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 --skill neqsim-stid-retrievergit clone --depth 1 https://github.com/equinor/neqsimWrote 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/neqsim-stid-retriever)<a href="https://agentmods.dev/skills/equinor/neqsim/neqsim-stid-retriever"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-stid-retriever/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/neqsim-stid-retriever"><img src="https://agentmods.dev/badge/skills/equinor/neqsim/neqsim-stid-retriever.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00096 | $0.06857 |
| Opus 5.5 | $0.00038 | $0.02743 |
| Sonnet 5.5 | $0.00019 | $0.01371 |
| Haiku 4.5 | $0.00010 | $0.00686 |
Grade A, and why
neqsim-stid-retriever 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 today.
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 — 677 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Retrieval Skill for Engineering Tasks
Retrieve engineering documents (compressor curves, mechanical drawings, data sheets, vendor reports) for use in NeqSim task-solving workflows.
For water-hammer/liquid-hammer tasks, retrieve STID/P&ID route drawings, line lists, stress isometrics, piping specifications, valve data sheets with closure time, pump curves or trip logs, design-pressure basis, and relevant tagreader event exports into the task references folder.
⚠️ CRITICAL: All documents go INSIDE the task folder
ALL downloaded documents — STID drawings, PI historian exports,
vendor datasheets, P&IDs, literature PDFs — MUST be saved to
step1_scope_and_research/references/ within the task folder.
NEVER download or save task-related files to workspace-level directories like
output/, figures/, or any path outside task_solve/YYYY-MM-DD_slug/.
# CORRECT — saves inside the task folder:
TASK_DIR = "task_solve/YYYY-MM-DD_slug"
out_dir = os.path.join(TASK_DIR, "step1_scope_and_research", "references")
# WRONG — saves outside the task folder:
out_dir = os.path.join(os.path.dirname(__file__), "..", "figures", "stid_docs") # NEVER
out_dir = "output/stid_docs" # NEVER
For PDF-to-PNG conversion: Output to the task's figures/:
uv run --project devtools --locked devtools/pdf_to_figures.py task_solve/YYYY-MM-DD_slug/step1_scope_and_research/references/ \
--outdir task_solve/YYYY-MM-DD_slug/figures/
This rule ensures every task is self-contained and portable.
This skill is backend-agnostic — it works with any document source:
- Documents placed manually in the task folder
- A pre-existing local directory of downloaded files
- An auto-retrieval backend (configured separately, not part of the public repo)
Document Sources (Priority Order)
The task solver checks these sources in order:
- Local directory — user provides a path to pre-downloaded docs
- Task references folder — docs already in
step1_scope_and_research/references/ - Retrieval backend — auto-fetch from a document management system (if configured
in
devtools/doc_retrieval_config.yaml— this file is gitignored)
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.
- today Changed e022cef02705
- 6d ago Changed · +6 lines f60dc2b34e26
- 20d ago Changed · +38 lines · -26 tokens per session 2e62be4cd3c3
- 25d ago Changed · +3 lines a6c8b6164790
- 1mo ago First seen · 630 lines · 122 tokens per session scan A a6fb72e57a71
neqsim-stid-retriever is a skill published in the GitHub repository equinor/neqsim (156 stars, last pushed today), licensed Apache-2.0. It adds 96 tokens to every session and 6,857 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 2026-09-03.
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