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Get Started Free →Campaign for mapping design spaces — discover dimensions/axes, enumerate combinations, identify gaps and novel opportunities. Produces dimensional maps in the wiki vault.
.claude/skills/yogsoth-ai-dimensional-analysis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -40% | 0% |
Map the design space of a research area by discovering its fundamental dimensions (axes of variation), enumerating meaningful combinations, and identifying unexplored regions that represent novel opportunities.
| Level | Count | Skills | |-------|-------|--------| | Strategy | 3 | dimension-discovery, combination-mapping, gap-prioritization | | Tactic | 2 | axis-extraction, matrix-generation | | SOP | 6 | dimension-page-creation, axis-validation, combination-enumeration, novelty-scoring, question-generation, matrix-export |
| Metric | Small | Medium | Large | |--------|-------|--------|-------| | Dimensions identified | 3 | 6 | 10 | | Axes per dimension | 3 | 5 | 8 | | Combinations explored | 10 | 30 | 60 | | Novel gaps identified | 3 | 8 | 15 | | Questions generated | 5 | 12 | 25 |
vault_search — find existing coverage of dimension valuesvault_add_edge — connect dimensions to conceptsvault_query_graph — explore existing dimension neighborhoodsvault_graph_stats — assess coverage completeness<HARD-GATE>
context-init at campaign startcontext-checkpoint after each strategy completesknowledge-compilation after each strategy</HARD-GATE>
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | dimension-discovery | Strategy for identifying fundamental dimensions of variation in a design space. | | knowledge-structuring-combination-mapping | Strategy for enumerating meaningful combinations across dimensions and marking existing work. | | knowledge-structuring-gap-prioritization | Strategy for ranking unexplored combinations by novelty, feasibility, and potential impact. |
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | axis-extraction | Tactic for systematically extracting axes of variation from literature — identify how practitioners compare approaches. | | knowledge-compilation | Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation. | | matrix-generation | Tactic for generating and populating combination matrices — cross dimensions to enumerate the design space. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 28,830 | 36,639 | +27% | 1 | 1 | 0% | 4,337 | 7,090 | +63% | 0 | 0 | — |
case-08 | fail→pass | 14,267 | 4,307 | -70% | 1 | 1 | 0% | 2,096 | 1,637 | -22% | 0 | 0 | — |
case-02 | fail→fail | 23,066 | 37,006 | +60% | 1 | 1 | 0% | 3,678 | 6,470 | +76% | 0 | 0 | — |
case-03 | fail→fail | 36,170 | 43,659 | +21% | 1 | 1 | 0% | 5,896 | 7,404 | +26% | 0 | 0 | — |
case-04 | fail→fail | 19,227 | 15,029 | -22% | 1 | 1 | 0% | 2,834 | 3,178 | +12% | 0 | 0 | — |
case-05 | pass→pass | 23,333 | 15,660 | -33% | 1 | 1 | 0% | 3,462 | 3,136 | -9% | 0 | 0 | — |
case-06 | pass→pass | 15,580 | 13,320 | -15% | 1 | 1 | 0% | 2,318 | 2,824 | +22% | 0 | 0 | — |
case-07 | fail→pass | 12,400 | 2,880 | -77% | 1 | 1 | 0% | 1,940 | 1,407 | -27% | 0 | 0 | — |
case-09 | pass→pass | 14,237 | 16,248 | +14% | 1 | 1 | 0% | 2,077 | 3,261 | +57% | 0 | 0 | — |
case-10 | fail→pass | 4,962 | 2,860 | -42% | 1 | 1 | 0% | 896 | 1,342 | +50% | 0 | 0 | — |
case-11 | fail→pass | 6,317 | 2,659 | -58% | 1 | 1 | 0% | 1,049 | 1,295 | +23% | 0 | 0 | — |
case-12 | fail→fail | 10,651 | 1,412 | -87% | 1 | 1 | 0% | 1,664 | 1,124 | -32% | 0 | 0 | — |
case-13 | fail→fail | 6,349 | 1,547 | -76% | 1 | 1 | 0% | 983 | 1,118 | +14% | 0 | 0 | — |
case-14 | fail→fail | 14,488 | 3,282 | -77% | 1 | 1 | 0% | 2,119 | 1,416 | -33% | 0 | 0 | — |
case-15 | pass→pass | 13,310 | 4,028 | -70% | 1 | 1 | 0% | 2,213 | 1,593 | -28% | 0 | 0 | — |
case-16 | fail→fail | 18,690 | 5,972 | -68% | 1 | 1 | 0% | 3,316 | 1,794 | -46% | 0 | 0 | — |
case-17 | fail→fail | 12,455 | 2,980 | -76% | 1 | 1 | 0% | 1,813 | 1,353 | -25% | 0 | 0 | — |
case-18 | fail→fail | 14,301 | 4,324 | -70% | 1 | 1 | 0% | 2,111 | 1,523 | -28% | 0 | 0 | — |
case-19 | fail→pass | 18,787 | 5,440 | -71% | 1 | 1 | 0% | 3,014 | 1,808 | -40% | 0 | 0 | — |
case-20 | pass→pass | 2,930 | 4,421 | +51% | 1 | 1 | 0% | 437 | 1,552 | +255% | 0 | 0 | — |
case-21 | pass→pass | 6,930 | 14,633 | +111% | 1 | 1 | 0% | 1,247 | 3,258 | +161% | 0 | 0 | — |
case-22 | fail→fail | 2,155 | 4,448 | +106% | 1 | 1 | 0% | 325 | 1,574 | +384% | 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 +23 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.