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 forecast-accuracygit 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/forecast-accuracy)<a href="https://agentmods.dev/skills/event4u-app/agent-config/forecast-accuracy"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/forecast-accuracy/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/forecast-accuracy"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/forecast-accuracy.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 | $0.00044 | $0.01952 |
| Opus 5 | $0.00022 | $0.00976 |
| Sonnet 5 | $0.00009 | $0.00390 |
| Haiku 4.5 | $0.00004 | $0.00195 |
Grade A, and why
forecast-accuracy 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 8d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
forecast-accuracy
When to use
- The quarterly forecast call is being constructed and the team needs a categorisation rule that survives retro — not a feel-good number that flatters this week.
- Commit has missed two or more quarters and nobody can name which signals broke — the retro-loop is missing or the categorisation rule is unwritten.
- A new RevOps lead inherits a pipeline and needs to rebuild the forecast call without inheriting last regime's optimism bias.
Do NOT use to design pipeline stages (route to
pipeline-strategy), qualify a single deal (route to
deal-qualification-meddic), or build the finance-side
top-down / bottom-up model (composes against — but does not
duplicate — the finance-partner forecasting capability,
via the forecast-construction-shape interface).
Cognition cluster
- Mental model 16 — Leading vs. lagging indicators. Closed-won
is lagging; per-stage conversion and MEDDIC-slot completeness
are leading. A forecast built on lagging signals can only confirm
the result after it lands. See
docs/contracts/mental-models.md§ 16. - Mental model 29 — Premortem. Before locking the call, write
the post-quarter retro as if commit missed by 20 %. The premortem
surfaces which categorisations are riding on weak evidence; demote
those before the call locks. See
mental-models.md§ 29. - Mental model 9 — Hypothesis-driven thinking. Each commit deal
carries a falsifiable claim: "this closes by <date> because
<evidence>." If the claim cannot be falsified inside the
quarter, the deal is best-case, not commit. See
mental-models.md§ 9. - Context-spine — product + customer-segment. Read the
product slot for what is actually GA-shippable this quarter
(deals depending on non-shipped scope are not commit), and the
customer-segment slot for segment-historical close rates —
pricing-power and cycle-length differ by segment and the forecast
must too. See
context-spine.
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.
- 8d ago First seen · 167 lines · 44 tokens per session scan A f058ea2d3065
forecast-accuracy is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 1,952 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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