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 equinor/neqsim-community-skills --skill neqsim-uncertainty-quantificationgit clone --depth 1 https://github.com/equinor/neqsim-community-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/equinor/neqsim-community-skills/neqsim-uncertainty-quantification)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-uncertainty-quantification"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-uncertainty-quantification/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/equinor/neqsim-community-skills/neqsim-uncertainty-quantification"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-uncertainty-quantification.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 | $0.00119 | $0.03635 |
| Opus 5.5 | $0.00048 | $0.01454 |
| Sonnet 5.5 | $0.00024 | $0.00727 |
| Haiku 4.5 | $0.00012 | $0.00364 |
Grade A, and why
neqsim-uncertainty-quantification 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Uncertainty Quantification
A Standard or Comprehensive NeqSim task must report P10/P50/P90 and a tornado diagram. In practice that gets written from scratch in every notebook, and the same four defects recur: sampling that clusters because it is plain pseudo-random at n = 200, a Monte Carlo loop that re-solves the flowsheet for a gas price that never touches it, no check that the run converged, and a tornado presented as if it were a sensitivity analysis when it cannot see interaction.
This skill supplies the sampling, the statistics, the caching, the convergence gate and the report block. It does not own the model — the task supplies that.
When to Use
- A task must produce
uncertaintyforresults.json: P10/P50/P90, mean, standard deviation, probability of a negative outcome, tornado. - A Monte Carlo loop wraps an expensive NeqSim flowsheet and the evaluation budget is the binding constraint.
- Parameters must be screened before a sensitivity budget is committed.
- An interaction between uncertain inputs is suspected and a tornado is not enough.
- An existing uncertainty block must be audited: enough samples, converged, correct percentile convention?
When Not to Use
- As an optimiser. Searching for the best setpoints is
neqsim-optimization-and-doe; this skill propagates uncertainty through a fixed design. - To invent input ranges. A distribution with no basis produces a precise answer to an arbitrary question — record where each range came from.
- For correlated inputs. Every marginal is sampled independently; correlation between, say, price and cost inflation is not represented.
- As a substitute for a risk register. A probability distribution on an output is not a hazard assessment.
Inputs
| Input | Meaning |
|---|---|
parameters |
list of Distribution objects, each with name, unit, kind |
kind |
"technical" (drives the expensive stage) or "economic" (cheap stage only) |
model |
f(values) -> float, for a single-stage study |
technical / economic |
the two-stage split: g(technical) -> intermediate, h(intermediate, economic) -> float |
sampling_method |
"lhs" (default), "random", or "halton" |
seed |
integer for reproducibility |
n |
sample count; at least 200 for a simulation-backed run |
What ships with it
19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/global_sensitivity_with_salib.py 2.3 KB runs code
- examples/monte_carlo_npv_study.py 2.1 KB runs code
- pyproject.toml 691 B
- README.md 1.8 KB
- src/uncertainty_quantification/__init__.py 2.3 KB runs code
- src/uncertainty_quantification/backends.py 8.2 KB runs code
- src/uncertainty_quantification/distributions.py 8.3 KB runs code
- src/uncertainty_quantification/models.py 5.2 KB runs code
- src/uncertainty_quantification/report.py 6.0 KB runs code
- src/uncertainty_quantification/sampling.py 3.8 KB runs code
- src/uncertainty_quantification/study.py 7.4 KB runs code
- src/uncertainty_quantification/summary_stats.py 5.0 KB runs code
- tests/test_distributions.py 4.5 KB runs code
- tests/test_staged_models.py 2.7 KB runs code
- tests/test_summary_stats.py 3.4 KB runs code
- tests/test_uncertainty_report.py 4.5 KB runs code
- tests/test_uncertainty_study.py 6.2 KB runs code
- tests/test_uq_backends.py 4.1 KB runs code
- tests/test_uq_sampling.py 2.5 KB runs code
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 Changed · -99 tokens per session fa012bc93d30
- 1mo ago First seen · 297 lines · 218 tokens per session scan A e35c218bd8aa
neqsim-uncertainty-quantification is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 119 tokens to every session and 3,635 once invoked, about $0.0005 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 2026-09-19.
neqsim-uncertainty-quantification compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| neqsim-uncertainty-quantificationequinor/neqsim-community-skills | Skill | 4 | today | Apache-2.0 | A | 119 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |