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概要と使いどころ
Find and merge duplicate Linear issues — group reports of the same underlying bug, pick the survivor, and move the evidence across. Use when the backlog has grown noisy, someone asks whether an issue is already reported, or you are cleaning up before planning.
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Launches a CARLA RPC server a carla.Client can connect to, working out for itself what you pointed it at — a downloaded/extracted release, a package cooked inside a checkout, or a source checkout run through the UE4 editor — headless or windowed, and shuts it down cleanly. ROS2=1 starts it with the native ROS 2 interface active (--ros2, --rmw=fastdds/cyclonedds/zenoh, --ros-domain-id). Use when the user asks to "run/start the CARLA server", "boot CARLA headless", "launch CARLA with a window", "run the CARLA I downloaded", "start CARLA with ROS2 enabled", or "serve a packaged CARLA build".
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Imports FBX meshes into CARLA on UE 5.8 as spawnable props — drives the editor headlessly to import the mesh, derives the prop size from its measured bounds, and registers it in PropParameters.json so it appears in the blueprint library as static.prop.<name>. Handles the FBX unit-scale problem, verifies the result against a running server, and can revert cleanly. Use when the user asks to "import a prop", "add a custom mesh/object to CARLA", "make my model spawnable", or a prop imports but never shows up in the blueprint library.
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Starts a CARLA UE 5.8 server from a ue58-dev tree — the editor binary in -game mode (the working headless server), the packaged shipping build, or the full editor — with offscreen/nullrhi rendering, custom RPC and streaming ports, and native ROS 2 via the single-dash -ros2 flag. Probes a running server, and stops one cleanly despite the 15-character process-name truncation. Use when the user asks to "run/start CARLA UE5", "launch a ue58 server", "run headless", "publish ROS 2 topics from UE5", or a client cannot reach the simulator.
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Spawns vehicles on a running CARLA server and destroys them — scattered across the map's spawn points on Traffic Manager autopilot, a row queued in a single lane at a fixed spacing, or a single hero/ego vehicle (optionally named for native ROS 2 publishing with --ros-name). Covers blueprint filtering, the atomic autopilot hand-off, and cleanup. Use when the user asks to "spawn vehicles/cars/traffic", "add N cars driving around", "put cars in a lane 15 m apart", "spawn the ego/hero vehicle", or "remove the vehicles".
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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
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Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
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。
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.
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Use when building .NET 8 applications with minimal APIs, clean architecture, or cloud-native microservices. Invoke for Entity Framework Core, CQRS with MediatR, JWT authentication, AOT compilation.
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。
Scan open issues and discussions, deduplicate, prioritize, and propose labels. Provides a clean actionable queue.
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。
Scan recent merges to main for tech debt, TODOs, debug code, and small cleanup opportunities. Produces a prioritized fix list.
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Takes a structured list of changes from changelog-scan and produces a clean, categorized, user-facing release notes draft. Follows project voice and conventions. Never publishes — only proposes a draft file or section.
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。
Turn raw notes or a transcript into a clean summary with decisions and action items.
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。
Verify an implementer's diff with an INDEPENDENT, different-vendor sub-agent (diff plus contract only); turn blocking issues into fix-tasks and loop until clean.
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Archive accumulated phase directories from completed milestones
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Create a clean PR branch by filtering out .planning/ commits — ready for code review
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End-of-semester case handoff memos — the mirror of /ramp. Produces per-case transition memos and a cohort summary so the departing cohort hands work to the incoming cohort cleanly. Reads deadlines, client-comms, and case history. Use when the professor or departing students need to wrap up the semester, build transition memos, or offboard a graduating/withdrawing student.
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。
Resolve unresolved PR review comments across an entire Graphite (gt) stack of many PRs, bottom-up, in one working directory. Use when asked to "go through this stack and resolve comments", "clean up review comments across the whole stack", or given a list/range of PR numbers that form (or partially form) a gt stack. Distinct from the single-PR resolve-pr-comments skill, which this one uses as its per-branch inner loop.
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Workaround for write_file failures caused by special characters (apostrophes, backticks, template literals) using a Python heredoc script, with mandatory post-write verification via read_file and cleanup of /tmp helper scripts.
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Unified guide for authoring dashboard panel components in vanilla TypeScript — covers the authoritative Panel base class (grid-span persistence, dual resize handles, radar loading animation, exponential-backoff error countdown, data/new badge helpers, full destroy() cleanup), localStorage state via loadMap/saveMap, exponential-backoff retry, and CircuitBreaker integration. Shows how to wrap any existing fetchX() service call with CircuitBreaker.execute() as a one-liner and clarifies when each resilience layer is appropriate.
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Build cross-game mashups and total conversions, the "Minecraft inside Elden Ring" or "skateboarding in MW2" kind. Covers passthrough mods (two games running at once and exchanging state, rendering and collision), porting one game's content or mechanics into another, embedding a decompiled game as a library, and clean reimplementations that read the user's own game files. Use when the user wants to combine two games, bring an enemy, mechanic or world from one game into another, or rebuild a game's runtime, or reimplement the guest's rules headless inside a real host.
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。