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Get Started Free →Writing, exploit — assemble raw material into a journey of beats, grounding each term before a beat leans on it.
.claude/skills/asymmetric-al-writing-beats/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 231% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 458% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 52% | 0% |
<what-to-do>
The user has passed (or will pass) a markdown file of raw material. This is exploit: the exploring is done, the pile is fixed — commit to a path through it and mine the pile to fill each beat.
If the user did not say where to save the article, ask once and remember the path.
Then run a beat-by-beat journey, choose-your-own-adventure style:
</what-to-do>
<supporting-info>
Every concept has to be grounded before a beat can lean on it: the audience either walked in knowing it or met it in an earlier beat. A beat that reaches for an ungrounded concept loses the reader — that is the one move the journey can't make. The unit is the concept, not the word for it: a beat can lean on an idea the reader lacks even with no jargon in sight. Where a concept has a name — a term — grounding it means landing the idea and the term together.
A concept gets grounded one of two ways:
So each beat does two jobs: it requires concepts that are already grounded, and it grounds new ones. Keep a running list of what's grounded so far, and update it each time a beat lands.
This is what shapes the choose-your-own-adventure. A candidate beat is only reachable if everything it requires is already grounded; picking a beat that grounds concept X unlocks every beat that was waiting on X. When you offer next beats, they must all be reachable from the current grounded set — and say what each one grounds, so the user can see which paths it opens.
The big lever is what you make a prerequisite versus what you ground inside the piece. Demand too much up front and you shut out readers who don't have it; ground too much inside and the early beats drown in definitions. Settle this with the user when you establish prerequisites, and revisit it whenever a tempting beat turns out to require a concept nothing has grounded yet — the fix is either a grounding beat before it, or promoting the concept to a prerequisite.
A beat is one move in the journey. It does one thing — sets a scene, lands a point, asks a question, drops an aside, twists the angle. Then it stops, leaving the reader at a place where the next beat can pivot.
A beat is sized by what it needs:
If a "beat" needs five paragraphs and three subheadings, it's not a beat — it's two beats glued together. Split it.
Pull material from the raw pile to populate each beat. You can paraphrase, split, recombine, or quote. The pile is a quarry.
The article ends when the journey is complete — not when the pile is empty. Most piles will have leftover fragments that don't make it in. That is fine; that is the point of having more raw material than you need.
</supporting-info>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 2,743 | 4,880 | +78% | 1 | 1 | 0% | 319 | 1,388 | +335% | 0 | 0 | — |
case-02 | fail→pass | 8,849 | 2,946 | -67% | 1 | 1 | 0% | 1,439 | 1,573 | +9% | 0 | 0 | — |
case-08 | fail→fail | 3,067 | 3,775 | +23% | 1 | 1 | 0% | 481 | 1,679 | +249% | 0 | 0 | — |
case-03 | fail→fail | 7,571 | 5,550 | -27% | 1 | 1 | 0% | 1,209 | 1,339 | +11% | 0 | 0 | — |
case-04 | fail→pass | 4,135 | 6,846 | +66% | 1 | 1 | 0% | 699 | 2,311 | +231% | 0 | 0 | — |
case-05 | fail→pass | 10,223 | 1,984 | -81% | 1 | 1 | 0% | 1,644 | 1,389 | -16% | 0 | 0 | — |
case-06 | pass→fail | 11,811 | 9,385 | -21% | 1 | 1 | 0% | 1,777 | 2,693 | +52% | 0 | 0 | — |
case-07 | fail→fail | 9,714 | 7,662 | -21% | 1 | 1 | 0% | 1,485 | 2,406 | +62% | 0 | 0 | — |
case-09 | pass→pass | 8,174 | 4,455 | -45% | 1 | 1 | 0% | 1,448 | 1,836 | +27% | 0 | 0 | — |
case-10 | pass→fail | 7,427 | 3,700 | -50% | 1 | 1 | 0% | 1,212 | 1,714 | +41% | 0 | 0 | — |
case-11 | fail→pass | 3,245 | 12,835 | +296% | 1 | 1 | 0% | 473 | 2,640 | +458% | 0 | 0 | — |
case-12 | pass→pass | 11,577 | 9,122 | -21% | 1 | 1 | 0% | 1,912 | 2,610 | +37% | 0 | 0 | — |
case-13 | pass→pass | 2,978 | 5,408 | +82% | 1 | 1 | 0% | 545 | 1,905 | +250% | 0 | 0 | — |
case-14 | pass→pass | 10,595 | 3,696 | -65% | 1 | 1 | 0% | 1,679 | 1,704 | +1% | 0 | 0 | — |
case-15 | pass→pass | 12,000 | 2,865 | -76% | 1 | 1 | 0% | 1,860 | 1,567 | -16% | 0 | 0 | — |
case-16 | pass→pass | 9,750 | 3,623 | -63% | 1 | 1 | 0% | 1,355 | 1,643 | +21% | 0 | 0 | — |
case-17 | pass→pass | 9,295 | 3,017 | -68% | 1 | 1 | 0% | 1,270 | 1,626 | +28% | 0 | 0 | — |
case-18 | fail→fail | 8,814 | 3,767 | -57% | 1 | 1 | 0% | 1,668 | 1,699 | +2% | 0 | 0 | — |
case-19 | pass→fail | 8,100 | 13,299 | +64% | 1 | 1 | 0% | 1,236 | 3,313 | +168% | 0 | 0 | — |
case-20 | pass→pass | 4,130 | 10,349 | +151% | 1 | 1 | 0% | 608 | 2,905 | +378% | 0 | 0 | — |
case-21 | pass→pass | 3,653 | 5,892 | +61% | 1 | 1 | 0% | 519 | 1,477 | +185% | 0 | 0 | — |
case-22 | pass→fail | 8,707 | 5,818 | -33% | 1 | 1 | 0% | 1,450 | 2,190 | +51% | 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 0 percentage points is the difference between those two pass rates over the 20 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.