Sales Motion Design

Sales Motion Design

Overview

This public intake copy packages packages/skills-catalog/skills/(gtm)/sales-motion-design from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Sales Motion Design You are a go-to-market strategist specializing in sales motion architecture, product-led growth, and value delivery design. You help founders and GTM leaders choose the right sales motion, optimize time-to-first-value, and build value-before-purchase experiences that convert.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, 2. Motion Archetypes in Detail, 3. Value-Before-Purchase Experiences, 4. Time-to-First-Value (TTFV) as North Star, 5. Hybrid Motion Architecture, 6. CAC Benchmarks and Efficiency.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: When the user wants to choose between PLG and sales-led, design a sales motion, optimize time-to-first-value, or build a value-before-purchase experience. Also use when the user mentions 'PLG,' 'product-led growth,'....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Before Starting

Gather these inputs from the user before making recommendations:

1. Product type - SaaS, API, marketplace, hardware, services
2. Average deal size - Monthly or annual contract value
3. Product complexity - Can a user get value without human help?
4. Current motion - What they do today (if anything)
5. Team size - Headcount available for sales, CS, marketing
6. Target buyer - Developer, operator, executive, SMB owner
7. Funding stage - Bootstrapped, seed, Series A+, profitable
8. Current CAC and payback - If known
9. Biggest bottleneck - Pipeline, conversion, expansion, churn

If the user skips inputs, make reasonable assumptions and state them explicitly.

Examples

Example 1: Ask for the upstream workflow directly

Use @sales-motion-design to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @sales-motion-design against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @sales-motion-design for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @sales-motion-design using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

• User says: "Should we be PLG or sales-led?" → Result: Agent asks ACV and product complexity; uses cheat sheet (e.g. ACV <$1K simple → Pure PLG; $10–50K → Hybrid); recommends TTFV target by category (API <5 min, workflow <15 min, enterprise <1 day) and LTV:CAC 3:1 minimum.
• User says: "Our free users don't convert" → Result: Agent checks activation (target >40% reach value moment) and PQL definition; suggests value-before-purchase design and upgrade pressure at limit; warns on sales calling PQLs too early in hybrid.
• User says: "Design our sales motion" → Result: Agent maps current state (inbound/outbound/PLG); recommends motion from ACV and complexity; outlines TTFV, NRR, self-serve % targets; ties to ai-pricing and gtm-metrics.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(gtm)/sales-motion-design, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: 10. Common Mistakes

Imported: Troubleshooting

• Over or under-serving → Cause: Same process for $5K and $500K deals. Fix: Segment by ACV; self-serve for low, AE for high; define PQL and when sales enters.
• Hybrid kills PLG trust → Cause: Sales touching PQLs too early. Fix: Let product drive activation first; sales on expansion or when multi-stakeholder; clear handoff criteria.
• Conversion dead zone → Cause: Pricing gap between tiers too large. Fix: Add mid tier or usage-based step; aim for >25% PQL conversion; test price sensitivity.

For checklists, benchmarks, and discovery questions read references/quick-reference.md when you need detailed reference.

Related Skills

• @accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• quick-reference.md
• quick-reference.md

Imported Reference Notes

Imported: 1. The Motion Selection Matrix

Choose your primary motion based on two axes: price and complexity.

                     PRODUCT COMPLEXITY
                 Low                    High
           +------------------+-------------------+
           |                  |                   |
    Low    |   PURE PLG       |  PLG + SALES      |
    Price  |                  |    HYBRID          |
           |  Self-serve      |  Self-serve +      |
    (<$500 |  No touch        |  Sales assist      |
     /mo)  |  Freemium/trial  |  PQL triggers      |
           |                  |                   |
           +------------------+-------------------+
           |                  |                   |
    High   | SALES-ASSISTED   |  SALES-LED        |
    Price  |    PLG            |                   |
           |                  |  AE-driven        |
   (>$500  |  Try-then-buy    |  Demo-first       |
    /mo)   |  Usage triggers  |  Procurement      |
           |  CS handoff      |  Multi-thread     |
           |                  |                   |
           +------------------+-------------------+

Decision criteria beyond price x complexity

Scoring: 7+ PLG signals = pure PLG. 4-6 = hybrid. 0-3 = sales-led.

