「map」の検索結果

Create, migrate, improve, or debug detailed vehicle assets for CARLA using Blender and Unreal, including reference-based geometry, materials, wheels, physics, lights, and runtime validation. Use for vehicle asset authoring rather than ordinary traffic spawning or map decoration.

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。

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.

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。

Draws debug overlays in a running CARLA world via world.debug — points, lines, arrows, bounding boxes, and text strings, each with a colour and lifetime. Use when the user asks to "draw/mark/highlight a point or box in the world", "visualise a path/waypoints", "label a location", or "show something on the map for debugging". Shapes are an overlay, not actors.

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。

Answers "can CARLA do X here, and is there a vetted procedure for it" before you promise anything, and detects client/server/tree version skew — a support matrix over the whole collection (covered by a skill / works but undocumented / present but untested / broken or removed), an index of the features that are verified BROKEN on 0.10.0 (gbuffers crash the server, map layers do nothing, Landmark.waypoint is None, standalone asset packages are gone), and a probe that reports what this machine's build flags and running server actually offer. Use when the user asks whether CARLA supports something, when a feature has no skill, when a UE4 tutorial's steps do not exist, or before improvising a procedure.

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。

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".

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 Scenic probabilistic scenarios against a running CARLA server with the `scenic FILE --simulate --2d` CLI — the carlaChallenge1-10 traffic scenarios shipped by ScenarioRunner (`model srunner.scenic.models.model`) and by Scenic itself (`model scenic.simulators.carla.model`) — bounding the run, sampling scenes headlessly first to tell a map problem from a syntax problem, and restoring the world afterwards. Use when the user asks to "run a Scenic scenario", "run carlaChallenge", "test a .scenic file", "scenic --simulate", or reports a Scenic scenario that rejects every sample or cannot find blueprints.

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。

Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.

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。

Replays a CARLA .log recorded by the recorder — the whole run or a time window, at any speed, following a chosen actor, optionally regenerating sensors and restoring weather. Use when the user asks to "replay the recording", "play back the run", "watch it again in slow motion / at 2x", "follow vehicle N during replay", or "replay onto a different map".

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。

Downloads CARLA so nothing has to be fetched by hand — an official release package for any published version (resolved live from the GitHub releases, with AdditionalMaps), the rolling nightly Dev build, a shallow source checkout of a branch or tag, or the carlasim/carla Docker image. Reports exactly what was obtained and where, plus the environment variables the other skills read. Use when the user asks to "download CARLA", "get CARLA 0.9.16", "install CARLA", "clone the carla repo", "fetch the latest CARLA", or has no CARLA at all yet.

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。

Controls the map's light sources through the CARLA LightManager — turn street lamps, building/facade lights, vehicle lights or all of them on/off, set their colour and intensity, and toggle the automatic lights-at-night cycle. Use when the user asks to "turn on the street lights", "light up the city at night", "turn off building lights", "change the street-light colour", or "make lights come on automatically at night". Distinct from hiding lamp meshes (toggle-env-objects) and the sun (set-weather).

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 the CARLA Leaderboard evaluator over a set of routes with your agent — routes/routes-subset/repetitions/track selection, checkpoint and live-results endpoints, resume after a crash, recording, and the debug levels — for leaderboard 1.0, 2.0 or 2.1. Preflights the whole stack (version pairing, maps, ports, agent class, sensor budget) before spending hours, and hands the world back in async mode. Use when the user asks to "run the leaderboard", "evaluate my agent", "run routes_training", "resume an evaluation", or "reproduce my submission locally".

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。

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

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 writing or auditing the conversational copy layer of a Salesforce bot or Agentforce agent: utterance authoring strategy, fallback message copy, escalation-criteria phrasing, and persona-consistent dialog scripting across channels. NOT for platform configuration (bot builder setup, topic metadata wiring, action mapping, or Agentforce deployment) — see architect/einstein-bot-architecture and agentforce/agentforce-persona-design for those concerns.

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。

MUST USE for building FastAPI/REST API applications with Aerospike database (aerospike-py Rust/PyO3 async client). Covers AsyncClient lifespan management, FastAPI Depends injection, aerospike_py exception-to-HTTP-status mapping (RecordNotFound→404, RecordExistsError→409, BackpressureError→503, AerospikeTimeoutError→504), POLICY_EXISTS_CREATE_ONLY/UPDATE_ONLY for CRUD semantics, NamedTuple attribute access (record.bins not tuple unpacking), client.ping() readiness probe, batch_read returning dict, batch_write with in_doubt retry signal, and global AerospikeError handler. Triggers on: FastAPI + Aerospike, REST API + aerospike-py, CRUD API with Aerospike, client.ping() health probe, batch_read/batch_write endpoint, web server/HTTP service backed by Aerospike NoSQL, uvicorn + Aerospike.

