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Get Started Free →Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.
.claude/skills/sickn33-concise-planning/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -71% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -42% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -64% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-22 | ✗→✓ | ▲ Improved | -74% | 0% |
Turn a user request into a single, actionable plan with atomic steps.
README.md, docs, and relevant code files.Use the following structure:
markdown# Plan <High-level approach> ## Scope - In: - Out: ## Action Items [ ] <Step 1: Discovery> [ ] <Step 2: Implementation> [ ] <Step 3: Implementation> [ ] <Step 4: Validation/Testing> [ ] <Step 5: Rollout/Commit> ## Open Questions - <Question 1 (max 3)>
This skill is applicable to execute the workflow or actions described in the overview.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,040 | 10,148 | -28% | 1 | 1 | 0% | 2,354 | 655 | -72% | 0 | 0 | — |
case-02 | fail→pass | 32,470 | 8,176 | -75% | 1 | 1 | 0% | 5,644 | 1,658 | -71% | 0 | 0 | — |
case-03 | fail→pass | 22,399 | 12,493 | -44% | 1 | 1 | 0% | 3,955 | 2,289 | -42% | 0 | 0 | — |
case-04 | pass→fail | 3,313 | 12,049 | +264% | 1 | 1 | 0% | 684 | 2,648 | +287% | 0 | 0 | — |
case-05 | pass→fail | 8,350 | 17,441 | +109% | 1 | 1 | 0% | 1,425 | 3,435 | +141% | 0 | 0 | — |
case-06 | pass→pass | 19,121 | 20,053 | +5% | 1 | 1 | 0% | 3,236 | 3,685 | +14% | 0 | 0 | — |
case-07 | pass→fail | 10,991 | 4,401 | -60% | 1 | 1 | 0% | 1,881 | 641 | -66% | 0 | 0 | — |
case-08 | fail→fail | 20,599 | 15,455 | -25% | 1 | 1 | 0% | 3,419 | 2,872 | -16% | 0 | 0 | — |
case-09 | fail→fail | 14,127 | 7,387 | -48% | 1 | 1 | 0% | 2,285 | 1,599 | -30% | 0 | 0 | — |
case-10 | fail→pass | 43,263 | 12,464 | -71% | 1 | 1 | 0% | 7,133 | 2,566 | -64% | 0 | 0 | — |
case-11 | fail→pass | 14,110 | 11,134 | -21% | 1 | 1 | 0% | 2,392 | 2,338 | -2% | 0 | 0 | — |
case-12 | fail→fail | 27,738 | 14,678 | -47% | 1 | 1 | 0% | 4,641 | 2,951 | -36% | 0 | 0 | — |
case-13 | fail→fail | 9,878 | 9,479 | -4% | 1 | 1 | 0% | 1,604 | 1,992 | +24% | 0 | 0 | — |
case-14 | pass→fail | 5,213 | 4,933 | -5% | 1 | 1 | 0% | 885 | 1,206 | +36% | 0 | 0 | — |
case-15 | fail→fail | 15,788 | 5,617 | -64% | 1 | 1 | 0% | 3,395 | 1,337 | -61% | 0 | 0 | — |
case-16 | pass→pass | 24,005 | 7,965 | -67% | 1 | 1 | 0% | 3,486 | 1,722 | -51% | 0 | 0 | — |
case-17 | fail→fail | 18,901 | 12,850 | -32% | 1 | 1 | 0% | 2,985 | 2,503 | -16% | 0 | 0 | — |
case-18 | fail→fail | 23,435 | 8,455 | -64% | 1 | 1 | 0% | 3,951 | 1,820 | -54% | 0 | 0 | — |
case-19 | fail→fail | 10,417 | 8,970 | -14% | 1 | 1 | 0% | 1,851 | 2,003 | +8% | 0 | 0 | — |
case-20 | fail→fail | 47,705 | 12,461 | -74% | 1 | 1 | 0% | 3,546 | 2,615 | -26% | 0 | 0 | — |
case-21 | fail→fail | 6,977 | 5,990 | -14% | 1 | 1 | 0% | 1,318 | 1,396 | +6% | 0 | 0 | — |
case-22 | fail→pass | 44,095 | 9,671 | -78% | 1 | 1 | 0% | 8,236 | 2,130 | -74% | 0 | 0 | — |
case-23 | fail→pass | 6,660 | 10,778 | +62% | 1 | 1 | 0% | 1,175 | 2,227 | +90% | 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. 23 cases were attempted, and 21 counted toward the lift figure. The other 2 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 +9 percentage points is the difference between those two pass rates over the 21 comparable cases. 5 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.