Rechercher

PythonReactSEOPDF

benchflow-ai AI Skills

GitHub ↗

Explorez les skills IA publiés de benchflow-ai et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

349

Étoiles des dépôts des skills indexés

183,468

Forks du dépôt

66,973

Moyenne des étoiles par skill indexé

526

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

349 skills

Explorer

GitHub

badge-evaluation

benchflow-ai

Evaluate research artifacts against NDSS badge criteria (Available, Functional, Reproduced) by checking DOI, documentation, exercisability, and reproducibility requirements.

GitHub

mesh-analysis

benchflow-ai

Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.

GitHub

ac-branch-pi-model

benchflow-ai

AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction,...

GitHub

reflow-machine-maintenance-guidance

benchflow-ai

This skill should be considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This...

GitHub

matched-filtering

benchflow-ai

Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with Py...

GitHub

conditioning

benchflow-ai

Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound...

GitHub

artifact-evaluation

benchflow-ai

Interact with artifact containers via HTTP API for paper evaluation tasks. Execute commands, read files, and list directories in remote artifact environments.

GitHub

d3js-visualization

benchflow-ai

Build deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, p...

GitHub

power-flow-data

benchflow-ai

Power system network data formats and topology. Use when parsing bus, generator, and branch data for power flow analysis.

GitHub

setup-env

benchflow-ai

When given a Python project codebase, this skill helps the agent to set up virtual environments, install dependencies, and run scripts.

GitHub

temporal-python-testing

benchflow-ai

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests o...

GitHub

virtualhome-skills

benchflow-ai

Skill library for embodied household task planning in VirtualHome environments. Provides reusable high-level skills composed of primitive actions to generate executable programs from task descriptions and initi...

GitHub

uv-package-manager

benchflow-ai

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python d...

GitHub

search-attractions

benchflow-ai

Retrieve attractions by city from the bundled dataset. Use this skill when surfacing points of interest or building sightseeing suggestions for a destination.

GitHub

python-packaging

benchflow-ai

Automatically applies when configuring Python project packaging. Ensures proper pyproject.toml setup, project layout, build configuration, metadata, and distribution best practices.

GitHub

locational-marginal-prices

benchflow-ai

Extract locational marginal prices (LMPs) from DC-OPF solutions using dual values. Use when computing nodal electricity prices, reserve clearing prices, or performing price impact analysis.

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.