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 varunk130/ai-gtm-skill-library --skill revenue-forecastinggit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/revenue-forecasting)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/revenue-forecasting"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/revenue-forecasting/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/varunk130/ai-gtm-skill-library/revenue-forecasting"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/revenue-forecasting.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 | $0.00064 | $0.01361 |
| Opus 5.5 | $0.00026 | $0.00544 |
| Sonnet 5.5 | $0.00013 | $0.00272 |
| Haiku 4.5 | $0.00006 | $0.00136 |
Grade A, and why
revenue-forecasting 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue Forecasting (FORECAST Framework)
Design a revenue-forecasting pipeline that produces a defensible, calibrated number - not a rep-roll-up that's been over-promised twice. FORECAST blends bottoms-up pipeline math with a tops-down model, runs scenarios, and closes the loop with calibration so the forecast improves quarter over quarter.
Core Principle
A forecast is only as good as its calibration loop. Most forecasts re-anchor every quarter and never learn. FORECAST treats forecasting as an ensemble of models with explicit error tracking, so the system gets more accurate over time.
The FORECAST Framework
| Letter | Stage | The Question |
|---|---|---|
| F | Foundations | What's the ARR / bookings definition, period boundary, and currency convention? |
| O | Outlook (Bottoms-Up) | What does pipeline-weighted by stage and rep commit produce? |
| R | Run-Rate Model | What does the time-series / cohort model produce independent of pipeline? |
| E | Ensemble Blend | How are bottoms-up and tops-down blended, and what's the confidence band? |
| C | Calibration | What's the historical forecast error by segment, stage, and rep? |
| A | Adjust | What manual adjustments are in, and which are evidence-based vs hope-based? |
| S | Scenarios | What are the base / upside / downside cases and their drivers? |
| T | Track | How is forecast vs actual tracked, and how does it feed back into the model? |
Bottoms-Up Forecast
| Element | Spec |
|---|---|
| Stage Conversion | Historical conversion % from each stage to closed-won, refreshed quarterly |
| Time-in-Stage Decay | Probability decay for opportunities aging past expected stage duration |
| Rep Commit Categories | Commit / Best Case / Pipeline / Omitted with named definitions |
| Coverage Multiples | 3x for new logo, 1.2-1.5x for renewal, segment-specific |
| Hygiene Rules | Stale opps demoted, no-next-step opps flagged, close-date discipline |
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 · 120 lines · 64 tokens per session scan A e258a03003d3
revenue-forecasting is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 64 tokens to every session and 1,361 once invoked, about $0.0003 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 2026-08-31.
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