — |
case-01 | fail→fail | 21,860 | 4,268 | -80% | 1 | 1 | 0% | 3,788 | 962 | -75% | 0 | 0 | — |
case-02 | fail→fail | 11,080 | 4,095 | -63% | 1 | 1 | 0% | 1,773 | 867 | -51% | 0 | 0 | — |
case-03 | fail→fail | 33,475 | 2,072 | -94% | 1 | 1 | 0% | 6,210 | 1,013 | -84% | 0 | 0 | — |
case-04 | fail→pass | 8,545 | 9,768 | +14% | 1 | 1 | 0% | 1,555 | 2,600 | +67% | 0 | 0 | — |
case-05 | fail→fail | 3,806 | 5,853 | +54% | 1 | 1 | 0% | 652 | 930 | +43% | 0 | 0 | — |
case-08 | fail→pass | 6,657 | 3,186 | -52% | 1 | 1 | 0% | 1,055 | 1,122 | +6% | 0 | 0 | — |
case-09 | fail→pass | 10,492 | 3,115 | -70% | 1 | 1 | 0% | 1,614 | 1,104 | -32% | 0 | 0 | — |
case-10 | fail→pass | 16,220 | 3,892 | -76% | 1 | 1 | 0% | 2,379 | 1,332 | -44% | 0 | 0 | — |
case-11 | pass→pass | 14,569 | 3,574 | -75% | 1 | 1 | 0% | 2,187 | 1,235 | -44% | 0 | 0 | — |
case-12 | fail→pass | 9,943 | 1,835 | -82% | 1 | 1 | 0% | 1,574 | 949 | -40% | 0 | 0 | — |
case-13 | pass→pass | 13,754 | 3,887 | -72% | 1 | 1 | 0% | 2,062 | 1,241 | -40% | 0 | 0 | — |
case-14 | pass→fail | 8,702 | 2,089 | -76% | 1 | 1 | 0% | 1,267 | 924 | -27% | 0 | 0 | — |
case-15 | pass→pass | 13,995 | 2,971 | -79% | 1 | 1 | 0% | 2,259 | 1,077 | -52% | 0 | 0 | — |
case-16 | pass→pass | 13,676 | 3,863 | -72% | 1 | 1 | 0% | 2,058 | 1,289 | -37% | 0 | 0 | — |
case-17 | pass→pass | 8,118 | 3,417 | -58% | 1 | 1 | 0% | 1,214 | 1,142 | -6% | 0 | 0 | — |
case-18 | fail→pass | 6,190 | 2,193 | -65% | 1 | 1 | 0% | 899 | 1,013 | +13% | 0 | 0 | — |
case-19 | pass→fail | 15,005 | 6,112 | -59% | 1 | 1 | 0% | 2,375 | 1,560 | -34% | 0 | 0 | — |
case-20 | pass→pass | 15,691 | 6,549 | -58% | 1 | 1 | 0% | 2,454 | 1,750 | -29% | 0 | 0 | — |
case-21 | pass→pass | 11,368 | 2,988 | -74% | 1 | 1 | 0% | 1,733 | 1,061 | -39% | 0 | 0 | — |
case-22 | pass→pass | 5,644 | 2,424 | -57% | 1 | 1 | 0% | 892 | 1,077 | +21% | 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 +23 percentage points is the difference between those two pass rates over the 18 comparable cases. 2 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.