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 skills/lemur47/logic/montecarlonpx skills add lemur47/logic --skill montecarlogit clone --depth 1 https://github.com/lemur47/logicWrote 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/lemur47/logic/montecarlo)<a href="https://agentmods.dev/skills/lemur47/logic/montecarlo"><img src="https://agentmods.dev/badge/skills/lemur47/logic/montecarlo.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 | $0.00000 | $0.02375 |
| Opus 5 | $0.00000 | $0.01188 |
| Sonnet 5 | $0.00000 | $0.00475 |
| Haiku 4.5 | $0.00000 | $0.00237 |
Grade A, and why
montecarlo 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monte Carlo: Schedule Simulation with Probability Distributions
Source: github.com/lemur47/logic
Purpose
Help users answer one question: "What's the probability we finish by this date?" using Monte Carlo schedule simulation. This skill replaces single-point PERT estimates with full probability distributions over project duration.
Monte Carlo builds on PERT. PERT asks "how long will it take?" and gives a single expected value. Monte Carlo takes the same three-point estimates, runs thousands of simulated schedules, and returns percentiles, critical path frequencies, and target-date probabilities. The expected value becomes a distribution.
When to Use
- Sprint or project commitment decisions ("should we commit to 40 days?")
- Comparing PERT expected value against realistic percentile-based commitments
- Identifying which tasks drive schedule risk via critical path frequency
- Target-date probability queries ("what's the chance we ship by April 15?")
- Any situation where a stakeholder needs a confidence level, not a point estimate
When NOT to Use
- Single-task estimation with no dependencies — use the PERT Skill directly
- You need reality adjustments via insight tags — use PERT first, then feed the adjusted estimates into Monte Carlo
- You need Dirichlet drift or time-evolving simulation — out of scope (Sprint 7)
Core Concepts
Beta-PERT Distribution
Each task's duration is sampled from a beta-PERT distribution, parameterised by the same three-point estimates used by textbook PERT:
- O — Optimistic (best case)
- M — Most likely (realistic)
- P — Pessimistic (worst case)
The beta-PERT distribution is a beta distribution rescaled to [O, P] with the mode at M. Unlike PERT's Gaussian approximation, it preserves the skew in asymmetric estimates.
Simulation Process
For each of N simulations (default 10,000):
- Sample a random duration for every task from its beta-PERT distribution
- Compute task start and finish times respecting dependency constraints (forward pass)
- Record the total project duration (max finish time)
- Identify which tasks are on the critical path (zero total float)
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 · 256 lines · 0 tokens per session scan A 44136c73e2da
montecarlo is a skill published in the GitHub repository lemur47/logic (2 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,375 tokens. 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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