ai-cost-and-metering
Use when modeling, metering, attributing, billing, or controlling AI usage costs across tenants, plans, features, providers, and agent workloads.
Best use case
ai-cost-and-metering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when modeling, metering, attributing, billing, or controlling AI usage costs across tenants, plans, features, providers, and agent workloads.
Teams using ai-cost-and-metering should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-cost-and-metering/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-cost-and-metering Compares
| Feature / Agent | ai-cost-and-metering | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when modeling, metering, attributing, billing, or controlling AI usage costs across tenants, plans, features, providers, and agent workloads.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AI Cost And Metering Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178. <!-- dual-compat-start --> ## Use When - Design AI usage metering, cost attribution, quotas, chargeback, or billing controls. - Model AI cost by tenant, feature, provider, model, request, or agent task. - Connect cost evidence to plan limits, entitlement enforcement, and customer-facing billing. ## 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. ## Consolidated Child References - Load [references/routing.md](references/routing.md) to map retired AI child skill slugs to their reference modules. <!-- dual-compat-end -->
Related Skills
web-app-security-audit
Use when auditing a PHP/JavaScript/HTML web application for security vulnerabilities. Covers configuration, authentication, authorization, input validation, XSS, API security, HTTP headers, and dependency scanning. Produces a severity-rated audit...
vibe-security-skill
Use when designing or reviewing security for a web application, API, or multi-tenant SaaS — produces threat model, abuse case list, auth/authz matrix, and secret handling plan; covers OWASP Top 10 2025 and the AI-code-generation blind spots. Neighbours — api-design-first owns auth model fields, deployment-release-engineering owns secret rotation choreography, ai-security and llm-security own model-specific threats.
network-security
Use when designing, hardening, or auditing network-layer security for self-managed Debian/Ubuntu SaaS infrastructure — firewalls (nftables/UFW), WAF (ModSecurity + OWASP CRS), VPN (WireGuard, OpenVPN, IPsec), TLS/PKI ops, IDS/IPS (Suricata, Fail2ban), zero-trust, SSH hardening, DDoS mitigation, DNS security. Complements web-app-security-audit (app layer) and cicd-devsecops (secrets/CI).
linux-security-hardening
Use when hardening a Debian/Ubuntu server — user/group/sudo hardening, file permission audits, PAM password policy + MFA, AppArmor mandatory access control, auditd system call logging, kernel sysctl hardening, file integrity monitoring (AIDE), rootkit detection (rkhunter/chkrootkit), unattended security patching, GRUB + UEFI + LUKS boot security, and CIS benchmark compliance.
dpia-generator
Generate a Data Protection Impact Assessment (DPIA), Uganda DPPA 2019-compliant. Use when producing or reviewing a data protection impact assessment, a privacy impact assessment, when uganda-dppa-compliance flags [DPIA-REQUIRED], or when processing large-scale or sensitive personal data for a new feature.
code-safety-scanner
Scan any codebase for 14 critical safety issues across security vulnerabilities, server stability (500 errors), and payment misconfigurations. Use when auditing code before deployment, reviewing AI-generated code for production readiness, or...
world-class-engineering
Use when designing, building, reviewing, or upgrading production software systems that must be secure, performant, maintainable, scalable, and user-centered. Apply before writing specs, code, architecture, APIs, databases, mobile apps, SaaS platforms, or ERP systems.
update-Codex-documentation
Update project documentation files (README.md, PROJECT_BRIEF.md, TECH_STACK.md, ARCHITECTURE.md, docs/API.md, docs/DATABASE.md, AGENTS.md, docs/plans/NEXT_FEATURES.md) when significant changes occur. MANDATORY at end of each work session to...
skill-writing
Use when creating or upgrading skills in this repository. Covers repository-specific frontmatter rules, progressive disclosure, reference-file strategy, validation, and the quality bar required for production-grade engineering skills.
skill-safety-audit
Scan new or updated skills for unsafe or malicious instructions (unknown tools, external installers, credential harvesting) before accepting them into the repository.
skill-composition-standards
Use when authoring a new skill, normalising an older skill, or reviewing a skill PR — defines the repository-wide house style (frontmatter, decision rules, anti-patterns, references), the output contracts each baseline-skill type must produce, and the input contracts each specialist skill must declare. This is the enforcement spine that makes the repository compose as a system, not a library of linked documents.
sdlc-documentation
Use when producing, reviewing, or consolidating SDLC documentation across planning, requirements, design, testing, deployment, user rollout, post-deployment, and maintenance phases. Load absorbed SDLC phase references as needed.