125건 · 관련도순
MCP Agent Mail for multi-agent coordination. Use when agents need file locks, messaging, inboxes, or conflict prevention. Handles macro_start_session, file_reservation_paths, send_message, threading, pre-commit guards.
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Iterative UI/UX polishing workflow for web applications. The exact prompt and methodology for achieving Stripe-level visual polish through multiple passes.
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Find opportunities to improve web application code using TanStack libraries (Query, Table, Form, Router, etc.). Avoid man-with-hammer syndrome by applying TanStack after vanilla implementation works.
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The philosophy and practical benefits of agent fungibility in multi-agent software development. Why homogeneous, interchangeable agents outperform specialized role-based systems at scale.
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Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive workflows.
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Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.
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Start a new Flywheel planning round. Produce initial markdown plan, route to competing frontier models, synthesize hybrid, refine 4–5×. Use before any coding begins on a new feature.
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Synthesize competing plans from multiple frontier models into a single best-of-all-worlds hybrid. Use after /plan has produced 2+ competing plans in .flywheel/plans/.
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Design and audit PFC 5.0 asphalt dynamic loading and stress-wave studies with damping, timestep, rate, dispersion, source direction, boundary reflection, histories, and wheel-load handoff checks.
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Orchestrate PFC2D/PFC3D 5.0 asphalt-mixture case handoff, specimen generation, fistPkg26 integration, Burger calibration, Marshall or rutting tests, validation, and reproducible delivery; use whenever a user mentions PFC5 asphalt, AC mixtures, fistPkg, Marshall, wheel tracking, or rutting.
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Design, implement, and audit PFC3D 5.0 asphalt wheel-tracking and intersection-rutting simulations using RVE-to-strip-to-full-size scaling, PFC5-compatible vector reaction control, Burger-calibrated contacts, vertical-horizontal coupled loads, rut/shear histories, edition-specific metrics, and explicit surrogate validation.
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Use when a task needs the judgment of a bicycle repairer — diagnosing drivetrain skip and quoting chain-vs-cassette replacement, torquing carbon components to spec, bleeding hydraulic disc brakes, truing a wheel to tension (not just by eye), or deciding whether a safety-critical fastener issue needs to be declined-in-writing rather than patched.
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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).
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Checks outdated packages, unused deps, reinvented wheels, vulnerability scan (CVE/CVSS). Supports mode: full | vulnerabilities_only.
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Auto-activate for uv build, hatch build, PyApp, PYAPP_*, wheel assets, GitHub release matrices, cargo-zigbuild, or python-build-standalone. Not for runtime deployment.
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Expert-level energy storage covering pumped hydro, battery storage, compressed air, thermal storage, hydrogen, flywheel, and the role of storage in energy transition.
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Deploy a Python project to the target environment. Runs pytest, builds the wheel, and pushes a deployment tag. Only invoke manually when ready to deploy.
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Use when auditing a CPython C/C++/Cython/Rust extension for free-threaded Python, a `t`-suffixed ABI wheel, GIL opt-out, native race, callback crash, or free-threaded release gate.
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Operational guide for the NV oOS Model Foundry (Proposal 067) — the cluster fine-tuning flywheel. Phase 1 shipped: the Corpus Foundry (Pro addon, addons/pro/includes/model-foundry/) — consent-gated, deduplicated, PII-scrubbed corpus exporters (trajectory_v1, preference_v1, docs_v1) with deterministic holdout splits and R12 provenance manifests. Covers the governance contract, row schemas, output layout, filter seams, the registration pattern for foundry tool slices, how to add a new exporter, and the test/validation gates. Use when extending the Corpus Foundry, debugging corpus exports, wiring a new data source, or continuing Phases 2–6 (train-worker, registry/serving, eval gate, fleet sharing).
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Build and leverage online communities to drive product growth and brand loyalty. Use when the user wants to create a community strategy, grow a Discord or Slack community, manage a forum or subreddit, build brand advocates, increase word-of-mouth, drive community-led growth, engage users post-signup, or turn customers into evangelists. Trigger phrases: "build a community," "community strategy," "Discord community," "Slack community," "community-led growth," "brand advocates," "user community," "forum strategy," "community engagement," "grow our community," "ambassador program," "community flywheel."
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Publish a released version of the Apache SkyWalking AI Sessionizer LangChain plugin, apache-skywalking-asz-langchain, to PyPI. Says what a release manager sets up before the first upload, downloads the voted source package, verifies it, builds the source distribution and the wheel from plugins/langchain inside it, checks and installs them, and uploads them with twine. Use after a version is published.
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Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.
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Create, migrate, improve, or debug detailed vehicle assets for CARLA using Blender and Unreal, including reference-based geometry, materials, wheels, physics, lights, and runtime validation. Use for vehicle asset authoring rather than ordinary traffic spawning or map decoration.
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Installs CARLA's ScenarioRunner (the official scenario engine) and picks the branch that matches the CARLA in use — master for 0.9.14-0.9.16 (UE4), ue5-master for 0.10.0 (UE5), leaderboard-1.0/2.0/2.1 when driving the Leaderboard. Clones or checks out the branch, installs requirements, wires PYTHONPATH (including the `agents` package the carla wheel does not ship), and verifies the import. Use when the user asks to "install scenario runner", "set up SR", "run CARLA scenarios", "use OpenSCENARIO", or has a scenario_runner that fails to import.
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