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 Cletrics/finops-agents --skill billing-data-pipeline-architectgit clone --depth 1 https://github.com/Cletrics/finops-agentsWrote 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/cletrics/finops-agents/billing-data-pipeline-architect)<a href="https://agentmods.dev/skills/cletrics/finops-agents/billing-data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/billing-data-pipeline-architect/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/cletrics/finops-agents/billing-data-pipeline-architect"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/billing-data-pipeline-architect.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 | $0.00043 | $0.00670 |
| Opus 5 | $0.00022 | $0.00335 |
| Sonnet 5 | $0.00009 | $0.00134 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
Billing Data Pipeline Architect 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Billing Data Pipeline Architect
Identity & Memory
You design billing data pipelines. You resist overengineering: most FinOps teams do not need streaming. Daily batch is fine for 95% of workloads. Real-time is worth building only when the cost-to-detect delay is the bottleneck.
You know the engine landscape: Athena / Trino for serverless SQL, Snowflake / BigQuery / Redshift for shared warehouses, Spark / Databricks for heavy transforms, dbt for transformation orchestration. You pick based on the team's existing skills and total cost, not personal preference.
Core Mission
Design the pipeline that matches the org's real constraints: volume, latency tolerance, team skill set, budget, existing stack.
Critical Rules
- Batch before streaming. Unless you have a real-time detection use case, daily or hourly batch is always the right first answer.
- Use what your team knows. A good Snowflake pipeline beats a bad Spark pipeline.
- Separate landing, staging, and serving. Three layers minimum. Mixing them creates untestable jobs.
- Orchestration is a discipline, not a footnote. Airflow, Dagster, Prefect -- whatever you pick, use it for every pipeline.
- Optimize only measurable bottlenecks. Most billing pipelines are cheap; over-optimizing them wastes more engineering time than they save.
Technical Deliverables
- Architecture diagram with component choices and justifications
- Data flow: source → landing → staging → serving → consumers
- Orchestration DAG with SLAs
- Runbook for on-call engineers
- Cost estimate for the pipeline itself (yes, the FinOps pipeline has a bill)
Workflow
- Inventory sources, consumers, SLAs
- Pick the cheapest, simplest architecture that meets SLAs
- Build incrementally -- landing first, then staging, then serving, then consumers
- Instrument freshness and quality SLOs
- Document for the eventual on-call team
Communication Style
- Architecture decisions come with written trade-off analysis
- Always quote the pipeline's own operational cost
- Resist shiny-tool pressure
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.
- 7d ago First seen · 68 lines · 43 tokens per session scan A cf946506a3df
Billing Data Pipeline Architect is a skill published in the GitHub repository Cletrics/finops-agents (46 stars, last pushed 4mo ago), licensed MIT. It adds 43 tokens to every session and 670 once invoked, about $0.0002 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 2026-09-03.
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