Agent Skills
› mohitagw15856/pm-claude-skills
› agent-era-pricing
agent-era-pricing
GitHub针对Agent时代重新设计定价策略，解决按席位计费失效问题。输出价值指标决策、Agent层级设计、收入蚕食测算及分阶段迁移计划，帮助在自动化趋势下保护并优化营收。
Trigger Scenarios
客户因使用Agent导致席位减少
需迁移至用量或结果导向定价
为Agent或API层级定价
防御客户自动化带来的收入流失
Install
npx skills add mohitagw15856/pm-claude-skills --skill agent-era-pricing -g -y
SKILL.md
Frontmatter
{
"name": "agent-era-pricing",
"description": "Redesign seat-based pricing for the agent era — when one human runs ten agents, per-seat models collapse. Use when agents are eroding seat counts, when asked to migrate to usage- or outcome-based pricing, to price an agent\/API tier, or to defend revenue as customers automate their own usage. Produces a pricing migration plan: the new value metric, fences, agent-tier design, cannibalisation math, and a phased migration for existing customers. For general pricing and packaging strategy use pricing-strategy."
}
Agent Era Pricing Skill
Seat pricing quietly assumed the user was a human who logs in. Agents break the assumption from both sides: your customers need fewer seats (one operator, ten agents), and your product gets more usage than ever. This skill redesigns the model around a value metric that survives non-human users — without torching existing revenue on the way.
What This Skill Produces
- A value-metric decision: what you charge for when seats stop proxying value
- Agent-tier design: how agent/API usage is packaged, fenced, and priced
- Cannibalisation math: what happens to current revenue under the new model, computed on real cohorts
- A phased migration plan for existing customers, with the grandfathering decision made explicitly
Required Inputs
Ask for (if not already provided):
- Current model: plans, price points, seat definitions, current API/automation pricing if any
- The evidence of pressure: seat contraction, API traffic growth, customer asks, competitor moves
- Unit economics: cost to serve a seat vs an API call/agent action (rough is fine, labelled)
- 3-5 representative customer profiles with seat counts and usage (the cannibalisation test set)
Method
- Find the value metric that survives agents. Test candidates against three questions: does it scale with the value the customer receives (not your costs)? · is it counted identically whether a human or agent drives it? · can the customer predict their bill? Strong candidates are usually outcomes or work-objects (invoices processed, tickets resolved, campaigns run, records enriched) — not raw API calls (unpredictable, punishes retries) and not seats (dying assumption).
- Price the human and the agent differently, deliberately. The durable pattern is a hybrid: a platform/human layer (flat or few-seats — access, admin, support) plus a work layer priced on the value metric, agnostic to who did the work. Decide where agents authenticate: agent traffic on a user's token counted as that user's work, not as a "seat".
- Design the fences. What separates tiers now that seats don't: volume bands on the value metric, rate/concurrency limits, SSO/audit/compliance (still human-org fences), model/automation quality tiers. Every fence must be measurable and hard to game — name the gaming vector for each and why it's acceptable.
- Run the cannibalisation math on real cohorts. For each customer profile: current annual price vs new-model price at current usage, at 2× automation, at 5×. Sum to a revenue bridge. If the new model loses money on your best cohort, the metric or the bands are wrong — fix the model, don't hide the row.
- Phase the migration. New customers first (cleanest signal) → opt-in for existing (with a calculator showing their number) → forced migration only with long notice and a cap ("no more than X% increase in year one"). Grandfathering is a decision with a cost, not a default: state what perpetual legacy plans cost in five years.
- Set the tripwires. Which metrics reprice this model: value-metric inflation/deflation, gaming detected, agent share of traffic crossing thresholds. Pricing in the agent era is a program, not a project.
Output Format
Agent-Era Pricing Plan: [product]
Diagnosis: [the seat-erosion evidence, quantified] Value metric: [chosen metric] — because [the three-question test, answered]. Rejected: [runner-up + why].
The model
| Layer | What's included | Priced on | Tiers/bands |
|---|---|---|---|
| Platform (humans) | |||
| Work (human or agent) |
Fences: [fence → what it separates → gaming vector → why acceptable]
Cannibalisation bridge
| Cohort | Today | New @ current usage | New @ 2× automation | Δ |
|---|
Migration: [phase → who → when → the cap/grandfather decision, stated] Tripwires: [metric → threshold → action]
Quality Checks
- The value metric passes all three tests (customer value · human/agent-agnostic · predictable)
- Cannibalisation is computed on the provided cohorts, not asserted — assumptions labelled
- Every fence names its gaming vector
- The migration includes an explicit grandfathering decision with its long-run cost
- Agent authentication/attribution is specified — whose usage is whose bill
Anti-Patterns
- Do not price raw API calls as the value metric — unpredictable bills punish exactly the automation you want to encourage
- Do not bolt an "agent seat" onto seat pricing — an agent is not a discount human; the assumption is what broke
- Do not present only the happy cohort — the bridge shows the losers or it isn't math
- Do not force-migrate loyal customers without a year-one cap — churn from pricing anger costs more than the uplift
- Do not skip tripwires — a static price in a shifting usage regime is a slow leak in one direction or the other
Version History
- a38bc30 Current 2026-07-05 11:10
