agentmods.dev

neqsim-continuous-improvement-toolkit

neqsim-continuous-improvement-toolkit is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 102 tokens per session (1,698 once invoked), scanned A, original, Apache-2.0.

A set of plant-independent helpers for improving a running NeqSim task over repeated cycles. It reads PI or IP.21 historian data, chooses promising bounded trials, updates drifting model parameters, and checks whether a calibration can be trusted from the available data.

In plain words
What is it for?
Use it to feed historian tags into a task, tune setpoints with Gaussian-process Bayesian optimisation, update parameters such as fouling or meter bias with an ensemble Kalman filter, and test calibration identifiability.
Why use it?
It removes the need to build separate helpers for historian access, trial selection, model updating, and parameter identifiability. The identifiability check also warns when the data cannot reliably distinguish the parameters being fitted.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 4repo today A scan 102 tokens original Apache-2.0

Good fit Use it to feed historian tags into a task, tune setpoints with Gaussian-process Bayesian optimisation, update parameters such as fouling or meter bias with an ensemble Kalman filter, and test calibration identifiability.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit
View source ↗ equinor/neqsim-community-skills equinor.github.io
Install

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.

Any agent
npx skills add equinor/neqsim-community-skills --skill neqsim-continuous-improvement-toolkit
Clone the repo
git clone --depth 1 https://github.com/equinor/neqsim-community-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for neqsim-continuous-improvement-toolkit

README.md
[![agentmods](https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit/github.svg)](https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit)
Your own site
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit/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.

agentmods 80×15 button for neqsim-continuous-improvement-toolkit

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-continuous-improvement-toolkit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,698 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00102 $0.01698
Opus 5.5 $0.00041 $0.00679
Sonnet 5.5 $0.00020 $0.00340
Haiku 4.5 $0.00010 $0.00170

Measured 1mo ago against content hash 7464e0fd4d6e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-continuous-improvement-toolkit 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.

The scan reads SKILL.md. This mod also ships 6 executable files (src/continuous_improvement_toolkit/__init__.py, src/continuous_improvement_toolkit/bayes_opt.py, src/continuous_improvement_toolkit/enkf.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/process/neqsim-continuous-improvement-toolkit/SKILL.md · 156 lines

How it starts

The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Continuous Improvement Toolkit

Public, plant-agnostic helpers for the NeqSim living-task runner (neqsim task-living, task-cycle, task-solve, task-backtest; see the core skill neqsim-continuous-task-improvement). Everything here runs without enterprise access.

When to Use

  • A living task must read a PI or IP.21 historian: use TagreaderAdapter.
  • A solve stage tunes a few bounded setpoints and each trial is a NeqSim run: use propose_next (GP + expected improvement). Pass its expected_improvement as the candidate's predicted_delta so the stop rule can declare converged when no trial is worth its cost.
  • Model parameters drift (fouling factor, efficiency, meter bias): update them every cycle with enkf_update.
  • Before trusting a calibration: identifiability(J) — a rank below the number of parameters means some combination is set by noise, not data.

Inputs

  • TagreaderAdapter: tagreader source, imstype, step_seconds and a tags map (logical name -> historian tag), plus the runner's watermark window.
  • propose_next: previous trials X (list of setpoint vectors), results y, bounds per variable, maximize, and an optional seed.
  • enkf_update: parameter ensemble (members x parameters), matching model predictions (members x observations), the measured vector and obs_std.
  • identifiability: a sensitivity Jacobian (observations x parameters), optional parameter names and parameter_scale.

Outputs

  • TagreaderAdapter.fetch(...) returns a SourceResult (ok, partial, stale, failed, not_installed) and writes a CSV part file to the task's continuous/data/ folder.
  • propose_next returns {"x", "expected_improvement", "predicted", "predicted_std", "best_so_far"}.
  • enkf_update returns the updated ensemble array.
  • identifiability returns singular_values, condition_number, rank, n_parameters, an identifiable flag, unidentifiable_directions and a per-parameter sensitivity_norm.

Read the full file on GitHub · 156 lines

Files

What ships with it

8 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.

Changes

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.

  1. 1mo ago First seen · 156 lines · 102 tokens per session scan A 7464e0fd4d6e

Subscribe to this mod's changes

neqsim-continuous-improvement-toolkit is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 102 tokens to every session and 1,698 once invoked, about $0.0004 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 2026-09-27.

Analytics and metricsScientific
Related

neqsim-continuous-improvement-toolkit compared with skills from other repositories

neqsim-continuous-improvement-toolkit next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
neqsim-continuous-improvement-toolkitequinor/neqsim-community-skillsSkill4todayApache-2.0A102Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-pluginsSkill35k3d agoApache-2.0A123Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA83Claude Code
matlabK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA44Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA71Claude Code, Codex
research-engineerdavila7/claude-code-templatesSkill33kyesterdayMITA43Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmedSkill5.8kyesterdayApache-2.0A205Claude Code