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 skills add gyf9712/stat-theory-skills --skill theory-simulationgit clone --depth 1 https://github.com/gyf9712/stat-theory-skillsWrote 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/gyf9712/stat-theory-skills/theory-simulation)<a href="https://agentmods.dev/skills/gyf9712/stat-theory-skills/theory-simulation"><img src="https://agentmods.dev/badge/skills/gyf9712/stat-theory-skills/theory-simulation/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/skills/gyf9712/stat-theory-skills/theory-simulation"><img src="https://agentmods.dev/badge/skills/gyf9712/stat-theory-skills/theory-simulation.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 | $0.00214 | $0.12046 |
| Opus 5.5 | $0.00086 | $0.04818 |
| Sonnet 5.5 | $0.00043 | $0.02409 |
| Haiku 4.5 | $0.00021 | $0.01205 |
Grade A, and why
theory-simulation 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 1mo 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 — 1,015 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Theory-Simulation — Bridge Theory and Monte Carlo for Statistics Papers
🔬 Model Recommendation: Run this skill on Claude Opus for best results. Designing rate-verifying experiments and reconciling theory with empirical results requires deep reasoning. If your session is not on Opus, run
/model opus.
Bridges theoretical results and simulation experiments, two-way:
Theory ←————— stress tests, rate slopes ——————→ Simulation
←————— sharper bounds, weakened —————→
assumptions discovered from sim
Built to top statistics journal standards: clearly stated DGPs, multiple sample sizes, Monte Carlo replications, rate verification via log-log slopes, stress tests on every assumption, finite-sample vs asymptotic comparisons, and publication-grade figures conforming to AoS / JASA / Biometrika / JRSS-B style.
Context: $ARGUMENTS
Pipeline Position
/proofcheck → /proof-repair → /theory-sharpen → /theory-simulation → /proof-writer
Correct? Fix issues Strengthen theory Verify + stress Write proofs
(this skill)
This skill can also run standalone if user has theorems and wants Monte Carlo verification without a full pipeline.
Core Philosophy
A theoretical result is taken seriously by reviewers when simulation:
- Confirms the predicted rate/coverage/bias under stated assumptions
- Breaks in the predicted way when assumptions are violated
- Quantifies the finite-sample regime where asymptotics kick in
- Reveals improvements (sharper rates, weaker assumptions) for theory iteration
A simulation is taken seriously by reviewers when it has:
- Reproducible DGPs with hierarchical RNG streams (not just a single seed)
- Multiple cells along the asymptotic path the theory uses
(e.g.,
s log d / nfixed, NOT just "multiple n and d") - MCSE-driven replication count for each metric (NOT a fixed B threshold)
- Honest stress tests, including least-favorable DGPs matched to the theorem
- Inference diagnostics beyond rate: size, local power, interval length, EmpSE vs ModSE calibration
- Publication-grade figures with MC uncertainty shown
- Paired-replicate baseline comparison (all methods on the same synthetic data)
- Failure handling: nonconvergence, singular Hessian, optimizer stalls all logged and reported per cell
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.
- 1mo ago First seen · 1,015 lines · 214 tokens per session scan A 398eede7fbb1
theory-simulation is a skill published in the GitHub repository gyf9712/stat-theory-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 214 tokens to every session and 12,046 once invoked, about $0.0009 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.
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