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 commands/ololand-ai/ololand-plugins/record-outcomegit clone --depth 1 https://github.com/ololand-ai/ololand-pluginsWrote 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/commands/ololand-ai/ololand-plugins/record-outcome)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/record-outcome"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/record-outcome.svg" alt="Measured on agentmods" 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 | $0.00065 | $0.02074 |
| Opus 5 | $0.00032 | $0.01037 |
| Sonnet 5 | $0.00013 | $0.00415 |
| Haiku 4.5 | $0.00006 | $0.00207 |
Grade A, and why
record-outcome 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 5d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Record Outcome
Most AI tools forget every deal the moment the session ends. OloLand's flywheel does the opposite: it captures what the model predicted, then — once the deal closes and 12+ months of realized data exist — scores those predictions against reality. That accuracy history is what lets /calibrate-vs-history say "your firm overestimates revenue growth by 7pp in deals like this."
That read side only works if the write side is fed. This command is the write side. It does two things:
- Mints predictions (if the deal doesn't have them yet) by running the deterministic engines and a forecast run — so there is something concrete to grade later.
- Records realized actuals and closes the loop — stamping an accuracy score on each
enterprise_value/irr/moicprediction.
Usage
/record-outcome <deal_id>
Two distinct moments call for it:
- At underwriting / IC — mint the predictions so the deal is on the books to be graded later. (Steps 1-3 below; skip the actuals.)
- Post-close (12+ months out) — record the realized exit and close the loop. (Steps 4-5 below.)
Arguments
<deal_id>(required) — the deal to mint predictions for and/or record actuals against.
Execution
The instructions below are for the model executing this command.
Decide the path first — and run only one. This command has two mutually exclusive paths: A. mint predictions (IC / underwriting time) and B. record actuals (post-close). Work out which moment the user is in and run only that path. Never run A then B in the same pass.
Why (look-ahead guardrail — this is load-bearing): a prediction is only meaningful if it was made before the outcome was known. If you mint a forecast at post-close — from a snapshot that already reflects how the deal turned out — and then score it against the known actuals, you fabricate an artificially-accurate "IC-time" prediction and poison the calibration / similar-deal corpus that
/calibrate-vs-historydepends on. So: only mint (A) when the outcome is genuinely unknown. In the post-close path (B) you do not mint — if no IC-time predictions exist, that deal simply doesn't get a graded score, and that is the correct, honest result (see step 5). Note thatrecord_deal_actualsmay return a backend hint like "run create_forecast_run first" when 0 predictions close — do not follow that hint post-close; it is only valid at IC time.
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.
- 5d ago First seen · 86 lines · 65 tokens per session scan A 59635c90bbad
record-outcome is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 2,074 once invoked, about $0.0003 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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