Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add appeeky/stripe-skills --skill pricing-experimentsgit clone --depth 1 https://github.com/appeeky/stripe-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/appeeky/stripe-skills/pricing-experiments)<a href="https://agentmods.dev/skills/appeeky/stripe-skills/pricing-experiments"><img src="https://agentmods.dev/badge/skills/appeeky/stripe-skills/pricing-experiments/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/appeeky/stripe-skills/pricing-experiments"><img src="https://agentmods.dev/badge/skills/appeeky/stripe-skills/pricing-experiments.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.00392 |
| Opus 5 | $0.00033 | $0.00196 |
| Sonnet 5 | $0.00013 | $0.00078 |
| Haiku 4.5 | $0.00007 | $0.00039 |
Grade A, and why
pricing-experiments scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Pricing Experiments
Stripe MCP Setup
Requires Stripe MCP (https://docs.stripe.com/mcp). GetMcpTools before CallMcpTool. Paginate all lists (limit: 100, starting_after while has_more). Amounts in cents. Read-only for audits; confirm before writes.
Workflow
- Baseline: current prices + subs per price (
pricing-products-audit) - Design test:
- New
Priceobject (never edit existing -- create new) - Split traffic at checkout (price ID by cohort/flag)
- Grandfather existing subs (leave on old price)
- New
- Metrics to compare: conversion %, ARPU, churn at 30/60d
Price Increase Playbook
| Step | Action |
|---|---|
| 1 | Create new higher price |
| 2 | New customers -> new price |
| 3 | Existing -> keep or migrate with notice (email + subscriptions.update) |
| 4 | Measure churn delta post-migration |
Output Template
Pricing Test Plan
Hypothesis: [raising X->Y increases ARPU without >Zpp churn]
Variants: control [price_A] vs test [price_B]
Sample size needed: [N per arm]
Success metric: ARPU / conversion / 30d retention
Grandfathering: [policy]
Never mutate a live Price -- always create a new one.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 45 lines · 66 tokens per session scan A 8dccc272c2a2
pricing-experiments is a skill published in the GitHub repository appeeky/stripe-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 392 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…