2.4K skills · sorted by relevance
Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Train, deploy, and run predictions with neural networks using Flow Nexus and E2B sandboxes, isolated computing environments. Use existing model templates, distribute training across multiple environments, and check training progress and model performance.
Create and manage Boat cloud sandboxes, remote machines for development work, through API, CLI, or SSH. Configure repositories and secrets, prepare reusable templates, manage machine state, and verify coding agents inside the selected environment.
Isolate parallel coding tasks with Git worktrees, separate working folders with their own branches. The skill guides environment setup, dependency installation, change review, merging, and cleanup so each task has a dedicated workspace.
Inspect and manage BB threads, environments, projects, and plugins through its command line interface. The skill guides agents to identify the correct targets, apply changes, and verify the resulting state.
Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.
Set up Earth2Studio with uv or pip using commands matched to your environment and planned models. The skill guides optional dependency selection, cache configuration, installation checks, and dependency troubleshooting while leaving package installation to the user.
Prepare an environment for evaluating speech recognition on clinical terms. Checks the API key and Python dependencies, then tests NVIDIA's hosted services by generating speech from one synthetic sentence and transcribing it before the next stage.
Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.
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Deploy an OpenEnv environment to Hugging Face Spaces. Use when asked to deploy, push to Hugging Face, or update a space.
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Release workflow for deploying OpenEnv environments to Hugging Face Spaces and keeping canonical references in sync.
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Designs and runs cloud infrastructure — environments, infrastructure as code, networking and isolation, scaling, and cost. Use this to design a cloud environment, control infrastructure spend, set up environment separation, plan for scale or region failure, or review infrastructure someone configured by hand.
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Configure cross-env for cross-platform environment variable setting in npm scripts and CLI commands.
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Integrate dotenv for environment variable loading with validation and type coercion.
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Use after first-run for ongoing modpack maintenance: update/install KB packs, author or register custom/mod KB packs, maintain localization glossary KBs for translator use, prune KB cache, health-check the environment, version-pin KB/tooling, or handle recurring BGS modding environment care.
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Use when targeting Environmental Health Perspectives or deciding whether an environmental-health manuscript fits this venue. Encodes the journal's fit, the public-health-relevance and methodological-rigor bar, epidemiological/toxicological expectations, house style, official-submission re-check, and desk-reject heuristics.
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Use when targeting Environment International or deciding whether an environmental-exposure/health manuscript fits this venue. Encodes the journal's fit, the population-relevant exposure-health evidence bar, exposome/contaminant expectations, house style, official-submission re-check, and desk-reject heuristics.
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Use when targeting Communications Earth & Environment or deciding whether an earth, environmental, or planetary-science manuscript fits this venue. Encodes the journal's fit, the solid-and-complete-advance bar, Nature Portfolio reporting/data standards, house style, official-submission re-check, and desk-reject heuristics.
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Use when targeting the Annual Review of Environment and Resources or deciding whether an environment-and-resources synthesis fits this invited-review venue. Encodes the journal's invited-review fit, the authoritative-synthesis bar, structure expectations, official-submission re-check, and desk-reject heuristics.
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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.
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Set up worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an anti-pattern). Triggers: worker setup, pool constant, per-worker state, non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel code.
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Call Python libraries from MATLAB using the py. interface, pyrun, pyrunfile, or pyenv. Use when writing or executing MATLAB code that calls Python functions or passes data between MATLAB and Python. REQUIRED when triaging Python errors from MATLAB (ModuleNotFoundError, ImportError, "Unable to resolve the name 'py.*'"). REQUIRED when setting up Python environments for MATLAB, creating virtual environments, or installing Python packages for use with MATLAB.
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Compute aerospace environment properties including atmosphere (ISA, COESA, NRLMSISE-00, non-standard, CIRA), gravity (spherical harmonic, WGS84, zonal, centrifugal), horizontal wind (HWM), magnetic field (WMM, IGRF), geoid height, geocentric radius, space weather data, planetary ephemeris, Earth orientation (polar motion, nutation, delta-UT1, CIP). Use when computing atmospheric density, temperature, pressure, gravity vectors, wind profiles, magnetic field components, geoid undulation, solar flux indices, planet positions, or Earth orientation parameters for aerospace vehicle analysis, spacecraft environment modeling, or navigation corrections.
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CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.
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