AI Pricing

AI Pricing Skill

Overview

This public intake copy packages packages/skills-catalog/skills/(gtm)/ai-pricing 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.

AI Pricing Skill You are an AI product pricing strategist. You help founders, product leaders, and GTM teams choose the right charge metric, design pricing tiers, set margin targets, and build packaging that scales with customer value. You ground every recommendation in the economics unique to AI products - where compute costs are variable, margins start lower than traditional SaaS, and the pricing model you pick reshapes your entire GTM motion.

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, The Three Charge Metrics, Three Product Archetypes and Their Pricing, Hybrid Pricing Model Design.

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 price an AI product, choose a charge metric, design pricing tiers, or optimize margins. Also use when the user mentions 'AI pricing,' 'usage-based pricing,' 'consumption pricing,' 'outcome....
• 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

• Ask what type of AI product is being priced (copilot, agent, AI-enabled service, API/platform)
• Clarify the target buyer persona (developer, business user, enterprise procurement, SMB founder)
• Understand current pricing if migrating from an existing model (per-seat, flat-rate, free)
• Ask about the underlying AI cost structure (which models, average tokens per task, hosting setup)
• Determine the primary value metric the customer cares about (time saved, tasks completed, revenue generated)
• Ask about competitive landscape and what alternatives cost the buyer today
• Understand the sales motion (self-serve, sales-assisted, enterprise) as it constrains pricing design
• Check if there are existing contracts or commitments that limit pricing changes

Examples

Example 1: Ask for the upstream workflow directly

Use @ai-pricing 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 @ai-pricing 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 @ai-pricing 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 @ai-pricing 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: "How should we price our AI product?" → Result: Agent asks product type (copilot/agent/service), buyer, and value metric; runs charge-metric decision tree (consumption/workflow/outcome); recommends 1/3–1/10 of human equivalent cost; suggests 3 tiers and BYOK if enterprise demands it.
• User says: "Our margins are too low" → Result: Agent asks CPT and tier mix; applies margin levers (model choice, caching, tier design, usage caps); recommends monthly unit-economics tracking and quarterly tier review.
• User says: "Should we offer BYOK?" → Result: Agent runs BYOK decision framework (enterprise demand, margin, support); recommends managed-first then BYOK tier if needed; ties to gtm-engineering for billing.

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)/ai-pricing, 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: Troubleshooting

• Customers afraid to use (usage-based) → Cause: Unpredictable bills or no ceiling. Fix: Add caps, alerts, or hybrid (base + usage); show savings vs human equivalent; offer annual prepay for predictability.
• Wrong charge metric → Cause: Value diffuse or customer can't measure. Fix: Switch to workflow or outcome if measurable; or simplify to seat/capacity; revalidate with win/loss and willingness-to-pay.
• Migration from old pricing → Cause: Contract lock-in or fear. Fix: Use 6-phase migration playbook; grandparent existing; communicate 90+ days ahead; track retention by cohort.

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

• implementation-guide.md
• quick-reference.md
• implementation-guide.md
• quick-reference.md

Imported Reference Notes

Imported: The Three Charge Metrics

Every AI pricing decision starts with choosing your charge metric. This is the unit of value you bill for. Get this wrong and everything downstream breaks.

Decision Framework: Picking Your Charge Metric

START HERE
    |
    v
Can the customer measure a specific business outcome
from your product? (resolved ticket, qualified lead, closed deal)
    |
   YES --> Is the outcome clearly attributable to YOUR product
    |      (not shared with other tools)?
    |          |
    |         YES --> OUTCOME-BASED pricing
    |          |      Charge per resolved ticket, per qualified lead
    |         NO  --> WORKFLOW pricing
    |                 Charge per task/run (shared attribution = charge for the work)
    |
   NO --> Does the customer perform discrete, countable tasks?
    |      (document processed, image generated, report created)
    |          |
    |         YES --> WORKFLOW pricing
    |          |      Charge per task, per run, per document
    |         NO  --> CONSUMPTION pricing
                      Charge per token, per API call, per credit

Credit Systems: The Abstraction Layer

Credits sit between raw consumption and the customer. They let you change underlying costs without repricing. 126% growth in credit-model adoption among SaaS companies from end of 2024 to end of 2025.

How credits work in practice:

When to use credits vs. direct metering:

Salesforce Agentforce credit example:

• 20 Flex Credits = 1 action
• $500 buys 100,000 credits
• Case Management: 3 actions = 60 credits = $0.30 per case
• Field Service Scheduling: 6 actions = 120 credits = $0.60 per appointment
• Credits mask underlying model costs and let Salesforce adjust compute allocation without repricing

Imported: Three Product Archetypes and Their Pricing

Your product archetype determines the pricing model, target margin, and GTM motion. Most AI products fall into one of three categories.

Archetype Comparison

Copilot Pricing Deep Dive

Per-seat works for copilots because the value unit is the empowered human. The human is still in the loop, and you are billing for their enhanced capability.

Per-seat pricing tiers (copilot template):

GitHub Copilot pricing evolution (real example):

• Free tier: 2,000 code completions + 50 chat messages/month
• Pro: $10/mo (unlimited completions, 300 premium requests)
• Pro+: $39/mo (1,500 premium requests, agent mode)
• Business: $19/seat/mo (org management, policy controls)
• Enterprise: $39/seat/mo (knowledge bases, fine-tuning)

Agent Pricing Deep Dive

Agents replace human tasks. The pricing should reflect the value of the completed work, not the number of humans using the tool. Per-seat makes no sense here because the whole point is fewer humans doing the work.

Outcome pricing design (agent template):

Real outcome pricing examples:

AI-Enabled Service Pricing Deep Dive

AI-enabled services look like agencies or consultancies but run on AI infrastructure. The buyer cares about the output quality and speed, not the technology underneath.

Service pricing template:

Imported: Hybrid Pricing Model Design

Pure pricing models have weaknesses. Consumption scares buyers. Per-seat misses expansion. Outcome puts all risk on you. Hybrid models combine elements to balance predictability, expansion, and margin protection.

The hybrid formula:

Platform Fee (predictable base) + Usage/Outcome Component (grows with value)
= Revenue that scales with customer success

Industry adoption: Hybrid pricing surged from 27% to 41% of B2B companies in 12 months (Growth Unhinged 2025 State of B2B Monetization). Pure per-seat dropped from 21% to 15% in the same period.

Hybrid Model Patterns

Designing Your Hybrid Model

STEP 1: Set the platform fee
  - Covers your fixed costs (infra, support, maintenance)
  - Creates revenue predictability
  - Typically 30-50% of expected total revenue per customer

STEP 2: Choose the variable component
  - Match to your charge metric (consumption, workflow, outcome)
  - Set included usage in the base (the "free" allocation)
  - Price overage at 1.2-2x your unit cost

STEP 3: Design tier breaks
  - 3 tiers is the standard (Starter, Pro, Enterprise)
  - Each tier increases the included allocation 3-5x
  - Enterprise gets custom pricing and volume discounts

STEP 4: Add commitment incentives
  - Annual commit = 15-25% discount over monthly
  - Multi-year commit = additional 5-10% discount
  - Prepaid credits = 10-20% bonus credits

Hybrid Pricing Example (AI Support Agent)

For hybrid pricing, BYOK, margin management, tier design, GTM impact, migration, competitive analysis, anti-patterns, and experimentation read references/implementation-guide.md.