AI Pricing Skill workflow skill. Use this skill when the user needs 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 pricing,' 'BYOK,' 'bring your own key,' 'per-seat pricing,' 'pricing tiers,' 'AI margins,' 'cost per token,' or 'pricing model.' This skill covers pricing strategy, packaging, and margin management for AI-native products. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
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AI Pricing Skill workflow skill. Use this skill when the user needs 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 pricing,' 'BYOK,' 'bring your own key,' 'per-seat pricing,' 'pricing tiers,' 'AI margins,' 'cost per token,' or 'pricing model.' This skill covers pricing strategy, packaging, and margin management for AI-native products. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
references/implementation-guide.md
Starts with the smallest copied file that materially changes execution
Supporting context
references/quick-reference.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
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.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
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.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/implementation-guide.md
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
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.
Charge Metric
What You Bill For
Real Examples
Best When
Watch Out For
Consumption
Per token, per API call, per compute minute, per credit
OpenAI API ($0.01/1K tokens), AWS Bedrock (per-token), Anthropic API
Technical buyer wants granular control; platform/API play
Customers afraid to use product; unpredictable bills kill adoption
Workflow
Per automation run, per agent task, per document processed
Clear time-saving value per task; easy to define boundaries
Must define task boundaries precisely; scope creep erodes margins
Outcome
Per resolved ticket, per qualified lead, per successful match
Intercom Fin ($0.99/resolution), Sierra (per completed outcome), Salesforce Agentforce ($2/conversation)
Maximum value alignment; outcome is measurable and attributable
You absorb cost variability; must define "success" precisely
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.
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
Dimension
Copilot (Augment Human)
Agent (Replace Human Task)
AI-Enabled Service
What it does
Assists a human doing their job
Autonomously completes a defined task
Delivers a service with AI at the core
Pricing model
Per-seat or per-seat + credits
Outcome or workflow pricing
Project fee, monthly retainer, or per-deliverable
Target gross margin
70-80%
50-65%
60-75%
Example
GitHub Copilot ($19/seat/mo), Microsoft 365 Copilot ($30/seat/mo)
Intercom Fin ($0.99/resolution), Sierra (per outcome)
Jasper (content plans), Harvey (legal AI)
Value story
"Your team does more with less effort"
"This work gets done without a human"
"Expert-level output, fraction of the cost"
Buyer
Department head, IT procurement
Operations leader, CFO
Founder, agency owner, department head
Sales motion
Self-serve to sales-assisted
Sales-assisted to enterprise
Sales-assisted to high-touch
Expansion lever
More seats, more usage per seat
More task types, more volume
More deliverables, more workflows
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.
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):
Step
Action
Example
1. Define outcome
What counts as "done"?
Ticket fully resolved without human handoff
2. Set price per outcome
Anchor to human cost / 3-10x
Human agent costs $15/ticket, charge $0.99-2.00
3. Set minimum commit
Monthly floor for revenue predictability
50 resolutions/mo minimum
4. Add volume tiers
Discount at scale, protect margin
1-500: $0.99, 501-2000: $0.79, 2000+: $0.59
5. Define non-outcome
What happens when it fails?
Handoff to human = no charge
Real outcome pricing examples:
Company
Outcome
Price
Human Equivalent Cost
Intercom Fin
Resolved support conversation
$0.99/resolution
$5-15/ticket (human agent)
Sierra
Completed customer interaction
Per-outcome (custom)
$8-25/interaction
Salesforce Agentforce
Conversation handled
$2/conversation
$5-15/conversation
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:
Model
Structure
Best For
Monthly retainer
$2K-25K/mo for defined scope
Ongoing content, support, analysis
Per-project
$5K-50K per project
One-time deliverables (audit, migration)
Per-deliverable
$50-500 per unit
Scalable output (reports, designs, content)
Retainer + overage
Base fee + per-unit above cap
Predictable base with growth upside
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
Pattern
Structure
Example
When to Use
Base + consumption
Platform fee + per-unit overage
$99/mo + $0.05/API call over 10K
API/platform products with variable usage
Base + credits
Platform fee + credit allocation
$199/mo includes 1,000 credits, $0.15/credit after
Multi-feature products with different cost profiles
Base + outcome
Platform fee + per-outcome
$499/mo + $0.99/resolved ticket
Agent products with measurable outcomes
Seat + consumption
Per-seat + usage cap/overage
$30/seat/mo + credits for AI actions
Copilots with heavy AI features
Commitment + burst
Annual commit + on-demand pricing
$50K/yr commit + pay-as-you-go above
Enterprise deals needing budget predictability
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)
Component
Starter
Pro
Enterprise
Monthly platform fee
$199/mo
$599/mo
Custom
Included resolutions
200/mo
1,000/mo
Custom
Overage per resolution
$1.29
$0.89
$0.49-0.69
Channels
Chat only
Chat + email
All channels
SLA
Best effort
99.5% uptime
99.9% + dedicated CSM
Annual discount
15%
20%
Negotiated
For hybrid pricing, BYOK, margin management, tier design, GTM impact, migration, competitive analysis, anti-patterns, and experimentation read references/implementation-guide.md.
