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/firm-calibration)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/firm-calibration"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/firm-calibration.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 | $0.00036 | $0.01180 |
| Opus 5 | $0.00018 | $0.00590 |
| Sonnet 5 | $0.00007 | $0.00236 |
| Haiku 4.5 | $0.00004 | $0.00118 |
Grade A, and why
firm-calibration 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Firm Calibration
Answers "how accurate have we been?" — the firm's own track record on predictions that have since been scored against realized outcomes. This is the institutional-memory read behind any claim that a forecast from this firm is trustworthy.
Firm-wide by construction. There is no deal argument: the scope always comes from your authenticated workspace, and the tool cannot be pointed at another firm's data.
Usage
/firm-calibration [sector=<sector>]
Arguments
sector(optional) — restrict to one deal sector, e.g.sector=Healthcare. See the coverage caveat below: this filter does not reach every section.
Execution
- Call
mcp__ololand__get_firm_calibration, passingsectoronly if the user gave one. - Check
suppressedfirst. Ifsuppressed: truewithsuppression_reason: "ethical_wall_enforced", stop and report that calibration is withheld for this workspace because it carries an enforced ethical wall — the underlying sources aggregate company-wide and cannot be filtered to the deals you're cleared for. This is not "no data" and must never be reported as an un-calibrated firm. - Read
coverageandtotal_with_outcomesbefore quoting any accuracy figure.overall_accuracyis computed only over predictions with a realized actual recorded. A firm with a handful of closed deals will produce a confident-looking percentage resting on very little.- If
total_with_outcomesis small, say "insufficient calibration history" and report the sample size. Do not manufacture a verdict on the firm's reliability from a thin sample. - Always report the sample size next to the number, never the number alone.
- If
- Report the sections that carry data. Skip empty ones rather than printing empty tables.
- If the user asked how a specific deal's projections should shift given this history, that is
/calibrate-vs-history— this command gives the firm-level picture, not a per-deal adjustment.
Reading the payload honestly
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 · 73 lines · 36 tokens per session scan A 1c6608e9f991
firm-calibration is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 36 tokens to every session and 1,180 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.
Other commands, from other repositories
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
merit-reconcile
Preview or check the status of Stripe → Merit payout reconciliation (read-only).
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.
finops-status
Orientation — say where an opportunity or assignment sits in the five-step FinOps lifecycle and what unlocks next. Useful when a record has no active stage: an opportunity while its assignments do the work, an assignment whose plan has not been approved yet, or a rejected or archived assignment. Read-only; mutates…
audit
Scan for non-kernel money math. Rebuild critical flows as JournalEntrys. Replay and prove. Complements /ledger-verify.