「ods」の検索結果

Tells you what CARLA on UE 5.5 (branch ue5-dev) cannot do that UE 5.8 (ue58-dev) can, so you do not attempt an unsupported workflow there — no Autoware integration, no ROS 2 middleware choice (FastDDS only), no DLSS or ray-traced-lens camera, no OFPA large-map mount so Town12/13/15 load black, and a few missing World/Actor methods. Also states what is identical, so the ue58 skills can be used directly for everything else. Use when working against a 5.5 checkout, when a ue58 skill's step has no counterpart, or when deciding whether a feature request is possible on 5.5 at all.

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Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

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Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation frameworks; analyzing coverage gaps; performance testing with k6 or Artillery; security testing with OWASP methods; debugging flaky tests; or working on QA, regression, test automation, quality gates, shift-left testing, or test maintenance.

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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Review open NPRM comment periods, log decisions, track deadlines. Use when an NPRM has a comment window open and you need to surface deadlines, decide whether to file, or record a filing / not-filing / waived decision (--decide CMT-ID).

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Termination review — high-risk flag detection, severance + release, and final pay timing by jurisdiction. Jurisdiction-specific rules and release consideration periods are researched per review, not stored. Use when the user says "reviewing a termination", "can we fire this person", "term review", or describes a termination scenario.

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Scan Bifrost HTTP controllers/handlers/integrations and validate OpenAPI API coverage, route methods/paths, parameters, request/response docs, and auth/security information. Use when asked to audit missing or incorrect APIs, compare controllers against docs/openapi, validate auth information, or fix API documentation drift. Invoked with /api-validator [scope] [--fix].

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Authoring guide for the primary Native SDK app-logic path: TypeScript app cores - Model, Msg, update, and the pure functions they call, written in the closed app-core subset, checked by the @native-sdk/core frontend, and compiled ahead-of-time to native code by the external core compiler. Use for new apps unless the user explicitly chose Zig, when writing or modifying a src/core.ts app core, fixing subset checker errors (NS1001-NS1069), or deciding how to express state, messages, text (bytes and the byte-text string methods), text input, continuous controls (sliders, scroll), effects (Cmd), subscriptions (Sub), the host-event wiring channels (frameMsg, keyMsg, pinchMsg, dropMsg, appearanceMsg, chromeMsg, envMsgs, app manifest assets), derived values, the view_unbound lint opt-out, local mutation of owned arrays, or how to split a core into modules under src/ (relative imports, namespace imports, @native-sdk/core/text, @native-sdk/core/events). Use ts-services alongside this guide for src/services work.

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Handle tool failures when researching regulatory/government content by using fallback methods and domain knowledge

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Kubernetes cluster attack techniques covering the full attack lifecycle from initial foothold in a pod to cluster-wide compromise. Covers service account token theft and impersonation, RBAC misconfiguration exploitation including wildcard permissions and privilege escalation via role binding, direct etcd access for secret extraction, kubelet API abuse on port 10250 and read-only port 10255, pod escape via hostPID hostNetwork and hostPath volume mounts, Kubernetes secrets enumeration and decoding, admission controller bypass techniques, network policy bypass and lateral movement, cloud metadata service access from pods for credential theft on AWS EKS GCP GKE and Azure AKS, CRD and operator abuse for persistence, and node compromise via DaemonSet deployment. Tools include kubectl, kube-hunter, peirates, kubeaudit, kdigger, kubeletctl. Maps to MITRE ATT&CK T1609 Container Administration Command, T1610 Deploy Container, T1613 Container and Resource Discovery. Use this skill when assessing Kubernetes clusters, attacking from within a compromised pod, exploiting RBAC or kubelet misconfigurations, or performing cloud-native lateral movement.

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Validate that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after recipe-runner brings a deployment up (especially disagg/multi-node) to confirm the KV transport is correct; use troubleshoot for diagnosing already-failed pods.

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Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Package and release a finished game mod. Covers the pre-release lint (no game files, decompiled code or leaked keys), README and install steps, credits and AI disclosure, versioning, where to upload (Nexus Mods, Steam Workshop, Thunderstore, mod.io, GitHub releases), and the post with a video. Use when the user wants to ship, share, upload, release or post a mod.

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Launch, see and drive a real game so an agent can test its own mods. Covers launching via Steam or the exe, windowed mode at a fixed size, screenshots of the game window (GPU-safe capture), clicking and typing into menus, holding keys, relative mouse, crash-reporter cleanup, killing by PID, reading logs, scripted test scenes, and an in-game JSON bridge that lets an agent observe and act. Windows-first (also from WSL), with notes for Linux and macOS. Use when a mod must be verified in game, when navigating menus automatically, or when building a repeatable test or demo scene.

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Calculate TAM, SAM, and SOM with explicit assumptions, methods, and caveats. Use when sizing a market for a product idea, business case, or executive review.

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Build, integrate, audit, or refactor rigged monsters for Three.js and web action games. Use for monster asset contracts, procedural or imported creature rigs, semantic joints and sockets, hurtboxes and attack volumes, combat animation states, enemy-runtime adapters, LODs, deterministic review fixtures, and validating that every monster follows one shared system.

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Create or integrate highly detailed 3D models with smooth silhouettes, shaped surfaces, believable bevels, and close-up geometry, then prepare suitable runtime LODs and loading. Use when a user requests high polygon counts, ultra-realistic models, detailed architectural assets, or removal of visibly faceted geometry.

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TypeScript SDK for the Payment HTTP Authentication Scheme. Handles 402 Payment Required flows with Tempo, Stripe, and other payment methods. Use when integrating payments or mppx into a client or server application.

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TypeScript library for NEAR Protocol blockchain interaction. Use this skill when writing code that interacts with NEAR Protocol, including viewing contract data, calling contract methods, sending NEAR tokens, building transactions, creating type-safe contract wrappers, integrating wallets (Wallet Selector, HOT Connect), React hooks and providers (@near-kit/react), managing keys, testing with sandbox, meta-transactions (NEP-366), and message signing (NEP-413).

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0G Compute Network guide for decentralized AI inference, fine-tuning, and GPU services. Covers chatbots, image generation, speech-to-text, SDK integration (0g-serving-broker), processResponse API, broker.inference methods, CLI commands (0g-compute-cli), and account management. Use this skill for any 0G compute, 0G AI, or decentralized GPU question.

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This skill helps users extract basic product details other sellers prices and seller ratings from Amazon via ASIN automatically using the BrowserAct API. Agent should proactively apply this skill when users express needs like query Amazon buy box information, monitor Amazon product prices, extract Amazon product details by ASIN, check other sellers prices on Amazon, get Amazon seller ratings and feedback count, monitor buy box ownership for a specific ASIN, track Amazon fulfillment methods for competitors, compare Amazon product prices across different sellers, retrieve Amazon buy box availability status, analyze Amazon seller profile details.

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Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English.

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。