Produit & Go-to-marketSûr100100/100
Finance Billing Ops
Evidence-first revenue, pricing, refunds, team-billing, and billing-model truth workflow for ECC. Use when the user wants a sales snapshot, pricing comparison, duplicate-charge diagnosis, or code-backed billing reality instead of generic payments advice.
ou envoie-le directement à ton agent.
Installer dans ton projet
$ npx arboris-cli@latest install finance-billing-opsaffaan-m
affaan-m/ECC
Contenu à copier
--- name: finance-billing-ops description: Evidence-first revenue, pricing, refunds, team-billing, and billing-model truth workflow for ECC. Use when the user wants a sales snapshot, pricing comparison, duplicate-charge diagnosis, or code-backed billing reality instead of generic payments advice. metadata: origin: ECC --- # Finance Billing Ops Use this when the user wants to understand money, pricing, refunds, team-seat logic, or whether the product actually behaves the way the website and sales copy imply. This is broader than `customer-billing-ops`. That skill is for customer remediation. This skill is for operator truth: revenue state, pricing decisions, team billing, and code-backed billing behavior. ## Skill Stack Pull these ECC-native skills into the workflow when relevant: - `customer-billing-ops` for customer-specific remediation and follow-up - `research-ops` when competitor pricing or current market evidence matters - `market-research` when the answer should end in a pricing recommendation - `github-ops` when the billing truth depends on code, backlog, or release state in sibling repos - `verification-loop` when the answer depends on proving checkout, seat handling, or entitlement behavior ## When to Use - user asks for Stripe sales, refunds, MRR, or recent customer activity - user asks whether team billing, per-seat billing, or quota stacking is real in code - user wants competitor pricing comparisons or pricing-model benchmarks - the question mixes revenue facts with product implementation truth ## Guardrails - distinguish live data from saved snapshots - separate: - revenue fact - customer impact - code-backed product truth - recommendation - do not say "per seat" unless the actual entitlement path enforces it - do not assume duplicate subscriptions imply duplicate value ## Workflow ### 1. Start from the freshest billing evidence Prefer live billing data. If the data is not live, state the snapshot timestamp explicitly. Normalize the picture: - paid sales - active subscriptions - failed or incomplete checkouts - refunds - disputes - duplicate subscriptions ### 2. Separate customer incidents from product truth If the question is customer-specific, classify first: - duplicate checkout - real team intent - broken self-serve controls - unmet product value - failed payment or incomplete setup Then separate that from the broader product question: - does team billing really exist? - are seats actually counted? - does checkout quantity change entitlement? - does the site overstate current behavior? ### 3. Inspect code-backed billing behavior If the answer depends on implementation truth, inspect the code path: - checkout - pricing page - entitlement calculation - seat or quota handling - installation vs user usage logic - billing portal or self-serve management support ### 4. End with a decision and product gap Report: - sales snapshot - issue diagnosis - product truth - recommended operator action - product or backlog gap ## Output Format ```text SNAPSHOT - timestamp - revenue / subscriptions / anomalies CUSTOMER IMPACT - who is affected - what happened PRODUCT TRUTH - what the code actually does - what the website or sales copy claims DECISION - refund / preserve / convert / no-op PRODUCT GAP - exact follow-up item to build or fix ``` ## Pitfalls - do not conflate failed attempts with net revenue - do not infer team billing from marketing language alone - do not compare competitor pricing from memory when current evidence is available - do not jump from diagnosis straight to refund without classifying the issue ## Verification - the answer includes a live-data statement or snapshot timestamp - product-truth claims are code-backed - customer-impact and broader pricing/product conclusions are separated cleanly
Colle ce Markdown dans ton agent ou utilise les boutons ci-dessus pour l’écrire dans ton projet.
Ce que fait Finance Billing Ops
`customer-billing-ops` for customer-specific remediation and follow-up
`research-ops` when competitor pricing or current market evidence matters
`market-research` when the answer should end in a pricing recommendation
`github-ops` when the billing truth depends on code, backlog, or release state in sibling repos
Comment utiliser Finance Billing Ops
1
Copie le prompt
Un clic copie le prompt packagé (ou l'envoie à ton agent).
2
L'agent installe le skill
Il ajoute le SKILL.md et ses ressources à ton projet.
3
Activation automatique
Le skill s'active dès que le contexte correspond.
Déclencheurs pour Finance Billing Ops
Dis simplement à ton agent quelque chose comme :
Applique le skill finance-billing-ops à cette tâche
Utilise finance-billing-ops pour améliorer cette implémentation
Passe en revue ce sujet avec finance-billing-ops
Skills liés à Finance Billing Ops
Abandonment Analysis (Microsoft)
Helps a Marketing Manager review anonymous synthetic abandonment patterns without contacting shoppers.Add Seo (Microsoft)
Adds SEO essentials to a Power Pages code site, including robots.txt, sitemap.xml, meta tags, Open Graph tags, and favicon configuration. Use when the user wants to improve search engine optimization or make their site more searchable.ai-seo
When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.