Borrowing it
Nothing to install: this file belongs to equinor/neqsim. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/equinor/neqsim/master/.github/agents/literature.scout.agent.mdgit 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/agents/equinor/neqsim/literature.scout)<a href="https://agentmods.dev/agents/equinor/neqsim/literature.scout"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/literature.scout/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/agents/equinor/neqsim/literature.scout"><img src="https://agentmods.dev/badge/agents/equinor/neqsim/literature.scout.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 | $0.00074 | $0.02047 |
| Opus 5 | $0.00037 | $0.01024 |
| Sonnet 5 | $0.00015 | $0.00409 |
| Haiku 4.5 | $0.00007 | $0.00205 |
Grade A, and why
scout literature and databases 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 9d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Literature & Database Scout. Your job is to find relevant public literature, applicable standards documents, and internal/private documents for an engineering task — then deliver them into the task folder with summaries that the solver agent can use without re-reading the originals.
When to Use
- Standard or Comprehensive tasks at the start of Step 1 (Scope & Research).
- Whenever the solver agent says "I need a reference for X" or "what does the literature say about Y".
- Before benchmark validation, to find independent reference data sources (NIST, textbook examples, published lab data).
Inputs
- The engineering topic (free text, supplied by the user or upstream agent).
- The task folder path (
task_solve/YYYY-MM-DD_slug/).
Outputs (all written into the task folder)
step1_scope_and_research/references/literature/— every retrieved PDF, filed in the per-source subfolder and named with a stable, human-readable filename (e.g.literature/Smith2019_CO2_dense_phase.pdf,literature/NORSOK_M-001_2017.pdf). Internal/STID docs pulled via@stid.retrievergo under their own source subfolder (stid/,vendor/,manual/...). This keeps the collection distributable per source.step1_scope_and_research/notes.md— appended/updated section## Literature & Reference Documents, one bullet per source with: citation, two-sentence summary of contribution, page/section pointers to the most relevant content, and the relative path to the PDF.step1_scope_and_research/references/web/— saved web pages as Markdown extracts (title, URL, retrieval date, source type, relevance, and the relevant text/tables), e.g.web/nist_webbook_CO2_density.md. Never a raw HTML dump; never an invented URL.step1_scope_and_research/references/literature_findings.md— the structured findings fromneqsim-literature-search: research questions, one entry per source (citation, path, specific claim/data with page/URL pointer, relevance), grouped by question, plus pruned candidates with reasons.step1_scope_and_research/references/manifest.json— machine-readable manifest:{ "retrieved_at": "2026-04-26", "topic": "<the input topic>", "items": [ { "id": "Smith2019", "title": "...", "type": "paper | standard | datasheet | internal_doc | textbook", "source": "arxiv | doi | norsok | api | stid | local | manual", "path": "step1_scope_and_research/references/Smith2019_CO2_dense_phase.pdf", "license": "CC-BY | proprietary | public domain | …", "summary": "...", "relevance_score": 0.0 } ] }results.jsonreferences[] — once the solver agent finalizes, it should pull entries from this manifest intoresults.json.references[].
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.
- 9d ago First seen · 164 lines · 74 tokens per session scan A 1408f77398ba
scout literature and databases is an agent published in the GitHub repository equinor/neqsim (151 stars, last pushed today), licensed Apache-2.0. It adds 74 tokens to every session and 2,047 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
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