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 agentmods add skills/dkedar7/fast_dash/fast-dashnpx skills add dkedar7/fast_dash --skill fast-dashgit clone --depth 1 https://github.com/dkedar7/fast_dashWrote 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/dkedar7/fast_dash/fast-dash)<a href="https://agentmods.dev/skills/dkedar7/fast_dash/fast-dash"><img src="https://agentmods.dev/badge/skills/dkedar7/fast_dash/fast-dash.svg" alt="Measured on agentmods" 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 | $0.00069 | $0.01613 |
| Opus 5 | $0.00034 | $0.00807 |
| Sonnet 5 | $0.00014 | $0.00323 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
fast-dash 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 5d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fast Dash
Fast Dash turns a Python function into a Plotly Dash web app. The @fastdash decorator reads the function's signature, maps each parameter's type hint to a UI component, maps the return type to an output component, and serves the result. No frontend, no callbacks, no boilerplate.
When to use this skill
Use this skill when the user:
- has a Python function and wants a UI for it ("make this a web app", "add a form", "dashboard around this")
- is prototyping an ML / data / API tool and wants shareable interactivity
- needs cascading inputs, a multi-step wizard, or multiple tools in one app
Do not use this for: production apps with complex routing, custom auth, or non-Python frontends — Fast Dash is opinionated for the single-file-Python-function use case.
Install
pip install fast-dash
The core pattern
from fast_dash import fastdash
@fastdash
def greet(name: str = "world") -> str:
return f"Hello, {name}!"
# Serving on http://127.0.0.1:8080
That is the whole app. Open the URL, type a name, click Run.
How to approach a Fast Dash task
- Start from the user's function. If they don't have one, write the smallest function that captures their intent.
- Add type hints and defaults. This is where the UI comes from —
int→ number input,bool→ checkbox,strwith a list default → dropdown, etc. See references/components.md for the full table. - Decorate with
@fastdash. For more control (tabbed multi-function apps, multi-step pipelines), use theFastDash(...)class directly. - Run and verify. The decorator starts a server immediately on import. For notebooks, pass
mode="inline".
Common patterns
| Pattern | Syntax | When |
|---|---|---|
| Single function | @fastdash |
One tool, one form |
| Multiple outputs | -> (Graph, Graph) + mosaic="AB" |
Dashboard with several plots |
| Cascading inputs | state=depends_on("country", resolver) |
Dependent dropdowns |
| Multiple tools, one app | FastDash([fn_a, fn_b], tab_titles=[...]) |
Tabbed "apps" under one URL |
| Multi-step wizard | FastDash(steps=[fn_a, fn_b, fn_c]) + from_step(prev_fn) |
Pipeline UX, one panel at a time |
| Streaming outputs | update("output_x", chunk) inside the fn + stream=True |
LLM / token-by-token / progress |
| Notebook rendering | @fastdash(mode="inline") |
Jupyter |
| Wrap a custom component | Fastify(dcc.Slider(...), "value") |
Any Dash component |
What ships with it
3 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.
- 5d ago First seen · 135 lines · 69 tokens per session scan A 99e5d5221708
fast-dash is a skill published in the GitHub repository dkedar7/fast_dash (129 stars, last pushed 29d ago), licensed MIT. It adds 69 tokens to every session and 1,613 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
improving-python-coverage
Runs Python unit tests with coverage, analyzes coverage reports, and implements meaningful tests to increase coverage by 0.2%. Use when you want to systematically improve Python test coverage with high-value test cases.
marimo-pair
Work inside the user's live marimo notebook from the code editor: run Python in the same kernel the user does, inspect live notebook state, and commit durable notebook changes through code mode. Use whenever you create, analyze, or improve the user's marimo notebook.
fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC…
astropy
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve…
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed…
simpy
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.