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Get Started Free →Synthesize multiple dimension scores into radar chart data and compute overall readiness.
.claude/skills/yogsoth-ai-radar-synthesis/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 643% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 271% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 161% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 265% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 249% | 0% |
Combine individual dimension assessment scores into a unified radar chart representation and compute an overall readiness score. Produces visualization-ready data and a narrative summary.
Spawns a subagent that:
Synthesis requires holistic analysis of the score pattern — identifying asymmetries, computing weighted averages, and producing narrative interpretation that considers dimension interactions.
Output MUST include: radar_chart_data with all dimensions, overall_readiness score, and narrative summary. Reject if any dimension from input is missing in output.
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Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
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| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 7,687 | 21,014 | +173% | 1 | 1 | 0% | 617 | 4,584 | +643% | 0 | 0 | — |
case-01 | fail→pass | 9,717 | 21,153 | +118% | 1 | 1 | 0% | 857 | 3,182 | +271% | 0 | 0 | — |
case-02 | fail→pass | 5,604 | 16,001 | +186% | 1 | 1 | 0% | 1,064 | 2,773 | +161% | 0 | 0 | — |
case-03 | fail→pass | 8,972 | 15,554 | +73% | 1 | 1 | 0% | 698 | 2,549 | +265% | 0 | 0 | — |
case-05 | fail→fail | 14,006 | 39,807 | +184% | 1 | 1 | 0% | 449 | 1,053 | +135% | 0 | 0 | — |
case-06 | fail→pass | 8,394 | 15,938 | +90% | 1 | 1 | 0% | 651 | 2,272 | +249% | 0 | 0 | — |
case-07 | fail→pass | 17,654 | 7,138 | -60% | 1 | 1 | 0% | 3,062 | 1,392 | -55% | 0 | 0 | — |
case-08 | fail→pass | 8,509 | 24,261 | +185% | 1 | 1 | 0% | 723 | 3,810 | +427% | 0 | 0 | — |
case-09 | pass→pass | 17,753 | 11,328 | -36% | 1 | 1 | 0% | 1,868 | 1,937 | +4% | 0 | 0 | — |
case-10 | pass→pass | 21,830 | 22,189 | +2% | 1 | 1 | 0% | 2,634 | 2,871 | +9% | 0 | 0 | — |
case-11 | pass→pass | 15,126 | 10,645 | -30% | 1 | 1 | 0% | 1,773 | 1,752 | -1% | 0 | 0 | — |
case-12 | pass→pass | 21,695 | 37,680 | +74% | 1 | 1 | 0% | 2,721 | 6,129 | +125% | 0 | 0 | — |
case-13 | fail→pass | 11,381 | 19,045 | +67% | 1 | 1 | 0% | 661 | 2,375 | +259% | 0 | 0 | — |
case-14 | fail→pass | 17,848 | 12,215 | -32% | 1 | 1 | 0% | 2,124 | 1,504 | -29% | 0 | 0 | — |
case-15 | fail→pass | 7,526 | 17,044 | +126% | 1 | 1 | 0% | 357 | 2,365 | +562% | 0 | 0 | — |
case-16 | fail→pass | 10,030 | 41,748 | +316% | 1 | 1 | 0% | 813 | 4,299 | +429% | 0 | 0 | — |
case-17 | fail→pass | 15,968 | 20,574 | +29% | 1 | 1 | 0% | 1,832 | 3,120 | +70% | 0 | 0 | — |
case-18 | pass→pass | 8,843 | 13,065 | +48% | 1 | 1 | 0% | 1,572 | 1,482 | -6% | 0 | 0 | — |
case-19 | fail→pass | 6,627 | 9,355 | +41% | 1 | 1 | 0% | 1,144 | 1,946 | +70% | 0 | 0 | — |
case-20 | fail→pass | 9,168 | 9,235 | +1% | 1 | 1 | 0% | 1,285 | 2,102 | +64% | 0 | 0 | — |
case-21 | fail→pass | 2,779 | 17,448 | +528% | 1 | 1 | 0% | 335 | 2,543 | +659% | 0 | 0 | — |
case-22 | fail→pass | 2,844 | 19,644 | +591% | 1 | 1 | 0% | 453 | 2,897 | +540% | 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 21 counted toward the lift figure. The other 1 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 +73 percentage points is the difference between those two pass rates over the 21 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.