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Get Started Free →Integrated go-to-market strategy spanning ICP, motion, channels, messaging, success metrics, and launch plan. Use when launching a new product, entering a new segment, or auditing why an existing GTM isn't working.
.claude/skills/borghei-gtm-strategy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 152% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 104% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-08 | ✓→✗ | ▼ Worse | 346% | 0% |
A complete go-to-market strategy is the integrated cross-functional plan: ICP, motion, channels, messaging, success metrics, and launch sequence.
Before building the GTM strategy, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
See project-management/gtm/ideal-customer-profile.
Start narrow:
Reference: Crossing the Chasm. Don't try to sell to everyone Day 1.
| Motion | When | Cost structure | |--------|------|----------------| | PLG (product-led) | Self-serve product; low ACV ($0-$5K); strong activation | Low CAC; high product investment | | Sales-led | High ACV ($25K+); complex buying committees | High CAC; sales team needed | | Marketing-led / inbound | Mid-ACV ($5K-$25K); content-driven | Medium CAC; content + ops team | | Channel-led | Wide distribution via partners | Medium CAC; partner program needed | | Community-led | Strong category with passionate users | Long ramp; high ongoing investment | | Hybrid (PLG + sales) | PLG to capture; sales to expand | Most modern SaaS; complex to coordinate |
Don't try to run all motions Day 1.
| Motion | Primary channels | |--------|------------------| | PLG | Web direct, SEO, viral, content, app stores | | Sales-led | Outbound SDR, AE outbound, events, account-based | | Marketing-led | SEO, paid, content syndication, webinar | | Channel-led | Partner program, marketplace | | Community-led | Open source, community events, integrations |
| Motion | KPI focus | |--------|-----------| | PLG | Signups, activation, free-to-paid conversion, NRR | | Sales-led | Pipeline coverage, win rate, ACV, cycle time, NRR | | Marketing-led | MQL → SQL conversion, CPA, content engagement | | Channel-led | Partner-sourced revenue, partner activity |
Set targets; track weekly; tune.
gtm_strategy_validator.pyAudit GTM doc for: ICP specificity, motion fit, channel coherence, messaging clarity, metric definition, sequence realism.
bashpython3 project-management/gtm/gtm-strategy/scripts/gtm_strategy_validator.py \ --input gtm.json --format markdown
| ACV | Likely motion | |-----|---------------| | < $1K | PLG; consumer-style | | $1K-$10K | PLG-led; light sales-assist | | $10K-$50K | Marketing-led + inside sales | | $50K-$250K | Sales-led with marketing support | | $250K+ | Enterprise sales-led; long cycle |
Cross these and economics break.
Going broad Day 1:
Beachhead first:
Geoffrey Moore: cross the chasm one bowling pin at a time.
Each channel has product-fit assumptions:
If channel-product fit is off, channel won't deliver regardless of effort.
references/gtm-components-deep.md — ICP, motion, channels, messaging deepreferences/launch-sequence-playbook.md — T-90 → T+90 playbookreferences/gtm-anti-patterns.md — common failures + fixesproject-management/gtm/ideal-customer-profile — ICP definitionproject-management/strategy-frameworks/business-model-canvas — model behind GTMmarketing/launch-strategy — marketing execution layerbusiness-growth/customer-success-manager — post-sale GTMc-level-advisor/cro-advisor — sales / revenue strategyc-level-advisor/cmo-advisor — marketing strategy| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 30,831 | 7,199 | -77% | 1 | 1 | 0% | 4,752 | 3,273 | -31% | 0 | 0 | — |
case-02 | fail→fail | 25,093 | 23,703 | -6% | 1 | 1 | 0% | 3,549 | 5,476 | +54% | 0 | 0 | — |
case-03 | fail→fail | 23,166 | 26,836 | +16% | 1 | 1 | 0% | 3,594 | 6,005 | +67% | 0 | 0 | — |
case-04 | pass→pass | 24,650 | 23,425 | -5% | 1 | 1 | 0% | 3,512 | 5,670 | +61% | 0 | 0 | — |
case-05 | pass→pass | 15,674 | 20,070 | +28% | 1 | 1 | 0% | 2,203 | 5,034 | +129% | 0 | 0 | — |
case-06 | pass→pass | 19,911 | 18,367 | -8% | 1 | 1 | 0% | 2,888 | 4,793 | +66% | 0 | 0 | — |
case-07 | pass→pass | 14,846 | 23,804 | +60% | 1 | 1 | 0% | 2,256 | 5,765 | +156% | 0 | 0 | — |
case-08 | pass→fail | 4,187 | 4,973 | +19% | 1 | 1 | 0% | 638 | 2,847 | +346% | 0 | 0 | — |
case-09 | pass→pass | 10,142 | 11,095 | +9% | 1 | 1 | 0% | 1,609 | 3,802 | +136% | 0 | 0 | — |
case-10 | fail→pass | 8,687 | 8,910 | +3% | 1 | 1 | 0% | 1,318 | 3,315 | +152% | 0 | 0 | — |
case-11 | fail→fail | 8,842 | 7,890 | -11% | 1 | 1 | 0% | 1,235 | 3,134 | +154% | 0 | 0 | — |
case-12 | fail→pass | 14,857 | 17,719 | +19% | 1 | 1 | 0% | 2,388 | 4,883 | +104% | 0 | 0 | — |
case-13 | fail→pass | 10,053 | 1,721 | -83% | 1 | 1 | 0% | 1,553 | 2,384 | +54% | 0 | 0 | — |
case-14 | pass→fail | 12,644 | 14,646 | +16% | 1 | 1 | 0% | 1,903 | 4,158 | +118% | 0 | 0 | — |
case-15 | pass→pass | 18,690 | 24,217 | +30% | 1 | 1 | 0% | 2,704 | 5,449 | +102% | 0 | 0 | — |
case-16 | pass→pass | 17,187 | 19,480 | +13% | 1 | 1 | 0% | 2,517 | 4,979 | +98% | 0 | 0 | — |
case-17 | pass→pass | 18,285 | 19,267 | +5% | 1 | 1 | 0% | 2,550 | 4,780 | +87% | 0 | 0 | — |
case-18 | pass→pass | 15,299 | 14,338 | -6% | 1 | 1 | 0% | 2,177 | 4,174 | +92% | 0 | 0 | — |
case-19 | pass→pass | 16,722 | 16,629 | -1% | 1 | 1 | 0% | 2,503 | 4,466 | +78% | 0 | 0 | — |
case-20 | pass→pass | 13,739 | 16,093 | +17% | 1 | 1 | 0% | 2,049 | 4,382 | +114% | 0 | 0 | — |
case-21 | pass→pass | 23,110 | 26,591 | +15% | 1 | 1 | 0% | 3,685 | 6,356 | +72% | 0 | 0 | — |
case-22 | pass→pass | 26,338 | 36,073 | +37% | 1 | 1 | 0% | 4,720 | 8,302 | +76% | 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 +9 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 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.