Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill fluidsimgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/fluidsim)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/fluidsim"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/fluidsim/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/k-dense-ai/scientific-agent-skills/fluidsim"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/fluidsim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00054 | $0.03255 |
| Opus 5 | $0.00027 | $0.01628 |
| Sonnet 5 | $0.00011 | $0.00651 |
| Haiku 4.5 | $0.00005 | $0.00326 |
Grade A, and why
fluidsim 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 7d 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FluidSim
Use FluidSim 0.9.0 as a framework for Python-defined numerical solvers, especially periodic Cartesian pseudospectral CFD. Upstream FluidSim is CeCILL-2.1; the MIT frontmatter license applies only to this skill.
This skill does not treat a completed run, a stable time step, a smooth plot, or a closed program exit as evidence of numerical convergence or physical validity.
Required workflow
- State equations, units or nondimensionalization, geometry, boundaries, initial conditions, forcing, observables, and acceptance criteria.
- Select a verified solver and inspect its generated default parameters.
- Create a strict JSON plan with explicit CPU, RAM, disk, wall-time, output-file, timestep, CFL, resolution, and dealiasing bounds.
- Run the bundled validator and resource estimator.
- Generate and review a dry-run script. It does nothing unless executed with an explicit config-ID acknowledgement.
- Run one tiny serial pilot. Inspect budgets, divergence/constraints, spectral tails, CFL/time-step history, and output growth.
- Refine grid and time step independently. Check conservation/budget residuals and observable sensitivity.
- Only then prepare a site-specific MPI job. Never submit or launch MPI automatically.
- Preserve config, script,
uv.lock, package/platform/backend versions, logs, output inventory, checksums, and restart lineage.
Stop if physical assumptions, units, boundary conditions, forcing semantics, resolution criteria, resource limits, or acceptance criteria are missing.
Version and installation
As verified on 2026-07-23:
- Latest stable PyPI release:
fluidsim==0.9.0(2025-12-04). - Package metadata requires Python
>=3.11and lists Python 3.11–3.14. - Pseudospectral parameter creation needs FluidFFT; bare
fluidsimimported in the smoke test, butns2d.create_default_params()failed until thefftextra was installed. - Current companion versions tested here:
fluidfft==0.4.5andpyFFTW==0.15.1.
What ships with it
15 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.
- references/advanced_features.md 11 KB
- references/installation.md 9.5 KB
- references/output_analysis.md 9.9 KB
- references/parameters.md 10.0 KB
- references/simulation_workflow.md 11 KB
- references/solvers.md 7.8 KB
- scripts/__init__.py 74 B runs code
- scripts/_common.py 15 KB runs code
- scripts/_schema.py 28 KB runs code
- scripts/budget_summary.py 13 KB runs code
- scripts/grid_resource_estimator.py 8.8 KB runs code
- scripts/output_inventory.py 11 KB runs code
- scripts/restart_compatibility.py 15 KB runs code
- scripts/simulation_dry_run.py 7.6 KB runs code
- scripts/solver_config_validator.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.
- 7d ago Changed · +17 lines d616bf694c9b
- 11d ago First seen · 280 lines · 54 tokens per session scan A 8e6c91881922
fluidsim is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 3,255 once invoked, about $0.0003 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.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.