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Get Started Free →Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.
.claude/skills/aidevgtm-know-if-its-working/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 16% | 0% |
> The only early metric that matters is net developer retention. Without it, you're not running a funnel; you're running a colander.
Use this when: you're tracking GitHub stars and pageviews and still can't answer "is GTM working?", or you're pouring effort into acquisition while new users quietly churn.
Acquisition is worthless if users don't come back. Prove retention first; only then does spending on acquisition make sense. Most early founders optimize the top of the funnel while the bottom leaks. Fix that order.
> Of all the developers who first used the product in Month 1, how many used it in Month 2? Month 3?
Measure one honest number per stage, not pageviews, not stars.
| Stage | The metric that counts | |---|---| | Discovery | unique human visitors / month | | Research | newsletter subs + community joins + follows | | Evaluation | free-tier signups / downloads / active free users | | Activation | monthly active users · frequency · session depth | | Membership | community members actively posting & answering |
The gate before all of it, the weekend test: can a new developer get to first value over a weekend from docs + Stack Overflow, no support call? Time-to-value target: < 1 hour ideal, 1 day max. If Evaluation/Activation fails here, no channel work will save you.
Developer marketing is hard to attribute and that's normal. A dev sees your HN post, reads a tutorial, lurks for two months, then signs up direct.
Is month-2 cohort retention healthy (users come back)?
├─ NO → STOP optimizing acquisition. Fix Evaluation/Activation (the weekend test, time-to-value).
└─ YES → is a channel reliably producing retained users?
├─ YES → pour more in (and only now consider paid to amplify).
└─ NO → go back to first-50-users; find the channel before scaling spend.Built from real dev-tool GTM experience, with frameworks from Adam Frankl (The Developer-Facing Startup) and Jakub Czakon (markepear.dev). When a framework can't make the call, that's what a human is for: The DevTool GTM Company.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 26,843 | 15,987 | -40% | 1 | 1 | 0% | 4,015 | 3,381 | -16% | 0 | 0 | — |
case-02 | fail→pass | 25,537 | 16,554 | -35% | 1 | 1 | 0% | 3,832 | 3,559 | -7% | 0 | 0 | — |
case-03 | fail→pass | 28,009 | 21,574 | -23% | 1 | 1 | 0% | 4,277 | 4,182 | -2% | 0 | 0 | — |
case-04 | pass→pass | 20,091 | 10,507 | -48% | 1 | 1 | 0% | 2,808 | 2,367 | -16% | 0 | 0 | — |
case-05 | fail→pass | 19,500 | 12,574 | -36% | 1 | 1 | 0% | 2,606 | 2,666 | +2% | 0 | 0 | — |
case-06 | fail→fail | 28,511 | 14,403 | -49% | 1 | 1 | 0% | 2,421 | 2,987 | +23% | 0 | 0 | — |
case-07 | pass→pass | 16,105 | 9,054 | -44% | 1 | 1 | 0% | 2,228 | 2,372 | +6% | 0 | 0 | — |
case-08 | fail→pass | 18,049 | 15,396 | -15% | 1 | 1 | 0% | 2,361 | 2,728 | +16% | 0 | 0 | — |
case-09 | fail→pass | 19,740 | 6,209 | -69% | 1 | 1 | 0% | 3,074 | 2,015 | -34% | 0 | 0 | — |
case-10 | fail→pass | 19,081 | 8,754 | -54% | 1 | 1 | 0% | 2,685 | 2,119 | -21% | 0 | 0 | — |
case-11 | pass→pass | 20,880 | 19,769 | -5% | 1 | 1 | 0% | 2,898 | 3,636 | +25% | 0 | 0 | — |
case-12 | pass→pass | 15,909 | 10,108 | -36% | 1 | 1 | 0% | 2,214 | 2,452 | +11% | 0 | 0 | — |
case-13 | pass→pass | 15,295 | 10,761 | -30% | 1 | 1 | 0% | 2,052 | 2,550 | +24% | 0 | 0 | — |
case-14 | fail→pass | 21,837 | 9,542 | -56% | 1 | 1 | 0% | 2,848 | 2,260 | -21% | 0 | 0 | — |
case-15 | pass→pass | 12,747 | 7,870 | -38% | 1 | 1 | 0% | 1,829 | 2,068 | +13% | 0 | 0 | — |
case-16 | fail→pass | 15,967 | 11,942 | -25% | 1 | 1 | 0% | 2,220 | 2,507 | +13% | 0 | 0 | — |
case-17 | fail→pass | 12,161 | 4,228 | -65% | 1 | 1 | 0% | 1,742 | 1,710 | -2% | 0 | 0 | — |
case-18 | fail→pass | 16,642 | 13,730 | -17% | 1 | 1 | 0% | 2,391 | 2,925 | +22% | 0 | 0 | — |
case-19 | pass→pass | 14,345 | 13,170 | -8% | 1 | 1 | 0% | 2,205 | 2,722 | +23% | 0 | 0 | — |
case-20 | pass→fail | 23,579 | 19,194 | -19% | 1 | 1 | 0% | 3,231 | 3,859 | +19% | 0 | 0 | — |
case-21 | pass→pass | 26,057 | 18,466 | -29% | 1 | 1 | 0% | 3,968 | 3,759 | -5% | 0 | 0 | — |
case-22 | pass→pass | 22,326 | 20,048 | -10% | 1 | 1 | 0% | 3,695 | 4,394 | +19% | 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 +45 percentage points is the difference between those two pass rates over the 22 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.