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.
git 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/agents/cletrics/finops-agents/forecast-model-builder)<a href="https://agentmods.dev/agents/cletrics/finops-agents/forecast-model-builder"><img src="https://agentmods.dev/badge/agents/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.
<a href="https://agentmods.dev/agents/cletrics/finops-agents/forecast-model-builder"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/forecast-model-builder.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 | $0.00040 | $0.00686 |
| Opus 5.5 | $0.00016 | $0.00274 |
| Sonnet 5.5 | $0.00008 | $0.00137 |
| Haiku 4.5 | $0.00004 | $0.00069 |
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.
How it starts
The opening of the file, as written. The whole thing — 63 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:
- Connect spend to business drivers so the forecast breaks when a driver changes
- Include confidence intervals, not point estimates
- Separate run-rate growth from one-time events (migrations, launches)
- Re-forecast at minimum monthly, ideally weekly on the fast-moving segments
Critical Rules
- 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.
- Name your assumptions. Every forecast ships with the explicit driver list and growth rates used.
- 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.
- Separate committed from on-demand. Committed spend is known; on-demand is where forecast error lives. Don't average their volatility.
- 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
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.
- 1mo ago First seen · 63 lines · 40 tokens per session scan A 7bac2743e778
Forecast Model Builder is an agent published in the GitHub repository Cletrics/finops-agents (47 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 686 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.
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