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/pre-screen)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/pre-screen"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/pre-screen/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/pre-screen"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/pre-screen.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 | $0.00060 | $0.05753 |
| Opus 5.5 | $0.00024 | $0.02301 |
| Sonnet 5.5 | $0.00012 | $0.01151 |
| Haiku 4.5 | $0.00006 | $0.00575 |
Grade A, and why
pre-screen 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 21d 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 — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Announcement Screen — public or private target
Run an end-to-end stage-1 screen of a target company. This command auto-detects whether the target is public or private and routes accordingly:
- Public target (resolver returns
classification == "public"): constrain evidence to the latest canonical annual filing (10-K, 20-F, or 40-F) plus substantive exhibits and the backend-reported fundamentals result (SEC when enabled), with its source, coverage status, and gaps carried into the brief. Web search is off so the artifact reflects only what was knowable from pre-cutoff filings. - Private target (resolver returns
classification == "private"): use the PrivateCompanySnapshot (PCS) seeded from the four primary-source signal adapters (SEC N-PORT marks, counter-party 10-K mentions, USAspending federal contract awards, and the S-1 watcher if the target has filed). Deep-research web search is on — there is no 10-K to anchor against, so press / news / Sacra-style commentary IS the public-trace evidence layer for a private target. Honor theas-ofcutoff if supplied. This branch is signal-only and must not produce an OloLand valuation.
The audit log at the end is what separates this from "an LLM wrote a memo." Always surface it.
Usage
/pre-screen <ticker or company name> [as-of YYYY-MM-DD]
Arguments
<ticker or company name>(required) — Free text. Examples:GBTG,Amex GBT,Snowflake,SpaceX,Anthropic.as-of <YYYY-MM-DD>(optional) — Cutoff date. The screen ignores any filing, news, or evidence dated after this. Defaults to today. Use for retrospective screens against a target whose deal or IPO has already been announced.
Bounded public-branch valuation authority
The user's explicit invocation of, or unambiguous request to run, /pre-screen authorizes exactly one public-branch run_monte_carlo_simulation(deal_id, n_simulations=10000, seed=42) call for a target resolved as public. This authority is limited to the resolved deal and this command execution. Automatic routing, a background recommendation to pre-screen, or reuse of an earlier invocation does not authorize a call. Do not retry a failed call; report the gap unless the user explicitly starts a new run.
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.
- 21d ago Changed · +10 lines · +9 tokens per session 0ef5a065a392
- 1mo ago First seen · 335 lines · 51 tokens per session scan A e09cfbc8e876
pre-screen is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 60 tokens to every session and 5,753 once invoked, about $0.0002 per session on Opus 5.5. 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
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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).
cost-per-task
Compute cost-per-successful-task unit economics from usage and cost data, including retries, failures, and quality thresholds.
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.