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 designing how an Agentforce agent extracts structured input parameters (slots) from a user's natural-language utterance to invoke an action. Triggers: 'agent extract date from user', 'agent action argument extraction', 'agentforce slot filling', 'agent invocable input mapping', 'agent fails to fill required parameter'. NOT for action authoring (use agentforce/agent-actions) or for prompt-template variable binding (use agentforce/prompt-builder-templates).

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 you need to design, review, or improve REST APIs with Quarkus REST (Jakarta REST) — including resource classes, HTTP methods, status codes, request/response DTOs, Bean Validation, exception mappers, optional runtime OpenAPI exposure (SmallRye), contract-first generation from OpenAPI, content negotiation, pagination, sorting and filtering, API versioning, idempotency (Idempotency-Key), optimistic concurrency (ETag / If-Match), HTTP caching (Cache-Control), API deprecation (Sunset / Deprecation headers), RFC 7807 Problem Details, ISO-8601 for time in contracts, and security-aware boundaries. This should trigger for requests such as Review or improve JAX-RS resources in a Quarkus project; Design HTTP APIs with validation and error handling on Quarkus; Add API versioning, idempotency, ETag concurrency, or deprecation headers; Implement pagination, sorting, or RFC 7807 Problem Details error responses. Part of cursor-rules-java project

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 you need to design, review, or improve security in Quarkus applications — including Quarkus Security with JWT/OIDC, basic auth, @RolesAllowed / @Authenticated / @PermitAll, SecurityIdentity, permission checks, path-based authorization in configuration, exception mapping for auth failures, and sensitive-data-safe logging. This should trigger for requests such as Add Quarkus security support; Review Quarkus security configuration; Improve API authorization in Quarkus; Add JWT/OIDC security in Quarkus; Harden Quarkus authorization rules. Part of cursor-rules-java project

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 you need to design, review, or improve security in Micronaut applications — including micronaut-security authentication, @Secured and intercept-url-map rules, JWT/session strategies, SecurityService checks, CORS, CSRF awareness for browser apps, rejection handlers, and sensitive-data-safe logging. This should trigger for requests such as Add Micronaut security support; Review Micronaut security configuration; Improve API authorization in Micronaut; Add JWT security in Micronaut; Harden Micronaut route authorization rules. Part of cursor-rules-java project

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。

Entry-point router skill for the 1Password CLI. Use this skill when the user mentions the 1Password CLI (`op`) or 1Password generically, references the secret-reference URI scheme `op://`, asks about `OP_ACCOUNT` or `OP_SERVICE_ACCOUNT_TOKEN`, asks about biometric unlock for a CLI, or is choosing how to load secrets into an app and 1Password is a candidate. Provides general orientation, the auth-mode picker, the full command map, and routing to deeper sub-skills (`op-secrets-injection`, `op-item-management`, `op-provisioning`, `op-ssh-keys`, `op-shell-plugins`, `op-service-accounts`). Defer to those sub-skills for specific commands.

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。

Run a health check on an existing project: dependency audit, security scan, test runner detection, CI/CD evaluation, and missing configuration analysis. Maps the three execution gates (pre/in/post) from /10x-bootstrapper to an assessment framework for existing codebases. Reads optional context/foundation/stack-assessment.md from /10x-stack-assess to focus checks on identified gaps. Writes context/foundation/health-check.md with findings, prioritized fixes, and an agent-readiness verdict. Use when the user has an existing project and wants to verify its health before working with an agent. Trigger phrases: "health check", "check my project", "audit my project", "is my project healthy", "sprawdź projekt", "audyt projektu", "health-check", "project health". Use AFTER /10x-stack-assess (brownfield chain), BEFORE agent onboarding (m1-l4).

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。

Assemble a Japanese shokumu-keirekisho (career history document) from an experience inventory, choosing between reverse-chronological, chronological, and skills-based layouts, mapping the target posting's requirements to the experiences that can support them, and drafting the summary, work history, skills, and self-PR so that each sentence stays within what its evidence supports and confidential details are abstracted. Use when a user wants to write or rebuild a whole shokumu-keirekisho for a specific posting or as a general version, or wants to decide which experiences to include and in what order; do not use to invent experience, numbers, titles, or qualifications, to predict whether it will pass screening, to fill in a rirekisho form, or to submit it anywhere.

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。

Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.

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。

Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.

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。