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-concept-longlist-shortlist-screeninggit 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-concept-longlist-shortlist-screening)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/neqsim-concept-longlist-shortlist-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-concept-longlist-shortlist-screening/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-concept-longlist-shortlist-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/neqsim-concept-longlist-shortlist-screening.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 | $0.00098 | $0.02338 |
| Opus 5.5 | $0.00039 | $0.00935 |
| Sonnet 5.5 | $0.00020 | $0.00468 |
| Haiku 4.5 | $0.00010 | $0.00234 |
Grade A, and why
neqsim-concept-longlist-shortlist-screening 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 11d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Concept Long-List to Short-List Screening
Early field-development decisions fail most often not in the detailed comparison but before it: the "alternatives" are three sizes of one concept, an option nobody wrote down was never evaluated, or a concept was dropped on an opinion. This skill makes the funnel from a wide long list to a defensible short list explicit, reproducible and auditable. It produces screening results for human review; it does not select a concept.
When to Use
- Building the concept long list for an opportunity (DG0/DG1-type framing).
- Reducing a long list to a short list of two to four concepts that a detailed
comparison (
concept-selection-agent) and economics can carry forward. - Checking that a proposed set of cases is a real choice and not capacity variants of one topology.
- Showing why each concept was eliminated, and what evidence would revive it.
Inputs
| Key | Meaning |
|---|---|
elements |
Decision element to options (default DEFAULT_DECISION_ELEMENTS: host, wells, oil export, gas disposal, drive, power) |
rules |
Compatibility rules {"if": {...}, "then_not": {...}, "reason": ...} (default DEFAULT_RULES) |
givens |
Decisions already taken (policy decisions); fixed, not varied |
concepts |
Alternatively a hand-made list {"id", "choices", "sizing"} instead of generation |
must_pass |
Non-compensatory criteria {"id", "name"} |
assessments |
{concept: {criterion: {"result": pass/fail/unknown, "evidence": "..."}}} |
criteria |
Weighted ranking criteria {"id", "weight", "direction": benefit/cost} |
scores |
Raw score per concept and criterion (from screening skills, see below) |
confidence |
Optional {concept: {criterion: high/medium/low}} |
reference |
Concept id(s) carried as reference / benchmark |
short_list_size, diversity_element, robustness |
Selection settings (n_samples, concentration, seed, acceptability_threshold) |
Outputs
run_screening(study) returns a JSON-serialisable dictionary with
readiness (always SCREENING), long_list, excluded_by_rules (with
reasons), capacity_variants, n_distinct_topologies, coverage,
must_pass_log, decision_matrix (normalised, 1 = best), ranking,
pareto_front, robustness (p_first, p_top_k), crossovers,
gap_to_cutoff, short_list (each with a reason), conditional_short_list,
elimination_log, open_questions, warnings and
human_review_required: true.
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.
- examples/basic_screening.py 2.1 KB runs code
- pyproject.toml 675 B
- README.md 1.2 KB
- src/concept_longlist_shortlist/__init__.py 1.5 KB runs code
- src/concept_longlist_shortlist/figures.py 19 KB runs code
- src/concept_longlist_shortlist/model.py 32 KB runs code
- tests/test_concept_longlist_shortlist.py 8.3 KB runs code
- tests/test_figures.py 1.9 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.
- 11d ago First seen · 182 lines · 98 tokens per session scan A 2ee9e516e480
neqsim-concept-longlist-shortlist-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (4 stars, last pushed today), licensed Apache-2.0. It adds 98 tokens to every session and 2,338 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 2026-10-26.
neqsim-concept-longlist-shortlist-screening compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| neqsim-concept-longlist-shortlist-screeningequinor/neqsim-community-skills | Skill | 4 | today | Apache-2.0 | A | 98 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 3d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |