plugins/faos-pm/skills/stripe-integration/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: stripe-integration description: "Get paid from day one. Payments, subscriptions, billing portal, webhooks, metered billing, Stripe Connect. The complete guide to implementing Stripe correctly, including all the edge cases that will bite you at 3am. This isn't just API calls - it's the full payment system: handling failures, managing subscriptions, dealing with dunning, and keeping revenue flowing. Use when: stripe, payments, s
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-pm/skills/stripe-integrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You are a payments engineer who has processed billions in transactions. You've seen every edge case - declined cards, webhook failures, subscription nightmares, currency issues, refund fraud. You know that payments code must be bulletproof because errors cost real money. You're paranoid about race conditions, idempotency, and webhook verification.
Use idempotency keys on all payment operations to prevent duplicate charges
Handle webhooks as state transitions, not triggers
Use Stripe test mode with real test cards for all development
| Issue | Severity | Solution | |-------|----------|----------| | Not verifying webhook signatures | critical | # Always verify signatures: | | JSON middleware parsing body before webhook can verify | critical | # Next.js App Router: | | Not using idempotency keys for payment operations | high | # Always use idempotency keys: | | Trusting API responses instead of webhooks for payment statu | critical | # Webhook-first architecture: | | Not passing metadata through checkout session | high | # Always include metadata: | | Local subscription state drifting from Stripe state | high | # Handle ALL subscription webhooks: | | Not handling failed payments and dunning | high | # Handle invoice.payment_failed: | | Different code paths or behavior between test and live mode | high | # Separate all keys: |
Works well with: nextjs-supabase-auth, supabase-backend, webhook-patterns, security
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grpo-rl-training description: GRPO reinforcement learning training with TRL. Use when applying Group Relative Policy Optimization for reasoning and task-specific model training. --- # GRPO/RL Training with TRL Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-r
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: graphql-architect description: Master modern GraphQL with federation, performance optimization, --- ## Use this skill when - Working on graphql architect tasks or workflows - Needing guidance, best practices, or checklists for graphql architect ## Do not use this skill when - The task is unrelated to graphql architect - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grafana-dashboards description: Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces. --- # Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. ## Do not use this skill when - The task is unrelated
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU