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Ai Agent Commercial Operations

ASecurity

Use when pricing, billing, refunding, recognizing revenue, or packaging commercial terms for agentic AI services, or when defining agent SLAs, customer commitments, SLA dashboards, breach credits, and service evidence.

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  • Added May 27, 2026
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Scanned October 1, 2026

npx -y skills add peterbamuhigire/skills-web-dev --skill ai-agent-commercial-operations --agent claude-code

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SKILL.md
---
name: ai-agent-commercial-operations
description: Use when pricing, billing, refunding, recognizing revenue, or packaging commercial terms for agentic AI services, or when defining agent SLAs, customer commitments, SLA dashboards, breach credits, and service evidence.
metadata:
  portable: true
  compatible_with:
  - claude-code
  - codex
---

# AI Agent Commercial Operations

## Operating contract

## Inputs

| Input | Required | Purpose |
|---|---|---|
| Domain evidence | yes | offer catalogue, pricing metric, usage ledger, contract terms, refund policy, and accounting jurisdiction |
| SLA evidence | when commitments or credits apply | service boundary, customer tier, measurable SLI data, support coverage, exclusions, and credit policy |

## Outputs

- Produce: priced offer, billing rules, refund decision matrix, revenue-treatment handoff, and commercial control register.
- When commitments apply, also produce: SLA schedule, SLI/SLO definitions, dashboard specification, breach and credit workflow, and customer evidence.

## Capability and permission boundaries

Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.

## Degraded mode

When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.

## Decision rules

| Condition | Action |
|---|---|
| Scope, owner, or threshold is missing | Stop the affected decision and request it |
| Evidence is incomplete but read-only analysis is safe | Produce a qualified partial result and gap list |
| A mutation exceeds authority or tenant boundary | Block it and route for approval |
| Evidence meets the stated threshold | Issue the output with provenance and owner |

## Anti-Patterns

- Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
- Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
- Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
- Reporting a metric without population, window, or source. Fix: attach all three.
- Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

<!-- dual-compat-start -->

## Use When

- Design agent pricing, attempted-vs-completed billing, refunds, and revenue recognition policy.
- Connect commercial terms to task evidence, completion status, usage records, and customer promises.
- Define fair billing boundaries for abandoned, failed, partial, or human-approved agent tasks.
- Define agent availability, completion, response-time, quality, and support commitments (SLAs).
- Design SLA credit automation and customer-facing service dashboards; map task evidence to support, credits, renewals, and account reviews.

## Do Not Use When

- The work is not AI-specific or agentic-AI-specific.
- A narrower retained AI parent skill fits the request better.

## Required Inputs

- Product, tenant, user, data, risk, and operational context relevant to the AI workflow.
- Target artifact: design, implementation plan, audit, test strategy, UX flow, commercial policy, or runbook.
- Constraints from security, privacy, reliability, billing, support, and compliance stakeholders when relevant.

## Workflow

1. Read this SKILL.md first.
2. Load [references/routing.md](references/routing.md) to select the absorbed child reference that matches the task.
3. Load only the selected child reference files needed for the current request.
4. Produce execution-oriented output with assumptions, risks, evidence, and next actions where relevant.

## Quality Standards

- Keep routing explicit: name which reference files were used when the work depends on absorbed material.
- Preserve tenant isolation, auditability, cost controls, safety gates, and operational evidence when they matter.
- Prefer concrete contracts, checklists, tables, schemas, runbooks, and decision records over broad summaries.

## Anti-Patterns

- Loading every absorbed reference by default.
- Treating AI-specific billing, compliance, safety, or UX concerns as generic SaaS work without checking AI failure modes.
- Hiding retired skill names; old slugs must remain discoverable through [references/routing.md](references/routing.md).

## Outputs

- A concrete deliverable matched to the request: architecture, implementation plan, audit, policy, runbook, UX flow, test strategy, or operating model.
- The selected consolidated reference files and any assumptions, risks, evidence requirements, or follow-up actions that affect execution.
## References

- [references/routing.md](references/routing.md) maps retired child skill slugs to their consolidated reference folders.
- Load [references/provider-recourse-and-multi-agent-attribution.md](references/provider-recourse-and-multi-agent-attribution.md) when a breach or credit involves an upstream provider, or when several agents share one billed task.
- SLA work: load `references/ai-agent-sla-and-commitments/`, `references/ai-agent-sla-credit-automation/`, or `references/ai-agent-customer-sla-dashboard/` through [references/routing.md](references/routing.md).
## Consolidated Child References

- Load [references/routing.md](references/routing.md) to map retired AI child skill slugs to their reference modules.
## Evidence Produced

| Category | Artifact | Format | Example |
| --- | --- | --- | --- |
| Correctness | Agent commercial event reconciliation | Markdown table plus query evidence | attempted, completed, credited, refunded, and recognised amounts reconcile by tenant |
| Operability | SLA measurement and credit evidence | Markdown table plus calculation | service class, exclusion, breach window, credit calculation, approval, and customer notice |

<!-- dual-compat-end -->

Files in this skill

  • SKILL.md2.7 KB
  • references/ai-agent-abandonment-and-refund-policy/entrypoint.md10.9 KB
  • references/ai-agent-abandonment-and-refund-policy/references/abandonment-taxonomy.md7.2 KB
  • references/ai-agent-abandonment-and-refund-policy/references/refund-execution.md10.1 KB
  • references/ai-agent-attempted-vs-completed-billing/entrypoint.md11.9 KB
  • references/ai-agent-attempted-vs-completed-billing/references/attempt-classification.md7.2 KB
  • references/ai-agent-attempted-vs-completed-billing/references/billing-pipeline.md9 KB
  • references/ai-agent-attempted-vs-completed-billing/references/eval-gated-counting.md6.2 KB
  • references/ai-agent-pricing-engine/entrypoint.md10.3 KB
  • references/ai-agent-pricing-engine/references/intervention-credit-logic.md4.7 KB
  • references/ai-agent-pricing-engine/references/price-rule-resolver.md10.4 KB
  • references/ai-agent-pricing-engine/references/vendor-cost-pass-through.md6.1 KB
  • references/ai-agent-revenue-recognition/entrypoint.md10.2 KB
  • references/ai-agent-revenue-recognition/references/asc-606-for-agents.md8.4 KB
  • references/ai-agent-revenue-recognition/references/deferred-revenue-and-refund-reserves.md10.5 KB
  • references/ai-agent-revenue-recognition/references/month-end-close-pipeline.md10.6 KB
  • references/routing.md1.1 KB

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