Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Interview the user to turn project vision and decisions into README and ADR documentation.
.claude/skills/sickn33-brain-to-docs/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -27% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 195% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -39% | 0% |
The whole purpose: extract as much of the user's taste, judgment, knowledge, vision, preferences, and decisions as possible into text — saved as clear, concise markdown docs for the project. README holds the vision; docs/adr/ holds the decisions.
docs/adr/ (and README.md) beforedoing anything — other agents and people add/edit ADRs constantly.
unless the user asks for a different number. Make them high-variety: a wide, creative spectrum of unique angles, not all the same type (e.g. not all "tech stack" or all "product" or all "monetization"). Exception: if the user asks for a specific focus area, follow it. The user answers whichever they find most useful.
updates README.md or becomes a new ADR — whatever makes sense.
CONCISE, all sentences should be SHORT, and everything should be written in PLAIN ENGLISH.
NNNN-slug.md, Status + Context + Decision + Consequences.User request:
> Extract project vision, decisions, or preferences into durable docs.
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-01 | fail→pass | 6,209 | 6,016 | -3% | 1 | 1 | 0% | 1,052 | 1,242 | +18% | 0 | 0 | — |
case-02 | fail→pass | 7,821 | 6,522 | -17% | 1 | 1 | 0% | 1,409 | 1,547 | +10% | 0 | 0 | — |
case-03 | fail→fail | 8,526 | 2,936 | -66% | 1 | 1 | 0% | 1,442 | 958 | -34% | 0 | 0 | — |
case-21 | fail→fail | 3,431 | 7,351 | +114% | 1 | 1 | 0% | 612 | 1,894 | +209% | 0 | 0 | — |
case-04 | pass→pass | 3,318 | 7,192 | +117% | 1 | 1 | 0% | 586 | 1,192 | +103% | 0 | 0 | — |
case-05 | pass→pass | 8,181 | 6,991 | -15% | 1 | 1 | 0% | 1,366 | 1,613 | +18% | 0 | 0 | — |
case-06 | fail→fail | 3,107 | 2,389 | -23% | 1 | 1 | 0% | 513 | 856 | +67% | 0 | 0 | — |
case-07 | pass→fail | 3,767 | 2,084 | -45% | 1 | 1 | 0% | 662 | 848 | +28% | 0 | 0 | — |
case-08 | fail→pass | 11,753 | 8,891 | -24% | 1 | 1 | 0% | 2,188 | 1,600 | -27% | 0 | 0 | — |
case-09 | pass→pass | 9,422 | 9,056 | -4% | 1 | 1 | 0% | 1,987 | 1,439 | -28% | 0 | 0 | — |
case-10 | fail→pass | 3,262 | 7,216 | +121% | 1 | 1 | 0% | 652 | 1,925 | +195% | 0 | 0 | — |
case-11 | fail→fail | 12,203 | 3,759 | -69% | 1 | 1 | 0% | 2,714 | 766 | -72% | 0 | 0 | — |
case-16 | pass→pass | 2,858 | 2,083 | -27% | 1 | 1 | 0% | 473 | 735 | +55% | 0 | 0 | — |
case-12 | pass→pass | 7,627 | 8,997 | +18% | 1 | 1 | 0% | 1,359 | 1,647 | +21% | 0 | 0 | — |
case-13 | fail→fail | 8,440 | 4,913 | -42% | 1 | 1 | 0% | 1,467 | 1,341 | -9% | 0 | 0 | — |
case-14 | pass→fail | 7,242 | 1,967 | -73% | 1 | 1 | 0% | 1,208 | 742 | -39% | 0 | 0 | — |
case-15 | fail→fail | 3,190 | 3,864 | +21% | 1 | 1 | 0% | 633 | 1,192 | +88% | 0 | 0 | — |
case-17 | fail→pass | 12,160 | 3,878 | -68% | 1 | 1 | 0% | 1,905 | 1,166 | -39% | 0 | 0 | — |
case-18 | fail→fail | 5,822 | 5,316 | -9% | 1 | 1 | 0% | 970 | 1,451 | +50% | 0 | 0 | — |
case-19 | fail→pass | 6,275 | 4,999 | -20% | 1 | 1 | 0% | 1,126 | 1,288 | +14% | 0 | 0 | — |
case-20 | pass→pass | 5,235 | 3,566 | -32% | 1 | 1 | 0% | 1,054 | 1,165 | +11% | 0 | 0 | — |
case-22 | pass→pass | 4,335 | 2,041 | -53% | 1 | 1 | 0% | 829 | 888 | +7% | 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 21 counted toward the lift figure. The other 1 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 21 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
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.