Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Generate a compact pool of discussion-worthy research directions from the signal table, writing `output/trace/IDEA_DIRECTION_POOL.md`. **Trigger**: idea direction pool, brainstorm directions, research directions, 研究方向池, brainstorm pool. **Use when**: you already have a signal table and want a small, non-isomorphic set of candidate directions.
.claude/skills/willoscar-idea-direction-generator/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 95% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -46% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 426% | 0% |
Goal: turn a signal table into a modest pool of discussion-worthy research directions.
This skill should favor:
uv run python .codex/skills/idea-direction-generator/scripts/run.py --workspace <workspace>--workspace <dir> (required)--unit-id <U###>--inputs <semicolon-separated>--outputs <semicolon-separated>--checkpoint <C#>uv run python .codex/skills/idea-direction-generator/scripts/run.py --workspace workspaces/brainstorm-llm-agents| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 2,376 | 4,722 | +99% | 1 | 1 | 0% | 213 | 429 | +101% | 0 | 0 | — |
case-02 | fail→fail | 24,555 | 5,049 | -79% | 1 | 1 | 0% | 4,177 | 421 | -90% | 0 | 0 | — |
case-03 | fail→fail | 15,740 | 5,237 | -67% | 1 | 1 | 0% | 2,619 | 467 | -82% | 0 | 0 | — |
case-04 | fail→fail | 8,411 | 4,034 | -52% | 1 | 1 | 0% | 503 | 378 | -25% | 0 | 0 | — |
case-09 | pass→pass | 7,344 | 2,540 | -65% | 1 | 1 | 0% | 1,574 | 715 | -55% | 0 | 0 | — |
case-05 | pass→pass | 6,244 | 1,848 | -70% | 1 | 1 | 0% | 1,010 | 544 | -46% | 0 | 0 | — |
case-06 | fail→pass | 3,279 | 1,516 | -54% | 1 | 1 | 0% | 370 | 512 | +38% | 0 | 0 | — |
case-07 | fail→pass | 5,381 | 2,377 | -56% | 1 | 1 | 0% | 274 | 534 | +95% | 0 | 0 | — |
case-08 | fail→pass | 7,324 | 2,567 | -65% | 1 | 1 | 0% | 1,306 | 701 | -46% | 0 | 0 | — |
case-10 | fail→pass | 8,494 | 2,820 | -67% | 1 | 1 | 0% | 1,613 | 700 | -57% | 0 | 0 | — |
case-11 | fail→pass | 4,661 | 4,368 | -6% | 1 | 1 | 0% | 192 | 1,009 | +426% | 0 | 0 | — |
case-12 | pass→fail | 7,836 | 1,456 | -81% | 1 | 1 | 0% | 1,307 | 395 | -70% | 0 | 0 | — |
case-13 | pass→pass | 6,835 | 2,107 | -69% | 1 | 1 | 0% | 1,379 | 587 | -57% | 0 | 0 | — |
case-14 | pass→pass | 2,580 | 3,644 | +41% | 1 | 1 | 0% | 521 | 489 | -6% | 0 | 0 | — |
case-15 | pass→pass | 5,306 | 1,965 | -63% | 1 | 1 | 0% | 970 | 591 | -39% | 0 | 0 | — |
case-16 | fail→pass | 16,273 | 1,951 | -88% | 1 | 1 | 0% | 884 | 489 | -45% | 0 | 0 | — |
case-17 | fail→pass | 6,514 | 1,672 | -74% | 1 | 1 | 0% | 1,210 | 511 | -58% | 0 | 0 | — |
case-18 | fail→pass | 8,958 | 2,427 | -73% | 1 | 1 | 0% | 1,265 | 476 | -62% | 0 | 0 | — |
case-19 | fail→pass | 6,660 | 1,863 | -72% | 1 | 1 | 0% | 1,361 | 435 | -68% | 0 | 0 | — |
case-20 | pass→pass | 14,288 | 16,967 | +19% | 1 | 1 | 0% | 2,146 | 2,677 | +25% | 0 | 0 | — |
case-21 | pass→pass | 11,749 | 10,611 | -10% | 1 | 1 | 0% | 1,760 | 1,669 | -5% | 0 | 0 | — |
case-22 | pass→pass | 6,897 | 6,627 | -4% | 1 | 1 | 0% | 988 | 1,127 | +14% | 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 15 counted toward the lift figure. The other 7 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 +36 percentage points is the difference between those two pass rates over the 15 comparable cases. 1 case got worse with the skill loaded, and it is 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.