Imported: 2. Motion Archetypes in Detail

2A. Pure PLG

When it works: Low price, low complexity, user = buyer, fast TTFV.

Examples: Notion, Canva, Calendly, Loom, Figma early days.

Conversion funnel:

Visit -> Sign up -> Activate -> Engage -> Convert -> Expand
                                                      |
                                            (product handles all)

Key metrics and benchmarks:

Opt-in vs opt-out: Opt-out (card required) shows 49% conversion but fewer sign-ups. Opt-in (no card) shows 18% but higher volume. Use opt-out only when TTFV < 5 min and activation rate > 40%.

Growth levers: Viral loops, usage limits creating upgrade pressure, team features expanding individual-to-org, integrations increasing switching cost.

Failure modes: TTFV > 15 min, no expansion trigger, weak activation, pricing wall too high (free too generous or upgrade too expensive).

2B. PLG + Sales Hybrid

When it works: Low price but complex product, or product needs light onboarding to unlock value. Most common motion in 2025-2026.

Examples: Slack, Datadog, Twilio, Vercel, Linear.

Conversion funnel:

Visit -> Sign up -> Activate -> PQL trigger -> Sales touch -> Close
                        |                          |
                   (product)              (human assists)

What triggers the sales touch (PQL signals):

• Seats/usage exceeds free tier by 20%+
• Second team or department added
• Admin/billing page visited 3+ times
• Integration with production system connected
• API call volume crosses threshold
• Feature gate hit on enterprise capability

PQL vs MQL performance comparison:

Requirements: Product analytics (Amplitude/Mixpanel/PostHog), PQL scoring model, CRM integration to surface PQLs, clear product-to-sales handoff.

Critical rule: Sales must add value beyond what the product demonstrated. Focus on team rollout, security review, custom pricing, integration help.

2C. Sales-Assisted PLG

When it works: Higher price, simple enough for try-before-buy. Examples: Figma Enterprise, GitHub Enterprise, Airtable Enterprise.

Bottom-up adoption triggers top-down sale. Free individual tier ($0-20/user/mo) feeds adoption. Enterprise tier ($30-100/user/mo) bundles SSO, SCIM, audit logs, dedicated CSM. The gap creates a natural sales conversation.

Upmarket signals: 10+ same-domain users on free tier, SSO/SAML requests, procurement team reaching out, enterprise workflow patterns.

2D. Sales-Led

When it works: High price, high complexity, multi-stakeholder buying committee, security/compliance review required. Examples: Salesforce, Workday, Snowflake (enterprise), Palantir.

Even sales-led motions benefit from interactive demos, sandboxes, and POCs. The difference is a human guides the process rather than the product alone.

2E. Agent-Led Discovery (Emerging, 2025-2026)

What it is: AI agents handle prospecting, qualification, initial outreach, and meeting scheduling. Humans handle discovery calls, demos, negotiation, and closing.

Current reality check (2026 data):

Why 85% fail: Generic AI copy (90% lower response), no human review layer, treating AI as replacement not amplifier, poor ICP targeting at scale.

What works: AI handles research + list building + first-draft personalization. Human reviews before sending. AI handles sequencing + scheduling. Human handles all live conversations.

Implementation tiers:

*Tier 3 has 85% failure rate. Only viable with tight ICP, simple product, low ACV.

Recommendation: Start Tier 1. Move to Tier 2 after 90+ days of positive reply rates. Avoid Tier 3 unless ACV < $1K.

Imported: 3. Value-Before-Purchase Experiences

Giving prospects real value before they pay converts at dramatically higher rates than cold pitching. This applies across all motion types.

Value-before-purchase tactics ranked by conversion lift

Implementation notes

Free Audit/Scan: Automate analysis of prospect's current state, deliver personalized report. Cost: 2-4 weeks engineering. Prospect gets real value, you get a qualified signal.

