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.
git 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/calibrate-vs-history)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/calibrate-vs-history"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/calibrate-vs-history/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/commands/ololand-ai/ololand-plugins/calibrate-vs-history"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/calibrate-vs-history.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 | $0.00047 | $0.00845 |
| Opus 5 | $0.00023 | $0.00423 |
| Sonnet 5 | $0.00009 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
calibrate-vs-history 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 10d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Calibrate vs. History
Uses cross-deal outcome data to adjust the current deal's headline projections by the historical bias your firm has shown in similar deals. The output is a calibrated projection: management's $X.XM EBITDA, with the historical-bias-corrected $Y.YM EBITDA shown alongside.
Usage
/calibrate-vs-history <deal_id>
Arguments
deal_id(required) — The current deal to calibrate. Requires similar deals to have outcome data on file (i.e. closed and observed for at least 12 months post-close).
Execution
- Call
find_similar_dealsfrom the MCP server with thedeal_id. - If the response is
status: "no_usable_corpus"— stop here. Tell the user the firm does not yet have a usable cohort of closed-and-observed similar deals to calibrate against. Do NOT calibrate against a forced cohort. - Filter to similar deals with outcome data: deals that have entries in the outcome-tracking system showing realized vs. underwritten metrics.
- For each metric where outcome data exists (revenue growth, EBITDA margin, leverage trajectory, risk realization rates), compute:
- Bias — mean of (realized − underwritten) across similar deals
- Variance — standard deviation of the bias
- Confidence — sample size and recency
For the firm's authoritative measured bias — computed by the backend over every scored prediction, not derived from this cohort — call
get_firm_calibration(/firm-calibration). Use it to sanity-check the cohort bias computed here; a large divergence usually means the cohort is too small to trust. - Apply the bias to the current deal's projection. Return both the management projection and the calibrated projection side-by-side, with the bias explanation.
Output
| Metric | Management | Historical bias | Calibrated | Confidence |
|---|---|---|---|---|
| FY27 revenue growth | 22% | -7pp avg (n=6, σ=4pp) | 15% | medium |
| FY27 EBITDA margin | 24% | -2pp avg (n=6) | 22% | medium |
| Customer concentration risk realizing | 8% | flagged 5/6, realized 3/6 | 50% | high |
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.
- 10d ago First seen · 58 lines · 47 tokens per session scan A 9a5f6c60045e
calibrate-vs-history is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 47 tokens to every session and 845 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 2026-08-31.
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