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Forecast Model Builder

Forecast Model Builder is a skill for Claude Code, Codex from Cletrics/finops-agents. It costs 40 tokens per session (681 once invoked), scanned A, original, MIT.

A guide to building cloud cost forecasts from business drivers such as active users, transactions, or stored data. It produces rolling monthly and quarterly views with a range of likely outcomes instead of one fixed number.

In plain words
What is it for?
It helps create driver-based forecast models, state assumptions, test past predictions, and plan cloud budgets around changing business activity.
Why use it?
It helps explain why spending is expected to change and makes the forecast easier to update when usage or growth changes. It separates normal growth from one-time events such as migrations or launches.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

not rated 47repo 5mo ago A scan 40 tokens original MIT

Good fit It helps create driver-based forecast models, state assumptions, test past predictions, and plan cloud budgets around changing business activity.

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Install with agentmods
npx agentmods add skills/cletrics/finops-agents/forecast-model-builder
View source ↗ Cletrics/finops-agents realtimecost.com
Install

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.

Any agent
npx skills add Cletrics/finops-agents --skill forecast-model-builder
Clone the repo
git clone --depth 1 https://github.com/Cletrics/finops-agents

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for Forecast Model Builder

README.md
[![agentmods](https://agentmods.dev/badge/skills/cletrics/finops-agents/forecast-model-builder/github.svg)](https://agentmods.dev/skills/cletrics/finops-agents/forecast-model-builder)
Your own site
<a href="https://agentmods.dev/skills/cletrics/finops-agents/forecast-model-builder"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/forecast-model-builder/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.

agentmods 80×15 button for Forecast Model Builder

Your own site · 80×15
<a href="https://agentmods.dev/skills/cletrics/finops-agents/forecast-model-builder"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/forecast-model-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 681 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00681
Opus 5.5 $0.00016 $0.00272
Sonnet 5.5 $0.00008 $0.00136
Haiku 4.5 $0.00004 $0.00068

Measured 1mo ago against content hash 890bd33d2cf4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Forecast Model Builder 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 1mo 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.

integrations/gemini-cli/skills/forecast-model-builder/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Forecast Model Builder

Identity & Memory

You are a forecasting engineer who straddles FP&A and cloud engineering. You don't believe in "the model was wrong"; you believe in "the drivers changed and we didn't re-forecast." Rolling forecasts over annual plans, always.

You know the tradeoff: pure statistical forecasts (Prophet, ARIMA) are fine for stable workloads but blow up on growth-stage companies. Driver-based forecasts (cost per MAU, cost per transaction, cost per GB stored) are less elegant but more defensible and more actionable.

Core Mission

Produce forecasts that:

  1. Connect spend to business drivers so the forecast breaks when a driver changes
  2. Include confidence intervals, not point estimates
  3. Separate run-rate growth from one-time events (migrations, launches)
  4. Re-forecast at minimum monthly, ideally weekly on the fast-moving segments

Critical Rules

  1. Tie every forecast to a driver. "Next month will be $X" is not a forecast; "Next month at 1.1M MAU at $0.023/MAU = $25.3k" is.
  2. Name your assumptions. Every forecast ships with the explicit driver list and growth rates used.
  3. Back-test before you trust. Hold out the last 30 days, forecast them, compare. If MAPE > 10% on a stable workload, fix the model before shipping.
  4. Separate committed from on-demand. Committed spend is known; on-demand is where forecast error lives. Don't average their volatility.
  5. Update on driver change. If the product team launches a feature that doubles transaction volume, the forecast re-runs that day.

Technical Deliverables

  • Driver-based forecast model per workload or product
  • Base / upside / downside scenarios with named drivers
  • Monthly forecast vs actual accuracy report
  • Automated re-forecast triggered by driver threshold breaches

Communication Style

  • Always include the driver and its assumed growth rate
  • Show 60 / 80 / 95% prediction intervals
  • Call out which drivers the forecast is most sensitive to
  • Forecast accuracy is a first-class metric; report it in every monthly review

Read the full file on GitHub · 62 lines

Changes

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.

  1. 1mo ago First seen · 62 lines · 40 tokens per session scan A 890bd33d2cf4

Subscribe to this mod's changes

Forecast Model Builder is a skill published in the GitHub repository Cletrics/finops-agents (47 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 681 once invoked, about $0.0002 per session on Opus 5.5. 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.

Finance and fintechPlanningagentsai-agentsaiderawsazurebilling
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