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Get Started Free →You take a YouTube video transcript and produce a single LinkedIn post draft.
.claude/skills/owainlewis-linkedin-post-writer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 120% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 85% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 161% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 78% | 0% |
You take a YouTube video transcript and produce a single LinkedIn post draft.
A transcript file at content/youtube/{slug}/transcript.md (or passed as input).
Markdown file at content/linkedin/{slug}.md with the draft post in the body.
Don't try to summarize the whole video. Pick the one thing most worth posting about.
markdown--- source_video: {video-id-or-url} generated_by: linkedin-post-writer --- {post body, 100-200 words} {optional 2-4 hashtags}
agent/linkedin-{slug}LinkedIn draft for {slug}| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 17,121 | 6,788 | -60% | 1 | 1 | 0% | 2,885 | 888 | -69% | 0 | 0 | — |
case-02 | fail→fail | 6,797 | 6,596 | -3% | 1 | 1 | 0% | 422 | 877 | +108% | 0 | 0 | — |
case-03 | fail→fail | 5,463 | 5,455 | -0% | 1 | 1 | 0% | 350 | 820 | +134% | 0 | 0 | — |
case-04 | fail→fail | 5,015 | 5,401 | +8% | 1 | 1 | 0% | 300 | 629 | +110% | 0 | 0 | — |
case-05 | pass→fail | 11,905 | 4,539 | -62% | 1 | 1 | 0% | 1,615 | 610 | -62% | 0 | 0 | — |
case-06 | fail→fail | 9,062 | 4,842 | -47% | 1 | 1 | 0% | 1,512 | 690 | -54% | 0 | 0 | — |
case-07 | fail→fail | 8,529 | 6,886 | -19% | 1 | 1 | 0% | 1,365 | 885 | -35% | 0 | 0 | — |
case-08 | fail→pass | 7,933 | 16,777 | +111% | 1 | 1 | 0% | 1,308 | 2,875 | +120% | 0 | 0 | — |
case-09 | fail→pass | 8,607 | 11,677 | +36% | 1 | 1 | 0% | 1,382 | 2,554 | +85% | 0 | 0 | — |
case-10 | fail→pass | 7,365 | 13,406 | +82% | 1 | 1 | 0% | 1,170 | 3,059 | +161% | 0 | 0 | — |
case-11 | fail→pass | 14,223 | 12,256 | -14% | 1 | 1 | 0% | 2,483 | 2,797 | +13% | 0 | 0 | — |
case-12 | fail→pass | 10,443 | 15,325 | +47% | 1 | 1 | 0% | 1,794 | 3,195 | +78% | 0 | 0 | — |
case-13 | fail→fail | 8,787 | 3,942 | -55% | 1 | 1 | 0% | 1,315 | 521 | -60% | 0 | 0 | — |
case-14 | fail→pass | 14,990 | 15,860 | +6% | 1 | 1 | 0% | 2,502 | 3,441 | +38% | 0 | 0 | — |
case-15 | pass→pass | 4,228 | 10,397 | +146% | 1 | 1 | 0% | 738 | 2,260 | +206% | 0 | 0 | — |
case-16 | fail→fail | 6,899 | 14,209 | +106% | 1 | 1 | 0% | 976 | 2,597 | +166% | 0 | 0 | — |
case-17 | fail→fail | 7,953 | 8,295 | +4% | 1 | 1 | 0% | 1,390 | 1,870 | +35% | 0 | 0 | — |
case-18 | fail→fail | 5,303 | 4,944 | -7% | 1 | 1 | 0% | 278 | 538 | +94% | 0 | 0 | — |
case-19 | pass→fail | 16,680 | 6,261 | -62% | 1 | 1 | 0% | 1,638 | 1,510 | -8% | 0 | 0 | — |
case-20 | pass→fail | 32,871 | 13,081 | -60% | 1 | 1 | 0% | 5,753 | 2,925 | -49% | 0 | 0 | — |
case-21 | fail→fail | 2,884 | 3,783 | +31% | 1 | 1 | 0% | 423 | 958 | +126% | 0 | 0 | — |
case-22 | fail→fail | 12,830 | 13,249 | +3% | 1 | 1 | 0% | 1,648 | 2,841 | +72% | 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 13 counted toward the lift figure. The other 9 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 +14 percentage points is the difference between those two pass rates over the 13 comparable cases. 3 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.