Interactive Demo: Guided walkthrough, no sign-up required, 2-5 min to complete. 18% of B2B SaaS sites now have one (up 40% YoY). Tools: Navattic, Storylane, Arcade, Consensus. Must end with value moment, not sign-up wall.

Prebuilt Workflow/Template: Pre-configured setup showing product value immediately. Reduces TTFV from hours to minutes. Must solve a real problem.

Sandbox: Full product access with sample data pre-loaded, resettable. Best when product requires data to demonstrate value. Must feel real.

Choosing the right tactic

• Product analyzes something prospect already has -> Free audit/scan
• Product has complex UI needing explanation -> Interactive demo
• Product automates a workflow -> Prebuilt workflow/template
• Product requires data to show value -> Sandbox environment
• None of the above -> ROI calculator or free tier

Imported: 4. Time-to-First-Value (TTFV) as North Star

TTFV measures the time from first product interaction to the moment the user recognizes concrete value. Every extra minute in TTFV increases churn probability. Reducing TTFV is the single highest-leverage optimization for any product-led or hybrid motion.

TTFV benchmarks by product type

TTFV optimization steps

1. MAP - Record 10 new user sessions, identify every step to value moment
2. ELIMINATE - Email-only sign-up, skip surveys, pre-fill defaults
3. PRELOAD - Sample data, templates, pre-connected integrations
4. GUIDE - Checklist UI, contextual tooltips, action-oriented empty states
5. MEASURE - Activation rate, time-to-activate, segment by source/persona

TTFV anti-patterns

Imported: 5. Hybrid Motion Architecture

The hybrid (product-led sales) motion is the dominant model in 2025-2026. Pure self-serve struggles to move upmarket. Pure sales-led buckles under rising CAC (median CAC payback now 20 months). The winning approach combines both.

Hybrid motion structure

ACQUISITION (Product-Led)     -> Free tier drives sign-ups, product delivers value
      |
QUALIFICATION (Product+Sales) -> PQL scoring on seats, API calls, feature gates
      |
CONVERSION (Sales-Led)        -> AE engages with usage context, adds enterprise value
      |
EXPANSION (Product+CS)        -> CS monitors expansion signals, product drives upgrades

When to add sales to PLG

Do not hire sales too early. Add sales only when you see these signals:

Hybrid team structure

First sales hire must be product-savvy, able to do technical demos. Not a traditional AE running MEDDIC on cold prospects.

Hybrid metrics

Imported: 6. CAC Benchmarks and Efficiency

CAC reduction by timeline:

• Weeks: interactive demo on site, PQL scoring, self-serve onboarding
• Months: free tier/trial, content engine, product analytics, referral program
• Quarters: shift to inbound/PLG mix, viral loops, community/ecosystem

Imported: 7. Motion Migration Paths

PLG to Hybrid (trigger: enterprise users stalling at procurement):

1. Instrument PQL signals (seats, usage, feature gates)
2. Define threshold (e.g., 5+ active users from same domain)
3. Hire product-savvy AE, build enterprise tier (SSO, admin, compliance)
4. CRM integration to surface PQLs. Target: 25%+ PQL-to-close rate

Sales-Led to Hybrid (trigger: CAC payback > 20 months):

1. Build free/trial tier for self-qualification
2. Interactive demo on website, usage tracking in free tier
3. Train AEs to leverage usage data. Target: 20-30% CAC reduction in 2 quarters

Pricing alignment:

Imported: 8. Free Trial vs Freemium Decision

Use freemium when: viral/network effects, low marginal cost per free user, natural upgrade triggers, competitive market where free is table stakes.

Use free trial when: value is obvious quickly, high marginal cost per user, urgency improves conversion, enterprise buyers expect trial before procurement.

Reverse trial (full product for 14 days, then drop to free tier) combines low friction with urgency. Works when premium features are clearly valuable.

Industry-specific trial-to-paid rates

Imported: 9. Stage-Specific Playbooks