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Get Started Free →Use when you have a written implementation plan to execute in a separate session with review checkpoints
.claude/skills/getcrew44-executing-plans/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 221% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 366% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 397% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 3% | 0% |
Load plan, review critically, execute all tasks, report when complete.
Announce at start: "I'm using the executing-plans skill to implement this plan."
Note: Tell your human partner that Superpowers works much better with access to subagents. The quality of its work will be significantly higher if run on a platform with subagent support (such as Claude Code or Codex). If subagents are available, use superpowers:subagent-driven-development instead of this skill.
For each task:
After all tasks complete and verified:
STOP executing immediately when:
Ask for clarification rather than guessing.
Return to Review (Step 1) when:
Don't force through blockers - stop and ask.
Required workflow skills:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 1,978 | 2,695 | +36% | 1 | 1 | 0% | 261 | 837 | +221% | 0 | 0 | — |
case-02 | fail→pass | 4,138 | 3,339 | -19% | 1 | 1 | 0% | 195 | 909 | +366% | 0 | 0 | — |
case-03 | fail→pass | 2,181 | 3,213 | +47% | 1 | 1 | 0% | 201 | 998 | +397% | 0 | 0 | — |
case-04 | fail→pass | 8,520 | 2,677 | -69% | 1 | 1 | 0% | 1,412 | 980 | -31% | 0 | 0 | — |
case-05 | pass→pass | 8,759 | 4,108 | -53% | 1 | 1 | 0% | 1,370 | 1,160 | -15% | 0 | 0 | — |
case-06 | pass→pass | 6,059 | 7,178 | +18% | 1 | 1 | 0% | 986 | 1,598 | +62% | 0 | 0 | — |
case-07 | fail→pass | 5,489 | 2,641 | -52% | 1 | 1 | 0% | 805 | 827 | +3% | 0 | 0 | — |
case-08 | pass→fail | 10,193 | 4,304 | -58% | 1 | 1 | 0% | 1,216 | 1,201 | -1% | 0 | 0 | — |
case-09 | pass→pass | 3,540 | 2,230 | -37% | 1 | 1 | 0% | 508 | 905 | +78% | 0 | 0 | — |
case-10 | fail→fail | 4,162 | 2,507 | -40% | 1 | 1 | 0% | 632 | 905 | +43% | 0 | 0 | — |
case-11 | fail→pass | 9,011 | 1,891 | -79% | 1 | 1 | 0% | 1,401 | 822 | -41% | 0 | 0 | — |
case-12 | pass→pass | 5,182 | 4,825 | -7% | 1 | 1 | 0% | 879 | 1,257 | +43% | 0 | 0 | — |
case-13 | pass→pass | 6,451 | 4,044 | -37% | 1 | 1 | 0% | 1,009 | 1,162 | +15% | 0 | 0 | — |
case-14 | pass→pass | 9,682 | 2,817 | -71% | 1 | 1 | 0% | 1,558 | 935 | -40% | 0 | 0 | — |
case-15 | pass→pass | 8,095 | 3,804 | -53% | 1 | 1 | 0% | 1,253 | 1,094 | -13% | 0 | 0 | — |
case-16 | fail→fail | 7,901 | 1,741 | -78% | 1 | 1 | 0% | 1,116 | 812 | -27% | 0 | 0 | — |
case-17 | pass→pass | 5,424 | 1,890 | -65% | 1 | 1 | 0% | 767 | 805 | +5% | 0 | 0 | — |
case-18 | fail→pass | 7,679 | 2,109 | -73% | 1 | 1 | 0% | 1,068 | 771 | -28% | 0 | 0 | — |
case-19 | fail→fail | 11,521 | 5,267 | -54% | 1 | 1 | 0% | 1,809 | 856 | -53% | 0 | 0 | — |
case-20 | pass→fail | 19,710 | 4,508 | -77% | 1 | 1 | 0% | 3,073 | 690 | -78% | 0 | 0 | — |
case-21 | pass→fail | 4,351 | 6,344 | +46% | 1 | 1 | 0% | 802 | 908 | +13% | 0 | 0 | — |
case-22 | fail→fail | 14,607 | 8,054 | -45% | 1 | 1 | 0% | 1,259 | 1,500 | +19% | 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 20 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 +18 percentage points is the difference between those two pass rates over the 20 comparable cases. 4 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.