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Get Started Free →Build structured strategy documents by asking one question at a time and patching the file.
.claude/skills/sickn33-interview-style-doc-building/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 249% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 117% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 496% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 66% | 0% |
| case-15 | ✓→✗ | ▼ Worse | 108% | 0% |
The user's preferred mode for creating durable strategic docs. AI does NOT propose content — AI asks one question, the user answers, AI patches the file, AI asks the next question. The file IS the conversation's output, updated incrementally.
NOT for: day planning (use day-plan), task triage (organize-tasks), or anything where AI proposes content first.
write_file for the new file. After this, NEVER overwrite — only patch.patch for every update. Never write_file to an existing doc.After each answer:
patch with old_string = placeholder or previous entry, new_string = updated content with the user's words preserved.For ranked lists, append one rank at a time:
1. **Business** — Q2 #1 goal: ...
2. **Health** — get below 81.0 kg, sleep 9h/day, ...Each rank gets patched in as the user confirms it.
day-plan — different pattern (task triage), not interview-style.organize-tasks — Todoist-specific.memory-management — separate from this; persona/preferences go to memory.davidondrej/skills; verify local paths, tools, credentials, and agent features before acting.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 9,278 | 8,035 | -13% | 1 | 1 | 0% | 1,585 | 2,363 | +49% | 0 | 0 | — |
case-01 | fail→fail | 8,336 | 5,732 | -31% | 1 | 1 | 0% | 1,269 | 1,971 | +55% | 0 | 0 | — |
case-02 | fail→fail | 10,583 | 10,587 | +0% | 1 | 1 | 0% | 1,691 | 1,860 | +10% | 0 | 0 | — |
case-03 | fail→fail | 6,398 | 6,053 | -5% | 1 | 1 | 0% | 1,082 | 2,009 | +86% | 0 | 0 | — |
case-05 | fail→fail | 6,225 | 3,615 | -42% | 1 | 1 | 0% | 910 | 1,544 | +70% | 0 | 0 | — |
case-06 | pass→fail | 11,405 | 12,322 | +8% | 1 | 1 | 0% | 1,885 | 3,123 | +66% | 0 | 0 | — |
case-07 | fail→fail | 5,446 | 8,007 | +47% | 1 | 1 | 0% | 791 | 1,692 | +114% | 0 | 0 | — |
case-08 | fail→fail | 6,866 | 11,677 | +70% | 1 | 1 | 0% | 1,132 | 2,857 | +152% | 0 | 0 | — |
case-09 | fail→pass | 3,046 | 3,574 | +17% | 1 | 1 | 0% | 450 | 1,571 | +249% | 0 | 0 | — |
case-10 | fail→pass | 8,183 | 9,227 | +13% | 1 | 1 | 0% | 1,163 | 2,518 | +117% | 0 | 0 | — |
case-11 | pass→pass | 5,843 | 10,884 | +86% | 1 | 1 | 0% | 924 | 2,283 | +147% | 0 | 0 | — |
case-12 | pass→pass | 4,025 | 3,594 | -11% | 1 | 1 | 0% | 553 | 1,541 | +179% | 0 | 0 | — |
case-13 | pass→pass | 4,159 | 12,941 | +211% | 1 | 1 | 0% | 610 | 2,521 | +313% | 0 | 0 | — |
case-14 | pass→pass | 5,964 | 6,369 | +7% | 1 | 1 | 0% | 875 | 2,060 | +135% | 0 | 0 | — |
case-15 | pass→fail | 4,657 | 5,804 | +25% | 1 | 1 | 0% | 655 | 1,364 | +108% | 0 | 0 | — |
case-16 | fail→fail | 7,336 | 4,954 | -32% | 1 | 1 | 0% | 1,143 | 1,811 | +58% | 0 | 0 | — |
case-17 | fail→fail | 4,133 | 9,225 | +123% | 1 | 1 | 0% | 653 | 1,563 | +139% | 0 | 0 | — |
case-18 | pass→pass | 4,149 | 2,000 | -52% | 1 | 1 | 0% | 573 | 1,280 | +123% | 0 | 0 | — |
case-19 | pass→pass | 6,569 | 8,633 | +31% | 1 | 1 | 0% | 1,050 | 1,997 | +90% | 0 | 0 | — |
case-20 | fail→fail | 6,339 | 8,053 | +27% | 1 | 1 | 0% | 806 | 1,807 | +124% | 0 | 0 | — |
case-21 | fail→pass | 7,249 | 9,417 | +30% | 1 | 1 | 0% | 457 | 2,726 | +496% | 0 | 0 | — |
case-22 | fail→fail | 8,414 | 14,475 | +72% | 1 | 1 | 0% | 1,257 | 2,807 | +123% | 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 19 counted toward the lift figure. The other 3 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 +5 percentage points is the difference between those two pass rates over the 19 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.