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 gas-turbine-performance-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/gas-turbine-performance-screening)<a href="https://agentmods.dev/skills/equinor/neqsim-community-skills/gas-turbine-performance-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/gas-turbine-performance-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/gas-turbine-performance-screening"><img src="https://agentmods.dev/badge/skills/equinor/neqsim-community-skills/gas-turbine-performance-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 | $0.00081 | $0.01453 |
| Opus 5 | $0.00041 | $0.00727 |
| Sonnet 5 | $0.00016 | $0.00291 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade A, and why
neqsim-gas-turbine-performance-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 12d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gas Turbine Performance Screening
Use this skill for public, educational gas-turbine performance screening. It corrects an ISO base rating for ambient temperature, site elevation, and inlet/exhaust pressure losses to estimate site-rated shaft power, site heat rate, thermal efficiency, fuel heat input, exhaust mass flow, and exhaust temperature so an agent can scope a driver-selection study before detailed package review.
When to Use
- When a user asks how much power a gas-turbine driver makes at site conditions versus its ISO rating.
- When an agent needs a quick site heat rate, fuel heat input, or exhaust mass flow estimate for waste-heat or HRSG scoping.
- When examples must run without confidential vendor performance maps or company package specs.
Inputs
iso_base_power_kw: ISO base-load shaft power rating in kW.iso_heat_rate_kj_kwh: ISO base-load heat rate in kJ/kWh.ambient_temperature_k: site ambient temperature in kelvin, default 288.15 (ISO reference).site_elevation_m: site elevation above sea level in m, default 0.relative_humidity: site relative humidity fraction, default 0.6 (small effect).inlet_pressure_loss_mbar: inlet system pressure loss in mbar, default 10.exhaust_pressure_loss_mbar: exhaust system pressure loss in mbar, default 10.required_shaft_power_kw: optional driven-equipment shaft power in kW for the margin check.
Outputs
site_power_kw: derated site shaft power.ambient_derate_factor: ambient-temperature power factor.altitude_derate_factor: site-elevation power factor.pressure_loss_derate_factor: combined inlet/exhaust pressure-loss power factor.total_derate_factor: product of all derate factors.site_heat_rate_kj_kwh: site heat rate after efficiency degradation.thermal_efficiency: shaft thermal efficiency from the site heat rate.fuel_heat_input_kw: fuel lower-heating-value heat input.exhaust_mass_flow_kg_s: screening exhaust mass flow.exhaust_temperature_k: screening exhaust temperature.power_margin_ratio: site power divided by required shaft power, ornull.power_warning:ok,watch,insufficient-power, orno-rating.assumptions: public assumptions used by the placeholder model.
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.
- examples/basic_gas_turbine_performance_screening.py 1.4 KB runs code
- pyproject.toml 564 B
- README.md 1.0 KB
- src/gas_turbine_performance_screening/__init__.py 148 B runs code
- src/gas_turbine_performance_screening/model.py 8.0 KB runs code
- tests/test_gas_turbine_performance_screening.py 2.1 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.
- 12d ago First seen · 121 lines · 81 tokens per session scan A 4fcdc4a7325c
neqsim-gas-turbine-performance-screening is a skill published in the GitHub repository equinor/neqsim-community-skills (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 81 tokens to every session and 1,453 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 2026-08-31.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…