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neqsim-technology-assessment-report

neqsim-technology-assessment-report is a skill for Claude Code, Codex from equinor/neqsim-community-skills. It costs 99 tokens per session (1,539 once invoked), scanned A, original, Apache-2.0.

A report-writing skill that turns structured technology assessment and failure-risk results into a reviewable Markdown or Word document. It also combines project details, references, and author-written sections.

In plain words
What is it for?
It helps document a new or less familiar technology, including its maturity, benefits, risks, qualification needs, conclusions, and open points.
Why use it?
It removes the manual work of assembling assessment findings into a consistent report. It keeps unresolved questions visible instead of hiding them.

Skill for Claude CodeCodex

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

not rated 4repo today A scan 99 tokens original Apache-2.0

Good fit It helps document a new or less familiar technology, including its maturity, benefits, risks, qualification needs, conclusions, and open points.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/equinor/neqsim-community-skills/neqsim-technology-assessment-report
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-technology-assessment-report
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-technology-assessment-report

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-technology-assessment-report"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-technology-assessment-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 99 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,539 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 $0.00099 $0.01539
Opus 5.5 $0.00040 $0.00616
Sonnet 5.5 $0.00020 $0.00308
Haiku 4.5 $0.00010 $0.00154

Measured 10d ago against content hash a66c94d17eea, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

neqsim-technology-assessment-report 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 10d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (examples/build_example_report.py, src/technology_assessment_report/__init__.py, src/technology_assessment_report/model.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/reporting/neqsim-technology-assessment-report/SKILL.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Technology Assessment Report

Use this skill to write the technology assessment report that a project study delivers for a technology it may use. The report is generated from the structured results of neqsim-technology-assessment and neqsim-technology-fmirr, plus author-written narrative. It checks the inputs and lists every open point instead of hiding it.

When to Use

  • A concept or project study needs a technology assessment report for a new or step-out technology.
  • An assessment and FMIRR exist as data and must become a reviewable Word or Markdown document.
  • A report is updated after new evidence changes ratings (re-run, compare the issues list, issue a new revision).

Inputs

  • assessment: a technology_assessment.v1 mapping (required).
  • fmirr: a technology_fmirr.v1 mapping (recommended whenever any element needs qualification).
  • ReportMeta: title, subtitle, document number, revision, author, reviewer, date, classification, status, project.
  • narrative: author-written paragraphs per chapter id, appended to the generated text.
  • introduction_context: why the project considers this technology.
  • references: project documents, test reports and standards to add.

Outputs

  • A Report with chapters, tables and an issues list.
  • to_markdown() / save_markdown(path): Markdown with numbered tables.
  • save_docx(path): Word document with title block, contents field, header and footer (classification, status, page numbers), landscape FMIRR chapter and risk-coloured cells (needs python-docx).
  • to_dict(): JSON view for downstream tools.

Engineering Method

The report has fixed chapters so that reviewers always find the same content:

  1. Executive summary (generated from results).
  2. Introduction (purpose, scope, method, context).
  3. Technology qualification basis (description, use, environment, functions, performance expectations, regulatory requirements). Keep this chapter stable; it is reused in the qualification programme and qualification report.
  4. Technology description and decomposition.
  5. Technology maturity: novelty category and TRL per element and for the system.
  6. Enabling or enhancing technology.
  7. Step-out from existing experience (design, manufacturing, installation, operability, fluids, operating conditions, lifetime).
  8. Benefits and risks.
  9. Standardisation and multi-implementation.
  10. FMIRR, first version (landscape).
  11. Preliminary outline of qualification activities, linked to critical modes.
  12. Conclusions and recommendations.
  13. Assumptions, data gaps and open points.
  14. References.
  15. Appendix A: definitions (novelty table, TRL scale, consequence and probability classes).

Read the full file on GitHub · 161 lines

Files

What ships with it

6 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. 10d ago First seen · 161 lines · 99 tokens per session scan A a66c94d17eea

Subscribe to this mod's changes

neqsim-technology-assessment-report is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 99 tokens to every session and 1,539 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 2026-10-28.

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