Results for “coordinates”
20 skills
phoroth astropy
Provides guidance for using the Astropy Python library in astronomical research, covering coordinates, units, FITS files, cosmology, tables, time, and WCS transformations.
3 · bundle
lingxling astropy
Provides guidance on using the Astropy library for astronomy and astrophysics workflows, including units, coordinates, FITS I/O, tables, time, WCS, and cosmology.
253 · bundle
nous-hermeshub agent-squad
Main agent orchestrator that coordinates a specialized squad of agents
1
demerzels-lab eywa
Coordinates multiple agents with spatial memory and swarm navigation for collective task execution.
10 · bundle
majiayu000 bob
Coordinates blockchain operations on the Aptos testnet via MCP tools, including balance checks, transfers, swaps, staking, and approvals.
567 · bundle
dvcrn eywa
Coordinates multiple AI agents through a shared room: shared memory, task claiming, conflict detection, and destination tracking.
32 · bundle
More results
machenjie service-business-logic
`analysis-agent`/`task-agent`: use when a use case coordinates authorization, domain work, transactions, or external effects; skip transport, storage, and rule-only work.
4 · bundle
vvieira010-pixel inclusive-design-orchestrator
Coordinates UDL and differentiation tools through a universal-first hierarchy: barrier removal before targeted differentiation before individualised accommodation. Use when planning accessible learning.
0
jorcan astropy
Performs astronomical data analysis with astropy: coordinate transformations, unit conversions, FITS file handling, cosmological calculations, time systems, tables, and WCS.
0 · bundle
aniruddhaadak80 maps
Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
0 · bundle
johnalbertini14-glitch eywa
Coordinates multi-agent swarms with shared spatial memory, task management, conflict detection, and destination navigation via an Eywa room.
1 · bundle
24601 surrealdb
Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.
34 · bundle
jiachen-t-wang coyo-700m-image-text-pair-dataset-github-kakaobrain-coyo-700
COYO-700M: Image-Text Pair Dataset
6
gabrielmoreira polars-bio
Perform fast genomic interval operations (overlap, nearest, merge, coverage, cluster, complement, subtract, count-overlaps), multi-format bioinformatics I/O, DataFusion SQL, and pileup on Polars DataFrames via the polars-bio library, serving as a scalable alternative to bioframe and bedtools.
17 · bundle
tools-only 187-step-459c2d7b
Guides analysis of Neuropixels recordings from raw data to curated units, covering preprocessing, motion correction, spike sorting, quality metrics, and export.
7 · bundle
k-dense-ai astropy
Perform astronomical data analysis with Astropy: coordinate transformations, unit conversions, FITS I/O, cosmological calculations, time handling, table operations, and WCS transformations.
30.2k · bundle
lord1egypt mai
Coordinates shopping between buyers and merchants: publish products, manage stock, answer questions, compare prices, and create trackable orders with local or registry-backed state.
2
huggingface hf-cloud-sagemaker-deployment-planner
Plans and coordinates the deployment of a model to Amazon SageMaker AI, selecting the appropriate pathway (real-time, serverless, async, batch, or Bedrock CMI) based on model type, traffic, latency, and cost constraints.
10.8k
pawbytes paw-ps-agent-product-builder
Product orchestrator for Prodig Suites that guides product planning, routes work to specialists, and coordinates execution across the product lifecycle. Use when the user is shaping a digital product, deciding next steps, or needs help choosing the right Prodig specialist or workflow. Triggers: 'prodig', 'digital product', 'product idea', 'product planning', 'which product agent should I use', 'SaaS planning', 'course creation', 'template pack', 'productized service'.
85 · bundle
schattenspiegel arviz-python
Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.
0 · bundle