Email, calendars, task management, and automation (7.8K skills)
Cut or check Runner nightlies — one workflow for macOS and Windows, one public prerelease per channel (nightly from main, remote-nightly from the nightly-remote umbrella), shared stamp, and separate stable feeds
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Write less code and say less about it: YAGNI, stdlib-first, terse prose. Cuts agent token cost on every coding and writing task.
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Show Honey's benchmark scoreboard: committed quality and token results per task tier vs Caveman, Ponytail, and no-skill. One-shot.
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Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file work.
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Show Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline, or for the headline numbers.
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Router for FunASR speech workflows: local transcription, subtitles, services, vLLM, normalization, and training/export.
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Operate Flash Linear Attention package workflows: setup, kernels, layers/models, KDA/context parallel, and benchmarking.
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Build, inspect, and reuse Featuretools automated feature engineering workflows for EntitySets, DFS, feature matrices, primitive authoring, feature inspection, and selection.
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Route GenAI Processors tasks to the right workflow guide.
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Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection.
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Routes evo trajectory-evaluation workflows for APE/RPE, trajectory I/O, result analysis, configuration, and programmatic usage.
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Use FedML for setup, CLI operations, MLOps launch, distributed and federated training, model serving, and workflow orchestration.
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Use the einops Python package for readable tensor rearrangement, reductions, repetition, named-axis einsum, packing, framework layers, and repository maintenance workflows.
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Use DeepMind dm_control for MuJoCo simulation, Control Suite environments, PyMJCF models, Composer tasks, rendering, and high-level locomotion/manipulation workflows.
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Use Docling to convert, parse, configure, extract, chunk, serve, and maintain document-processing workflows across Python APIs, CLI commands, optional pipelines, and repository edits.
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Use this skill for Hugging Face Diffusers tasks: pipeline inference, schedulers, adapters/loaders, training recipes, modular pipelines, conversion helpers, CLI checks, and repo maintenance.
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Routes DI-engine users to the right workflow for CLI/config launches, serial and framework-based RL pipelines, and environment integration.
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Load, generate, compile, rasterize, evaluate, train, and serve DeTikZify and Ti*k*Zero workflows.
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Use for Electricity Maps contrib parser development, capacity updates, zone/exchange configuration maintenance, and focused repo validation workflows.
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Use Dipy for diffusion MRI IO, preprocessing, reconstruction, tractography, registration, visualization-aware workflows, and command-line operations.
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Use Hugging Face Evaluate to load metrics, comparisons, and measurements; compute and combine results; run evaluator pipelines; create custom modules; troubleshoot optional dependencies, cache, Hub, and CLI workflows.
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Routes Distil-Whisper inference, PyTorch distillation training, and Flax reproduction workflows.
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Route DeepLabCut markerless pose-estimation workflows, project setup, PyTorch training/inference, maDLC tracking, Model Zoo, post-processing, 3D, and export tasks.
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Route Torchreid deep-person-reid workflows for person re-identification training, evaluation, feature extraction, metrics, visualization, and model export.
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