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Get Started Free →Turn a pile of newsletters and subscriptions into one skimmable brief — the items that matter to YOUR interests extracted with sources, the noise dropped with a count, on a cadence that replaces daily trickle-reading. Use when asked digest my newsletters, summarize what my subscriptions said this week, what did I miss that I actually care about, or make my reading pile useful. Produces the interest-filtered brief with per-item sources, the dropped-with-reasons ledger, and the cadence that makes
.claude/skills/mohitagw15856-newsletter-digest-brief/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 262% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 156% | 0% |
Newsletters are individually reasonable and collectively unreadable — twelve arrivals a week, each 70% padding, each interrupting on its own schedule. The digest move: batch them (the inbox-unsubscribe-purge filter routes them to a label), then process the pile into one brief filtered by the reader's actual interests — items extracted with their source and why-it-matters, everything else dropped with a visible count so the reader trusts the filter instead of re-reading behind it.
Ask for these if not provided:
Items implying a move for this reader, first]
Per item: what — so-what for you. (best source])] Echo notes where heavy: "covered by 4 of your sources"]
…]
The counted categories — promos / echoes / off-interest]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 3,999 | 5,000 | +25% | 1 | 1 | 0% | 722 | 1,884 | +161% | 0 | 0 | — |
case-02 | fail→fail | 7,254 | 3,845 | -47% | 1 | 1 | 0% | 1,210 | 1,641 | +36% | 0 | 0 | — |
case-03 | fail→pass | 3,746 | 7,244 | +93% | 1 | 1 | 0% | 624 | 2,257 | +262% | 0 | 0 | — |
case-04 | fail→pass | 7,272 | 5,095 | -30% | 1 | 1 | 0% | 1,093 | 1,739 | +59% | 0 | 0 | — |
case-05 | pass→pass | 8,357 | 6,000 | -28% | 1 | 1 | 0% | 1,398 | 1,980 | +42% | 0 | 0 | — |
case-06 | pass→pass | 12,408 | 9,342 | -25% | 1 | 1 | 0% | 2,003 | 2,459 | +23% | 0 | 0 | — |
case-07 | fail→pass | 9,508 | 6,563 | -31% | 1 | 1 | 0% | 1,680 | 2,077 | +24% | 0 | 0 | — |
case-08 | pass→pass | 9,984 | 6,552 | -34% | 1 | 1 | 0% | 1,675 | 2,082 | +24% | 0 | 0 | — |
case-09 | fail→pass | 9,741 | 7,933 | -19% | 1 | 1 | 0% | 1,461 | 2,464 | +69% | 0 | 0 | — |
case-10 | pass→pass | 4,615 | 5,525 | +20% | 1 | 1 | 0% | 819 | 1,838 | +124% | 0 | 0 | — |
case-11 | pass→pass | 8,410 | 6,118 | -27% | 1 | 1 | 0% | 1,449 | 1,990 | +37% | 0 | 0 | — |
case-12 | pass→pass | 8,046 | 5,502 | -32% | 1 | 1 | 0% | 1,357 | 1,969 | +45% | 0 | 0 | — |
case-13 | pass→pass | 5,270 | 5,349 | +1% | 1 | 1 | 0% | 893 | 1,932 | +116% | 0 | 0 | — |
case-14 | pass→pass | 11,018 | 7,229 | -34% | 1 | 1 | 0% | 1,930 | 2,288 | +19% | 0 | 0 | — |
case-15 | pass→pass | 11,895 | 13,782 | +16% | 1 | 1 | 0% | 1,971 | 3,173 | +61% | 0 | 0 | — |
case-16 | pass→pass | 19,105 | 18,517 | -3% | 1 | 1 | 0% | 4,396 | 5,343 | +22% | 0 | 0 | — |
case-17 | pass→pass | 2,300 | 3,367 | +46% | 1 | 1 | 0% | 404 | 1,673 | +314% | 0 | 0 | — |
case-18 | pass→pass | 5,731 | 4,176 | -27% | 1 | 1 | 0% | 1,133 | 1,748 | +54% | 0 | 0 | — |
case-19 | pass→pass | 8,111 | 5,833 | -28% | 1 | 1 | 0% | 1,421 | 1,968 | +38% | 0 | 0 | — |
case-20 | pass→fail | 5,187 | 5,011 | -3% | 1 | 1 | 0% | 1,023 | 1,947 | +90% | 0 | 0 | — |
case-21 | fail→pass | 5,219 | 7,534 | +44% | 1 | 1 | 0% | 891 | 2,279 | +156% | 0 | 0 | — |
case-22 | pass→pass | 3,971 | 4,430 | +12% | 1 | 1 | 0% | 741 | 1,802 | +143% | 0 | 0 | — |
case-23 | pass→pass | 7,377 | 6,855 | -7% | 1 | 1 | 0% | 1,513 | 2,300 | +52% | 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. The headline lift of +17 percentage points is the difference between those two pass rates over the 23 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.