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Get Started Free →Track performance progress over time, quantify remaining headroom — 30 methods, 100 data points, 40 web searches budget
.claude/skills/yogsoth-ai-progress-quantification/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -28% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -22% | 0% |
Construct temporal performance trajectories for the target task. Fit progress curves, identify inflection points (paradigm shifts), estimate theoretical/practical ceilings, and quantify remaining headroom. Produces actionable intelligence about where the field is plateauing and where breakthroughs are needed.
| Resource | Floor | Target | |----------|-------|--------| | Methods tracked | 20 | 30 | | Historical data points | 70 | 100 | | Web searches | 25 | 40 | | Time span covered (years) | 3 | 5+ |
<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Methods tracked | 0 | 30 | BLOCKED |
| Historical data points | 0 | 100 | BLOCKED |
| Web searches used | 0 | 40 | — |
| Progress curves built | 0 | 3 | — |
| Headroom estimates | 0 | 3 | — |
| Inflection points identified | 0 | 2 | — |
</HARD-GATE>Cannot exit until historical_data_points >= 80 (80% of target).
json{ "progress_curves": [ { "dataset": "string", "metric": "string", "time_series": [{"date": "string", "method": "string", "score": 0.0}], "trend_type": "logarithmic|linear|sigmoid|stepped", "annual_improvement_rate": 0.0, "inflection_points": [{"date": "string", "method": "string", "cause": "string"}] } ], "headroom_analysis": [ { "dataset": "string", "metric": "string", "current_sota": 0.0, "human_performance": 0.0, "theoretical_ceiling": 0.0, "remaining_headroom_pct": 0.0, "saturation_status": "saturating|active_progress|early_stage" } ], "paradigm_shifts": [ { "year": 2024, "method_family": "string", "improvement_magnitude": 0.0, "description": "string" } ] }
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | leaderboard-harvesting | Systematically collect performance data from platforms and papers | | progress-curve-construction | Build performance-over-time progress curves with inflection detection |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | baseline-synthesis | Produce final structured baseline report integrating all analysis results | | headroom-estimation | Estimate theoretical/practical ceiling vs current SOTA gap | | progress-curve-fitting | Construct performance-over-time visualization data |
<!-- 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 | 40,501 | 25,110 | -38% | 1 | 1 | 0% | 6,862 | 1,600 | -77% | 0 | 0 | — |
case-02 | fail→fail | 47,630 | 41,881 | -12% | 1 | 1 | 0% | 8,254 | 1,816 | -78% | 0 | 0 | — |
case-03 | fail→fail | 38,067 | 27,414 | -28% | 1 | 1 | 0% | 8,251 | 2,114 | -74% | 0 | 0 | — |
case-04 | pass→pass | 24,241 | 38,146 | +57% | 1 | 1 | 0% | 4,023 | 8,080 | +101% | 0 | 0 | — |
case-05 | pass→pass | 26,227 | 44,299 | +69% | 1 | 1 | 0% | 4,344 | 8,499 | +96% | 0 | 0 | — |
case-06 | pass→pass | 18,707 | 54,558 | +192% | 1 | 1 | 0% | 2,912 | 9,149 | +214% | 0 | 0 | — |
case-07 | fail→fail | 34,541 | 26,742 | -23% | 1 | 1 | 0% | 5,775 | 1,741 | -70% | 0 | 0 | — |
case-08 | fail→fail | 31,092 | 24,032 | -23% | 1 | 1 | 0% | 4,243 | 1,874 | -56% | 0 | 0 | — |
case-09 | fail→fail | 13,142 | 20,527 | +56% | 1 | 1 | 0% | 907 | 1,675 | +85% | 0 | 0 | — |
case-10 | fail→pass | 41,523 | 8,723 | -79% | 1 | 1 | 0% | 1,533 | 1,407 | -8% | 0 | 0 | — |
case-11 | pass→pass | 22,416 | 8,011 | -64% | 1 | 1 | 0% | 2,470 | 2,234 | -10% | 0 | 0 | — |
case-12 | fail→pass | 18,105 | 3,896 | -78% | 1 | 1 | 0% | 2,053 | 1,241 | -40% | 0 | 0 | — |
case-13 | fail→fail | 20,472 | 22,266 | +9% | 1 | 1 | 0% | 4,025 | 1,933 | -52% | 0 | 0 | — |
case-14 | fail→fail | 17,427 | 27,650 | +59% | 1 | 1 | 0% | 2,124 | 1,895 | -11% | 0 | 0 | — |
case-15 | fail→pass | 22,196 | 7,782 | -65% | 1 | 1 | 0% | 1,873 | 1,354 | -28% | 0 | 0 | — |
case-16 | fail→pass | 28,539 | 10,224 | -64% | 1 | 1 | 0% | 1,965 | 1,200 | -39% | 0 | 0 | — |
case-17 | fail→fail | 38,721 | 23,553 | -39% | 1 | 1 | 0% | 7,334 | 2,399 | -67% | 0 | 0 | — |
case-18 | fail→fail | 17,150 | 20,352 | +19% | 1 | 1 | 0% | 3,961 | 1,798 | -55% | 0 | 0 | — |
case-19 | fail→fail | 19,084 | 26,951 | +41% | 1 | 1 | 0% | 3,496 | 1,584 | -55% | 0 | 0 | — |
case-20 | fail→fail | 23,673 | 14,463 | -39% | 1 | 1 | 0% | 4,025 | 1,915 | -52% | 0 | 0 | — |
case-21 | fail→pass | 11,132 | 2,798 | -75% | 1 | 1 | 0% | 1,851 | 1,448 | -22% | 0 | 0 | — |
case-22 | pass→pass | 44,847 | 28,208 | -37% | 1 | 1 | 0% | 2,530 | 5,358 | +112% | 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 10 counted toward the lift figure. The other 12 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 10 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.