「manual」の検索結果

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

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。

Usage manual for building micro-frontend projects with qiankun 3.x. Use when the user wants to create a qiankun main (host) app or sub (micro) app, convert an existing Vite app into a qiankun micro app, or wire up micro-frontend loading with React/Vue.

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。

Manually dispatch and iterate on the Release Canary workflow that smoke-tests published OpenShell artifacts (install.sh on macOS/Ubuntu/Fedora, Helm chart on kind) after each Release Dev publish. Use when changing `.github/workflows/release-canary.yml`, validating a release before tagging, debugging a canary failure, or reproducing a canary job locally. Trigger keywords - release canary, release-canary, canary failed, canary dispatch, test release canary, post-release smoke, install.sh canary, helm chart canary, kind canary, dispatch canary.

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。

Create a bounded, extractive, structurally idempotent rollup of recent Obsidian wiki log entries, with dry-run preview by default and one optional transaction apply. Use for manual log compression without modifying child pages. Triggers: fold the log, run a fold, run wiki-fold, log rollup, roll up log entries, commit the fold.

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。

Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.

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。

Tunes the CARLA Traffic Manager that drives autopilot vehicles — global and per-vehicle speed, following distance, running lights/signs, lane-change behaviour, lane offset, TM-managed lights, deterministic seed, hybrid physics, and TM sync mode. Use when the user asks to "make the traffic faster/slower", "let cars run red lights", "keep more distance", "stop lane changes", "make traffic deterministic", or otherwise change how autopilot cars behave. For manually driving one car, use control-vehicle.

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。

Directly drives a specific vehicle on a running CARLA server — raw VehicleControl (throttle/steer/brake/reverse/hand-brake), Ackermann speed control, vehicle lights, opening/closing doors, and physics-control tuning; ros-info reports the native ROS 2 command topics (vehicle_control_cmd, ackermann_control_cmd) for the hero. Targets the ego by default. Use when the user asks to "drive/steer the car", "accelerate/brake/reverse", "set a target speed", "turn on the headlights/blinkers/brake lights", "open a door", "drive the car from ROS", or "change the vehicle's mass/drag/physics". This is manual control (autopilot off) — for TM-driven traffic use control-traffic.

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。

Runs a single ScenarioRunner Python scenario (or a whole group) against a running CARLA server — FollowLeadingVehicle, ControlLoss, DynamicObjectCrossing, SignalizedJunctionLeftTurn and the rest of srunner/examples — in sync or async mode, with result output, recording and manual/agent control of the ego. Lists what is runnable on the checked-out branch and on the loaded map. Use when the user asks to "run a scenario", "test FollowLeadingVehicle", "list scenarios", "run a CARLA traffic scenario", or reports a scenario that is "not supported".

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。

Adds a new ROS 2 message type to CARLA's native ROS 2 interface — the hand-written POD struct under LibCarla/source/carla/ros2/types/msg, its Fast-CDR serialize/deserialize pair, the RIHS01 type hash computed in Docker, the CdrTopicInfo registration and the type-hash test. Use when the user asks to "add a ROS message type", "support sensor_msgs/X in CARLA", "add a carla_msgs type", "why is my type hash warning appearing", or needs a message CARLA does not publish yet. There is no IDL codegen — every step is manual.

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。

Check regulatory feeds now and report what's new since the last check, filtered by your materiality threshold. Use when the user says "check the feeds", "what's new", "regulatory update", when running from the scheduled agent, or when manually pasting a regulatory development for classification and diff.

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。

Manually distribute working-tree changes (or a batch of edits already made on the current branch) across the correct branches of a Graphite (gt) stack, when gt absorb's blame-based auto-split doesn't match the logical grouping - e.g. new code with no prior line to blame onto, or changes that conceptually belong with a commit gt's algorithm wouldn't pick. Use when asked to "split these changes into the stack", "absorb this properly", "put this fix on the right branch", or when a change was made on top of a branch but really belongs several branches down.

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。

How to correctly add route handlers in a vanilla TypeScript project that uses http.createServer with a manual switch/case router — without introducing Express Router or Next.js patterns.

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。

Single cohesive workflow for creating complete dashboard panel features in vanilla TypeScript — covers project-structure exploration, server dispatch pattern detection and wiring (Express or manual http.createServer), Panel base class component authoring with CircuitBreaker resilience and localStorage persistence, panel registration in index.ts, and safe file writing (including emoji workarounds).

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。

Multi-layer workflow for creating complete panel features by analyzing existing patterns and generating coordinated server routes, services, UI components, registration, styling files, AND explicitly wiring new handlers into the server's dispatch table (supports both Express middleware and manual http.createServer switch/case patterns).

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。

Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.

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。

Guides through Trail of Bits' 5-step secure development workflow. Runs Slither scans, checks special features (upgradeability/ERC conformance/token integration), generates visual security diagrams, helps document security properties for fuzzing/verification, and reviews manual security areas. Use when securing a smart contract end to end rather than hunting one bug, checking a project on every check-in or before deployment, triaging a Slither report, or asking where to start on smart contract security.

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。

Motion / animation context for the `use_figma` MCP tool — animating Figma nodes via manual keyframes, animation styles, easing, and timeline duration. Load alongside figma-use whenever a task involves adding, editing, or inspecting animation on a node.

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。

Translate all text in a Canva design to another language, creating a translated copy. Faster than manually copying and editing each text box in Canva's editor. Use when users say "translate my design to [language]", "make a Spanish/French/etc version", or "localize my Canva design".

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。

Resize a Canva design into multiple social media formats (Facebook post, Facebook story, Instagram post, Instagram story, LinkedIn post). Use this skill when users want to resize Canva designs specifically for multiple social media platforms in one operation, rather than resizing to a single format manually.

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。

Create a punchy sizzle reel from a video using Adobe Quick Cut. Use this skill whenever a user wants to cut, trim, or shorten a video into highlights — including phrases like "make a sizzle reel", "make a highlight reel", "quick cut this", "cut the best parts", "shorten this video", "make a highlight clip", "summarize this video visually", or any request to produce a shorter edited version of a video. Use this skill for Quick Cut requests before suggesting manual editing in Premiere. Requires the user to upload a video file.

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。

Motion / animation context for the `use_figma` MCP tool — animating Figma nodes via manual keyframes, animation styles, easing, and timeline duration. Load alongside figma-use whenever a task involves adding, editing, or inspecting animation on a node.

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。

Manually arbitrate NLA escrow fulfillments as an alternative to the automated oracle. Use when the user wants to review pending arbitration requests, evaluate demands against fulfillments, and submit on-chain decisions. Supports both interactive and LLM-auto modes.

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。

This skill helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping.

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。

This skill helps users extract structured video list data and comment data from YouTube using the BrowserAct API. The Agent should proactively apply this skill when users request searching for YouTube videos and their comments, analyzing viewer sentiment for a specific video topic, gathering audience feedback on AI or automation, extracting a list of top videos and their viewer reactions, compiling YouTube video data along with user opinions, retrieving competitor video titles and related audience discussions, monitoring public response to specific YouTube search keywords, summarizing comments from search results for market research, tracking viewer engagement metrics and replies for trending topics, collecting YouTube video URLs and author details alongside community discussions, or automating the extraction of YouTube comments without manual scraping.

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。