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 gokulb20/crewm8-cfo-skills --skill forecast-accuracy-trackinggit clone --depth 1 https://github.com/gokulb20/crewm8-cfo-skillsWrote 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/gokulb20/crewm8-cfo-skills/forecast-accuracy-tracking)<a href="https://agentmods.dev/skills/gokulb20/crewm8-cfo-skills/forecast-accuracy-tracking"><img src="https://agentmods.dev/badge/skills/gokulb20/crewm8-cfo-skills/forecast-accuracy-tracking/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/gokulb20/crewm8-cfo-skills/forecast-accuracy-tracking"><img src="https://agentmods.dev/badge/skills/gokulb20/crewm8-cfo-skills/forecast-accuracy-tracking.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 | $0.00086 | $0.02776 |
| Opus 5 | $0.00043 | $0.01388 |
| Sonnet 5 | $0.00017 | $0.00555 |
| Haiku 4.5 | $0.00009 | $0.00278 |
Grade A, and why
forecast-accuracy-tracking 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 11d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecast Accuracy Tracking
Measure how good the company's forecasts actually are. Track forecast vs actuals over time, identify where and why you're consistently wrong, and improve the forecasting process. Goal: forecasts get more accurate over time, not less, and the business learns from its mistakes.
Purpose
Most startups track whether they hit their plan, but almost none track whether their forecasts are actually getting better. Systematic forecasting errors — consistently overestimating new logo revenue, consistently underestimating churn — compound over time and lead to bad decisions. This skill provides the methodology to measure forecast accuracy, identify persistent biases, and improve the process so decisions are made on increasingly reliable numbers.
When to Use
- "How accurate were our forecasts?"
- "Track forecast vs actuals over time"
- "Why were we off last quarter?"
- "Improve our forecasting process"
- "What's our forecast track record?"
- "Analyze forecast bias"
Inputs Required
- Historical forecasts — for each forecasting exercise: what was forecast, when, for what period, and the forecast value.
- Actual results — the realized values for each forecasted metric (revenue, costs, cash, headcount).
- Forecast assumptions — the key assumptions that drove each forecast (growth rate, churn, hiring velocity, etc.).
- Contextual notes — what was happening in the business when each forecast was made (new product launch, market shift, team changes).
Quick Reference
| Metric | Formula | Target |
|---|---|---|
| MAPE | Average of |Actual − Forecast| / |Actual| across periods | < 10% excellent, 10-25% decent |
| Forecast Bias | Average of (Forecast − Actual) / Actual | Near 0 (neutral) |
| FE (Forecast Error) | Actual − Forecast | Positive = conservative, Negative = optimistic |
| Horizon MAPE | MAPE broken out by 1mo/3mo/6mo/12mo horizons | Higher for longer horizons |
| MAPE Range | Interpretation |
|---|---|
| < 10% | Excellent forecasting |
| 10-25% | Decent — room for improvement |
| 25-50% | Concerning — systematic issues likely |
| > 50% | Forecasting is essentially guessing |
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.
- 11d ago First seen · 226 lines · 86 tokens per session scan A 5d1cdb511ca5
forecast-accuracy-tracking is a skill published in the GitHub repository gokulb20/crewm8-cfo-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 86 tokens to every session and 2,776 once invoked, about $0.0004 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 2026-08-31.
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