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概要と使いどころ
Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.
日本語の概要は準備中です。原文の説明を表示しています。
Drive Unity 6.3 LTS character animation with Animator Controllers: states, transitions, parameters, blend trees, animation layers, and humanoid Avatar IK. Use when wiring an Animator, setting parameters from script (SetFloat/SetBool/SetTrigger), building blend trees, or when the user mentions Animator, Mecanim, state machine, blend tree, or .controller.
日本語の概要は準備中です。原文の説明を表示しています。
Animate in Godot 4.7 three ways: AnimationPlayer for keyframed clips (incl. call and signal tracks), AnimationTree with state machines and blend spaces for character animation, and Tween for short procedural/UI tweens via create_tween(). Use when working with AnimationPlayer/AnimationTree nodes in a .tscn, blending character states, sprite-sheet animation, or code-driven Tweens.
日本語の概要は準備中です。原文の説明を表示しています。
Implement and verify new React-library ports or copied/rewritten React surfaces in Octane bindings from npm names or npm/GitHub links/lists. Enforces immutable provenance, license boundaries, dependency ordering, full-surface parity, and machine evidence. Existing-binding audits and maintenance start with update-bindings.
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Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/* REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series, annotated chunk search, screener. Use when the Bigdata MCP's tearsheets/search feel thin and you need the machine-readable substrate: mentions of Bigdata.com, RavenPack, a bd_v2_ key, rp_entity_id, or chunk/query_unit cost.
日本語の概要は準備中です。原文の説明を表示しています。
Bridges Claude Code and Codex sessions when native tools can't reach the target. Use for 跨产品通信 / 脚本回帖 / 送达排查: cross-product messages, hooks or scripts posting to a session, verifying delivery (Held messages included), shared-work ownership, or an inbound peer assertion. Not for same-product or parent/subagent messaging (use native tools), spawning agents, moving full history, or cross-machine pairing (deprecated).
日本語の概要は準備中です。原文の説明を表示しています。
Read and manage farm data through Leaf Agriculture: fields, boundaries, machine operation files, and as-applied irrigation. Trigger phrases: leaf agriculture, farm data, field boundaries, as-applied irrigation, john deere data, operation files.
日本語の概要は準備中です。原文の説明を表示しています。
Find and remove AI slop — the generic, machine-default visual and copy tics of vibe-coded products — from a web project. Use when the user asks to "kill AI slop", "de-slop", "remove the AI look", "make this not look AI-generated", or clean up a landing page / UI / docs that feels templated. Detects and fixes the catalogue of tells: indigo→violet gradients, gradient-clip headlines, the default semantic palette, one-hue status boxes, atmospheric gradients, serif-italic emphasis, highlighted keywords, AI copywriting voice ("not just X — it's Y"), emoji everywhere, glowing status dots, wobbling spinners, colored-left-border callouts, pastel icon tiles, glassmorphism, over-rounding, oversized shadows, borders that die at corners, badge & pill spam, AI-drawn SVG icons, kickers over every heading, flat type hierarchies, invented stat rows, 01/02/03 section markers, cards nested in cards, the default Inter/Space Grotesk look, and more. Works on HTML/CSS, React/Vue/Svelte/Astro, Tailwind, PHP, and Markdown copy.
日本語の概要は準備中です。原文の説明を表示しています。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
Hand a spec that is mid-implementation to another machine or another agent.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user's video is on disk - VidSeeds MCP probe, frame/clip extraction, ffmpeg recipes, precision trim, and midroll analysis. Hosted tools return commands/recipes to run locally; media stays on the user's machine.
日本語の概要は準備中です。原文の説明を表示しています。
Use when your machine does not have JuliaLang runtime
日本語の概要は準備中です。原文の説明を表示しています。
Simplify code, plans and systems without losing required outcomes; delete machinery that earns nothing; for code, AST-measured complexity with regression gates. Triggers: simplify, refactor, complexity, overengineered, dead code, useless guard.
日本語の概要は準備中です。原文の説明を表示しています。
Talk to other coding agents on this machine (Claude Code, Codex, Copilot, Cursor, Gemini, Grok, OpenCode) through the magents MCP. Use when the user asks what another agent was working on, wants to carry on that work, send them a message, start an independent agent session, or check the shared inbox. Prefer magents MCP tools over hunting session files on disk.
日本語の概要は準備中です。原文の説明を表示しています。
Retrieve machine-readable context with tanstack libraries, tanstack doc, tanstack search-docs, tanstack create --list-add-ons --json, and --addon-details for agent-safe discovery and preflight validation.
日本語の概要は準備中です。原文の説明を表示しています。
ALWAYS invoke this skill when the user asks about work done outside this session - "check work", "check the others", "check other sessions" - or to accept one: "the session is done, check it", "can we merge it" - in any language. You are the receiving party: treat the report as a claim, verify each part against machine state, name every gap between claimed and true. Verified-ready work merges in the same pass. End with what is left, one next step, and any reply another session needs, ready to paste.
日本語の概要は準備中です。原文の説明を表示しています。
Use when positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect.
日本語の概要は準備中です。原文の説明を表示しています。
Use when organizing the ACM MM (ACM Multimedia) supplementary material due after the paper deadline — deciding what belongs in the 6-8 page body versus the supplement, packaging video/audio/interactive demos that render on a reviewer's machine, keeping all assets anonymous for double-blind tracks, and pointing to code and data.
日本語の概要は準備中です。原文の説明を表示しています。
Search the Wayback Machine for archived versions of websites. Extract cached pages, customer lists, testimonials, and partner directories from sites that have changed or gone offline. Uses the free CDX API — no API key needed.
日本語の概要は準備中です。原文の説明を表示しています。
the UGC fix-loop toolkit — surgically re-render a bad window/beat of a single-take UGC master (stitch_replacement.py, pure FFmpeg) and GPT cross-model review a Seedance prompt before render (vet_seedance_prompt.py, routed through the openai-proxy). Fetch it into a one-shot UGC recipe so both scripts resolve on any machine and the vet call bills the Ads agent.
日本語の概要は準備中です。原文の説明を表示しています。
Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning it.
日本語の概要は準備中です。原文の説明を表示しています。
Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
日本語の概要は準備中です。原文の説明を表示しています。
Build machine vision inspection systems with MATLAB Visual Inspection Toolbox. Covers the full arc from problem selection through deployment: anomaly detection (Student-Teacher, PatchCore, FastFlow, FCDD), object detection (YOLOX), counting (CounTR, shape matching), gauging and measurement (caliper, fitcircle, fitangle), blob analysis (regionprops, calibrated measurement), shape matching for fixturing and localization, synthetic data generation for training augmentation, and deployment to standalone apps, embedded C/C++, NVIDIA GPUs (TensorRT), NPUs (ONNX), and Beckhoff PLCs. Routes to rule-based tools when problems are geometric and deterministic, AI-based tools when defect appearance varies, and hybrid pipelines combining both.
日本語の概要は準備中です。原文の説明を表示しています。