Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill fluidsimgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-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/foryourhealth111-pixel/vibe-skills/fluidsim)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/fluidsim"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-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/foryourhealth111-pixel/vibe-skills/fluidsim"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/fluidsim.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 | $0.00069 | $0.02593 |
| Opus 5.5 | $0.00028 | $0.01037 |
| Sonnet 5.5 | $0.00014 | $0.00519 |
| Haiku 4.5 | $0.00007 | $0.00259 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- fluidsim — 92% identical, 7 lines differ
- fluidsim — 88% identical, 14 lines differ
- fluidsim — 88% identical, 14 lines differ
- fluidsim — 88% identical, 14 lines differ
- fluidsim — 88% identical, 14 lines differ
- fluidsim — 88% identical, 13 lines differ
- fluidsim — 88% identical, 5 lines differ
- fluidsim — 86% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FluidSim
Routing Boundary
Use this skill only for FluidSim or explicit CFD workflows such as Navier-Stokes, turbulence, shallow-water, stratified-flow, or pseudospectral fluid simulations. Do not use it for CSS fluid layouts, responsive design, generic Python simulation, generic PDE solving, or non-FluidSim numerical physics work.
Overview
FluidSim is an object-oriented Python framework for high-performance computational fluid dynamics (CFD) simulations. It provides solvers for periodic-domain equations using pseudospectral methods with FFT, delivering performance comparable to Fortran/C++ while maintaining Python's ease of use.
Key strengths:
- Multiple solvers: 2D/3D Navier-Stokes, shallow water, stratified flows
- High performance: Pythran/Transonic compilation, MPI parallelization
- Complete workflow: Parameter configuration, simulation execution, output analysis
- Interactive analysis: Python-based post-processing and visualization
Core Capabilities
1. Installation and Setup
Install fluidsim using uv with appropriate feature flags:
# Basic installation
uv uv pip install fluidsim
# With FFT support (required for most solvers)
uv uv pip install "fluidsim[fft]"
# With MPI for parallel computing
uv uv pip install "fluidsim[fft,mpi]"
Set environment variables for output directories (optional):
export FLUIDSIM_PATH=/path/to/simulation/outputs
export FLUIDDYN_PATH_SCRATCH=/path/to/working/directory
No API keys or authentication required.
See references/installation.md for complete installation instructions and environment configuration.
2. Running Simulations
Standard workflow consists of five steps:
Step 1: Import solver
from fluidsim.solvers.ns2d.solver import Simul
Step 2: Create and configure parameters
params = Simul.create_default_params()
params.oper.nx = params.oper.ny = 256
params.oper.Lx = params.oper.Ly = 2 * 3.14159
params.nu_2 = 1e-3
params.time_stepping.t_end = 10.0
params.init_fields.type = "noise"
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.
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.
- 1mo ago First seen · 351 lines · 69 tokens per session scan A c75c6fcc861c
fluidsim is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,772 stars, last pushed yesterday), licensed Apache-2.0. It adds 69 tokens to every session and 2,593 once invoked, about $0.0003 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.
Other skills, from other repositories
solo
Work on one hard mathematics problem in this session yourself, with no sub-agents: reason in stages, record each settled step in a notes file so that nothing written is lost if a response is cut off, and end with a self-contained proof.md. Usage: /math-proof:solo .
vss-generate-video-calibration
Use this skill when running or tuning AutoMagicCalib on local MP4s, RTSP, or the bundled sample dataset, or when deploying vss-auto-calibration. Do not use for non-AMC calibration or runtime analytics.
arxiv-package
Package a TeX/LaTeX project into a clean tarball or zip for arXiv upload: file selection, build-artifact exclusion, 00README.XXX generation, ancillary file organization, archive validation. Triggers on: "package for arXiv", "create arXiv tarball", "bundle submission", "zip for arXiv", "prepare arXiv upload", "arXiv…
paper-planning
Guides pre-writing planning for academic papers with 4 structured steps: story design (task-challenge-insight-contribution-advantage), experiment planning (comparisons + ablations), figure design (pipeline + teaser), and 4-week timeline management. Includes counterintuitive planning tactics (write a mock rejection…
model-ml-pipeline
Deterministic entry point for modeling. On a first landing, write the locked baseline design note and ask for approval. Later landings offer only the choices the workspace justifies (an EDA proposal, Backlog, discussion). A missing modeling lock loads frame-ml-problem and stops. After a design is approved, coordinate…
evaluate-ml-pipeline
Evaluate one learner with skore.evaluate. The splitter and any non-default score are already on the DataOp from build-ml-pipeline. This skill writes the call without splitter=, persists the report, and records the locator. When the locked split is the training table and test table that shipped with the data, fit on…