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Get Started Free →When a founder needs to write cold emails or LinkedIn messages to prospects, partners, or investors. Activate when the user mentions cold email, outbound, prospecting, LinkedIn outreach, or needs help getting replies from people who don't know them.
.claude/skills/mkurman-cold-outreach/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-06 | ✗→✓ | ▲ Improved | 102% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 126% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 106% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 121% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 58% | 0% |
Activate when a founder needs to write cold emails or LinkedIn messages to prospects, potential customers, investors, or strategic contacts. Also use when the user says "nobody replies to my emails," "how do I reach out to X," "write me a cold email," or "help with outbound."
From startup-context or the user:
Work with whatever the user provides. A strong research signal and clear value prop is enough to draft. Note what would strengthen the message but do not block on missing inputs.
Deliver all of the following:
The word "cold" is the problem. Every message should feel like it comes from someone who understands the prospect's world. Research is what makes that possible.
lead-scoring — use to prioritize which prospects to reach out to firstsales-script — use when the outreach lands a meeting and you need a discovery call or demo scriptExample prompt: "I need to reach out to VP Engineering at mid-market SaaS companies about our API monitoring tool. We reduced downtime by 73% for Acme Corp."
Good email output (Standard mode, Tier 2): > Subject: api alerts > > Hi Name], > > Saw your team just shipped the new payments integration — nice work. Launches like that usually surface a wave of edge-case API failures that are tough to catch with standard monitoring. > > We built a tool that catches those failures before customers notice. Acme Corp cut their API downtime by 73% in the first month. > > Worth a quick look?
Good LinkedIn connection request: > Hi Name] — saw the payments launch. We help engineering teams catch API failures before customers do. Would love to connect.
Follow-up (Day 7, new angle): > Hi Name], quick thought — after launches like yours, the #1 issue teams tell us about isn't downtime, it's the silent failures that slip through alerts. Happy to share what patterns we see across 50+ engineering teams if useful.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | pass→pass | 17,796 | 15,160 | -15% | 1 | 1 | 0% | 3,046 | 4,317 | +42% | 0 | 0 | — |
case-04 | pass→pass | 14,579 | 11,724 | -20% | 1 | 1 | 0% | 2,213 | 3,224 | +46% | 0 | 0 | — |
case-05 | pass→pass | 16,487 | 16,464 | -0% | 1 | 1 | 0% | 2,505 | 4,037 | +61% | 0 | 0 | — |
case-02 | fail→fail | 19,789 | 14,693 | -26% | 1 | 1 | 0% | 2,878 | 3,982 | +38% | 0 | 0 | — |
case-01 | fail→fail | 16,375 | 12,204 | -25% | 1 | 1 | 0% | 2,550 | 3,564 | +40% | 0 | 0 | — |
case-06 | fail→pass | 11,156 | 12,223 | +10% | 1 | 1 | 0% | 1,755 | 3,552 | +102% | 0 | 0 | — |
case-07 | fail→pass | 12,674 | 16,186 | +28% | 1 | 1 | 0% | 1,833 | 4,151 | +126% | 0 | 0 | — |
case-08 | fail→pass | 11,464 | 12,096 | +6% | 1 | 1 | 0% | 1,704 | 3,502 | +106% | 0 | 0 | — |
case-09 | fail→fail | 6,153 | 12,108 | +97% | 1 | 1 | 0% | 1,053 | 3,367 | +220% | 0 | 0 | — |
case-10 | fail→fail | 7,264 | 12,349 | +70% | 1 | 1 | 0% | 1,057 | 3,538 | +235% | 0 | 0 | — |
case-11 | pass→pass | 10,388 | 10,548 | +2% | 1 | 1 | 0% | 1,570 | 3,383 | +115% | 0 | 0 | — |
case-12 | fail→pass | 11,768 | 14,273 | +21% | 1 | 1 | 0% | 1,772 | 3,918 | +121% | 0 | 0 | — |
case-13 | fail→pass | 12,622 | 8,135 | -36% | 1 | 1 | 0% | 1,820 | 2,875 | +58% | 0 | 0 | — |
case-14 | fail→pass | 10,778 | 9,225 | -14% | 1 | 1 | 0% | 1,580 | 2,988 | +89% | 0 | 0 | — |
case-15 | pass→pass | 10,868 | 7,978 | -27% | 1 | 1 | 0% | 1,614 | 2,829 | +75% | 0 | 0 | — |
case-16 | fail→pass | 10,715 | 12,114 | +13% | 1 | 1 | 0% | 1,695 | 3,652 | +115% | 0 | 0 | — |
case-17 | fail→pass | 11,806 | 7,331 | -38% | 1 | 1 | 0% | 1,787 | 2,778 | +55% | 0 | 0 | — |
case-18 | pass→pass | 12,302 | 13,482 | +10% | 1 | 1 | 0% | 1,839 | 3,617 | +97% | 0 | 0 | — |
case-19 | fail→pass | 15,127 | 11,066 | -27% | 1 | 1 | 0% | 2,225 | 3,147 | +41% | 0 | 0 | — |
case-20 | fail→pass | 7,476 | 12,079 | +62% | 1 | 1 | 0% | 1,225 | 3,545 | +189% | 0 | 0 | — |
case-21 | pass→pass | 9,139 | 10,148 | +11% | 1 | 1 | 0% | 1,404 | 3,224 | +130% | 0 | 0 | — |
case-22 | fail→pass | 5,437 | 9,723 | +79% | 1 | 1 | 0% | 812 | 3,172 | +291% | 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. The headline lift of +50 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.