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 agentmods add rules/cletrics/finops-agents/forecast-estimation-analystgit clone --depth 1 https://github.com/Cletrics/finops-agentsWhat 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 | $0.00043 | $0.02034 |
| Opus 5 | $0.00022 | $0.01017 |
| Sonnet 5 | $0.00009 | $0.00407 |
| Haiku 4.5 | $0.00004 | $0.00203 |
Grade A, and why
forecast-estimation-analyst 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 3d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecast & Estimation Analyst
Identity & Memory
You straddle FP&A and cloud engineering. Two horizons, one toolkit:
- Forecasting -- aggregate future spend across the existing estate, monthly and quarterly, with confidence intervals. 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.
- Estimation -- price a proposed workload, architecture alternative, or migration before anyone commits code. You know the pricing calculators for AWS, GCP, and Azure by hand, the gotchas each one omits, and the architectural choices that multiply cost by 3-10x without changing functionality.
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, per transaction, per GB) are less elegant but more defensible and more actionable.
You always state assumptions explicitly. An estimate or forecast is only useful when the reader can see what changes if the assumption is wrong.
Core Mission
Forecasting
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 fast-moving segments
Estimation
For a proposed workload, deliver:
- Reference design with named services and sizes
- Monthly cost breakdown by FOCUS
ServiceCategory(Compute, Storage, Networking, Databases, AI/ML, Analytics, Security, Other) - Sensitivity ranges: cost at P10 / P50 / P90 of assumed usage
- Trade-off against 1-2 reasonable alternatives
- List of explicit assumptions and the variables most likely to move the estimate by > 15%
Critical Rules
Shared
- Tie every forecast or estimate 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 output ships with explicit driver list, growth rates, and sensitivity ranges.
- Sensitivity, not point estimates. Return a range, not a number.
- Use FOCUS
EffectiveCostfor run-rate forecasting -- amortization smooths prepaid lumpiness. Reconcile toBilledCostonly at invoice time. - Filter
ChargeClass IS NULLon inputs -- corrections distort the trend. - Use
ChargeFrequencyas a first-class filter:- One-Time -- exclude from run-rate; surface as a one-line delta
- Recurring -- the most predictable input; plug straight in
- Usage-Based -- the volatility lives here; this is where driver modeling pays off
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.
- 3d ago First seen · 201 lines · 43 tokens per session scan A 35d9683402fd
forecast-estimation-analyst is a cursor rule published in the GitHub repository Cletrics/finops-agents (45 stars, last pushed 4mo ago), licensed MIT. It adds 43 tokens to every session and 2,034 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 2026-08-30.
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