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Get Started Free →Apply the Doherty Threshold — keep system response times under 400ms to maintain user flow and perceived performance.
.claude/skills/owl-listener-doherty-threshold/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 93% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -16% | 0% |
You are an expert in perceived performance and the design of responsive, flow-preserving interfaces.
You apply the Doherty Threshold to identify where response latency breaks user flow, and design feedback patterns and technical targets to keep interactions feeling immediate.
Walter Doherty and Ahrvind Thadani (IBM, 1982) established that when a computer responds to a user action in under 400ms, productivity increases substantially — users stay in flow rather than losing their train of thought or shifting attention. Above this threshold, users notice the wait and their cognitive engagement with the task degrades. The key thresholds: | Response time | User perception | |---|---| | 0–100ms | Instant — the system feels like a direct extension of the action | | 100–300ms | Fast — perceptible but not disruptive | | 300–400ms | Approaching the boundary — some users notice | | 400ms–1s | Slow — users are aware of waiting; a response indicator is needed | | 1s+ | Definitely slow — progress feedback required; flow is broken | | 10s+ | Task-level disruption — users switch context |
If the system genuinely cannot respond in under 400ms:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | 11,394 | 11,119 | -2% | 1 | 1 | 0% | 1,800 | 2,572 | +43% | 0 | 0 | — |
case-15 | fail→pass | 11,600 | 12,203 | +5% | 1 | 1 | 0% | 1,881 | 2,727 | +45% | 0 | 0 | — |
case-01 | fail→pass | 18,825 | 17,216 | -9% | 1 | 1 | 0% | 3,085 | 3,526 | +14% | 0 | 0 | — |
case-02 | fail→pass | 18,171 | 18,639 | +3% | 1 | 1 | 0% | 2,939 | 3,706 | +26% | 0 | 0 | — |
case-03 | fail→fail | 17,835 | 16,795 | -6% | 1 | 1 | 0% | 3,144 | 3,669 | +17% | 0 | 0 | — |
case-04 | pass→pass | 14,024 | 11,754 | -16% | 1 | 1 | 0% | 2,313 | 2,942 | +27% | 0 | 0 | — |
case-05 | pass→pass | 18,611 | 17,887 | -4% | 1 | 1 | 0% | 3,466 | 3,759 | +8% | 0 | 0 | — |
case-06 | pass→pass | 18,446 | 20,005 | +8% | 1 | 1 | 0% | 3,132 | 4,233 | +35% | 0 | 0 | — |
case-07 | pass→pass | 15,640 | 11,865 | -24% | 1 | 1 | 0% | 2,668 | 2,817 | +6% | 0 | 0 | — |
case-08 | pass→pass | 10,128 | 11,170 | +10% | 1 | 1 | 0% | 1,866 | 2,659 | +42% | 0 | 0 | — |
case-09 | pass→pass | 11,730 | 9,553 | -19% | 1 | 1 | 0% | 2,058 | 2,503 | +22% | 0 | 0 | — |
case-10 | pass→pass | 11,578 | 8,155 | -30% | 1 | 1 | 0% | 1,814 | 2,158 | +19% | 0 | 0 | — |
case-11 | pass→pass | 13,596 | 14,507 | +7% | 1 | 1 | 0% | 2,301 | 3,181 | +38% | 0 | 0 | — |
case-12 | pass→pass | 10,178 | 8,187 | -20% | 1 | 1 | 0% | 1,656 | 2,185 | +32% | 0 | 0 | — |
case-13 | fail→pass | 4,509 | 4,297 | -5% | 1 | 1 | 0% | 782 | 1,513 | +93% | 0 | 0 | — |
case-16 | pass→pass | 10,887 | 14,084 | +29% | 1 | 1 | 0% | 1,780 | 3,024 | +70% | 0 | 0 | — |
case-17 | pass→pass | 15,907 | 13,267 | -17% | 1 | 1 | 0% | 2,747 | 3,090 | +12% | 0 | 0 | — |
case-18 | pass→pass | 13,766 | 14,959 | +9% | 1 | 1 | 0% | 2,243 | 3,235 | +44% | 0 | 0 | — |
case-19 | pass→pass | 12,280 | 10,026 | -18% | 1 | 1 | 0% | 2,192 | 2,515 | +15% | 0 | 0 | — |
case-20 | fail→pass | 8,764 | 4,127 | -53% | 1 | 1 | 0% | 1,742 | 1,468 | -16% | 0 | 0 | — |
case-21 | pass→pass | 13,517 | 11,872 | -12% | 1 | 1 | 0% | 2,290 | 2,749 | +20% | 0 | 0 | — |
case-22 | pass→pass | 14,074 | 13,949 | -1% | 1 | 1 | 0% | 2,449 | 3,228 | +32% | 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.