Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Design and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Raising prices is the highest-leverage move most companies are too scared to make well. Done carelessly it churns your base and lights up Twitter. Done well it's nearly pure margin. This skill plans the change end to end — the math, the segmentation, the grandfathering call, and every word customers will read.
Pair it with product-launch-war-room (to decide whether) and customer-panel-of-experts (to hear how each segment reacts). This skill owns the how.
Gather (read-only from billing/CRM where connected — Stripe, Mercury, Supabase via $SUPABASE_TOKEN; otherwise ask):
Security: read-only. Never modify subscriptions, prices, or send anything. No real customer PII in artifacts — segment in aggregate. Secrets in env vars only.
Split the base into action groups, e.g.:
For each: current price, proposed price, % change, estimated churn response, net revenue impact.
Build a simple, transparent model (write it out so the math is auditable — never hand-wave aggregates):
State assumptions explicitly and label them assumptions. If billing data is connected, ground the model in real numbers and cite them; if not, mark it as estimated and tell the user what data would tighten it.
# Price Change Plan — {Company}
Generated: {timestamp} · Grounding: {billing-data / estimated}
## Recommendation
Increase {X%} on {segments}, {grandfathering policy}, {notice}, effective {date}.
Expected: +{$} net revenue, {Y%} churn risk, break-even at {Z%} churn.
## Segment plan (table)
## Scenario model (table + assumptions)
## Rollout timeline & sequencing
## Risk register & rollback triggers
## Comms — ready to send
### Customer email (warm, value-first, no apology-for-existing tone)
### In-app / banner notice
### Sales talk track (for accounts that push back)
### Support FAQ + objection handling ("why is it going up", "I'll cancel", "match my old price")
### Internal brief (so the team answers consistently)
Lead every customer-facing message with value delivered, then the change, then the path forward — never apologize for charging fairly. Match the user's voice: direct, confident, no corporate fluff.
Offer to run the email and talk track through prospect-panel-simulator / customer-panel-of-experts before sending, and to set the rollback triggers as a monitoring checklist.
Read-only on billing — never change a subscription · model is transparent and assumptions are labeled · grandfathering and rollback are explicit decisions · comms lead with value, never apology · no real customer PII in artifacts.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
中文简介正在准备中。当前显示原文说明。
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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