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 event4u-app/agent-config --skill forecastinggit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/forecasting)<a href="https://agentmods.dev/skills/event4u-app/agent-config/forecasting"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/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/event4u-app/agent-config/forecasting"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/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 | $0.00042 | $0.02073 |
| Opus 5 | $0.00021 | $0.01037 |
| Sonnet 5 | $0.00008 | $0.00415 |
| Haiku 4.5 | $0.00004 | $0.00207 |
Grade A, and why
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 7d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
forecasting
When to use
- The annual plan or quarterly board pack needs a forecast model that survives a retro — not last quarter's number with a multiplier.
- Top-down (TAM × penetration × motion) and bottom-up (deal-level) calls have diverged and the reconciliation hasn't been written.
- A new finance-partner inherits a forecast and needs to rebuild the construction shape without inheriting the prior regime's optimism.
Do NOT use to qualify a single deal (route to deal-qualification-meddic), construct the RevOps commit list (route to forecast-accuracy (H10) — finance owns the shape, RevOps owns the call), or run capital-runway scenarios (route to runway-cognition (O3)).
Cognition cluster
- Mental model 9 — Hypothesis-driven thinking. Each forecast is
a falsifiable claim about a window. If the call cannot be falsified
inside the window, the call is a narrative, not a forecast. See
mental-models.md§ 9. - Mental model 29 — Premortem. Before locking the call, write the
post-window retro as if commit missed by 20 %. The premortem
surfaces which construction inputs were riding on weak evidence;
demote those before the call locks. See
mental-models.md§ 29. - Mental model 16 — Leading vs lagging. Closed-won is lagging;
pipeline coverage, segment conversion, and slot-completeness are
leading. A forecast built only on lagging signals can confirm but
not steer. See
mental-models.md§ 16. - Context-spine — product + fiscal-period + customer-segment.
Read the product slot for what is GA-shippable in the window;
the fiscal-period slot for the cadence the model must
reconcile against (monthly close vs quarterly board pack vs annual
plan vs multi-year plan); the customer-segment slot for
segment-historical close rates. See
context-spine.
Procedure
Step 0: Inspect the construction shape
Read the fiscal-period slot. Decide between three shapes:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 174 lines · 42 tokens per session scan A ad7aaf77cfc1
forecasting is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 2,073 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 2026-09-03.
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