「actors」の検索結果

Automate web scraping and data extraction with Apify -- run Actors, manage datasets, create reusable tasks, and retrieve crawl results through the Composio Apify integration.

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 large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of 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。

Gets actor and level 3D bounding boxes and projects them into a camera image as 2D boxes — the dataset-annotation workflow. Lists box geometry, draws 3D boxes in the world via debug, or captures one camera frame and writes an annotated PNG plus a JSON of 2D boxes for matching actors. Use when the user asks to "get bounding boxes", "draw boxes around the cars", "project boxes onto the camera", or "generate a detection dataset / annotations".

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。

Writes, debugs, and refactors JavaScript code using modern ES2023+ features, async/await patterns, ESM module systems, and Node.js APIs. Use when building vanilla JavaScript applications, implementing Promise-based async flows, optimising browser or Node.js performance, working with Web Workers or Fetch API, or reviewing .js/.mjs/.cjs files for correctness and best practices.

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。

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve 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。

Trademark clearance first pass — knockout + similar-marks check producing a flag list, not a clearance opinion. Use when a new mark is proposed, when asked whether a mark is available or to run a knockout search, or when assessing likelihood-of-confusion factors before a full professional search. This skill never concludes a mark is clear.

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。

Infringement triage across trademark, copyright, patent, and trade secret — a flag list with the factors cutting each way, not a finding. Use when assessing whether someone is infringing your IP or whether you might be infringing theirs, when a knockoff or copycat surfaces, or when deciding whether a matter is worth pursuing and how.

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。

Quarterly re-scan of a prior PESTEL analysis. Use when checking which macro factors moved, which assumptions broke, and what's new — turning PESTEL from a workshop artifact into a radar.

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。

Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Triggers on "learning exercise", "help me understand", "teach me", "why does this work", or after creating new files/modules. Do NOT use for urgent debugging, quick fixes, or when user says "just ship 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。

Detects anemic domain models, validates and refactors them into rich domain models, and enforces tactical DDD patterns (Entities, Value Objects, Aggregates, Domain Services, Domain Events). Use when the user asks to validate, review, or check domain models or DDD code; detect anemia; refactor domain objects; improve encapsulation; or mentions terms like "anemic model", "rich domain", "aggregate", "value object", "domain event", "ubiquitous language", "is this good DDD", "does this follow DDD", or "check my domain". Do NOT use for module or service boundary design, architectural decomposition, strategic DDD context mapping, or code outside the domain layer (DTOs, controllers, infrastructure adapters).

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。

Mandatory iPolloWork code-change gate for modern, minimal, performant, reuse-first implementation and clean repository ownership. Use whenever AI creates, edits, deletes, or refactors application code, server code, packages, scripts, tests, dependencies, schemas, routes, UI, or generated-file workflows. Reuse existing code before creating files, keep one source of truth, prevent parallel implementations and junk directories, justify every new file or dependency, and audit the current change before completion.

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, integrate, or audit production-ready rigged 3D characters and monsters with a main model, skeleton, animation library, sockets, collision contracts, separate character equipment, optimized runtime files, catalog media, and an interactive moveset inspector. Use when generating or importing FBX/GLB game actors; defining character-versus-monster asset requirements; separating swords, shields, helmets, armor, or stowed gear; adding idle, walk, run, attack, hit, dodge, block, cast, or death clips; wiring an asset into gameplay; or adding action and equipment controls plus deterministic tests to a game asset catalog.

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 git as a safety net - create a checkpoint commit before risky or large changes and roll back cleanly if a change makes things worse. Use before multi-file refactors.

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 Nexent requirements and bugfixes through SPEC acceptance, traditional UT and applicable D1-D5 test design, product/test implementation, local verification, and delivery evidence. Use for features, fixes, refactors and post-development test backfill across APIs, UI, model and Agent behavior.

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。

Safely refactors C#/.NET code without changing behavior. USE FOR any request to refactor, rename, move, extract, inline, merge, consolidate, deduplicate, split, or modernize C# code, including partial/generated declarations, wrappers, public APIs, serialization/reflection/configuration names, friend assemblies, conditional compilation, and multi-targeted projects. Also use when a request calls a feature, bug fix, package/framework upgrade, or public nullability change a refactor and the behavior-changing part must be separated or declined. DO NOT USE FOR ordinary feature or bug fixes not presented as refactoring; standalone upgrades after reclassification (use dotnet-upgrade); adding tests; or formatting-only work.

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。

Review code that performs git or gh operations against repository checkouts in gh-aw, checking that the right credentials are available at the right time and that sparseness, shallowness and credential-free factors are properly considered.

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。

Okta-as-IdP red-team attack chain — tenant discovery, user enumeration (multiple vectors), authentication flow analysis (factors enumeration, push-notification fatigue, SMS bypass), password spray with lockout discipline, Okta-specific phishing primitives (kits, FastPass abuse, OIDC redirect_uri tampering), MFA enumeration, post-compromise admin API surface. Many enterprise orgs use Okta instead of (or alongside) Entra ID. Distinct endpoints, distinct rate-limiting, distinct factor flows. Use when recon shows `<tenant>.okta.com`, `<tenant>.okta-emea.com`, `<tenant>.oktapreview.com`, or autodiscover-style records pointing at Okta IdP.

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。

Write a blameless incident postmortem under postmortems/ following the Google SRE shape — evidence-based timeline, trigger vs root cause vs symptom, contributing factors, what went well, and owned+dated+verifiable action items. Read when asked to write a postmortem, do an incident review, run a root cause analysis, write up the outage, retro on the outage, or when the user says we had an incident and wants it documented. Do NOT read to frame a proposal (use frame-a-proposal), write a spec (use write-a-spec), record a decision (use record-a-decision), or review a design (use review-a-design) — a postmortem documents an incident that already happened, it does not propose, specify, decide, or critique future work.

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。

When your CPA spikes, Claude breaks down exactly what caused it. It looks across your campaign data and isolates the contributing factors — audience fatigue, bid landscape shifts, creative decay, landing page conversion drops, budget distribution changes, or new competitor activity. Platform: Google and Meta.

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。

Decompose large tasks across parallel agents — use for migrations, multi-file refactors, or batch work

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 Experience Optimization (SXO), the bridge between SEO and UX/CRO. Audits the journey from the SERP click to the on-page goal: click-through factors (title, meta, rich results), then post-click signals that keep users and drive conversions: above-the-fold relevance and intent match, page speed / Core Web Vitals, readability, clear CTAs, mobile usability, and what causes pogo-sticking back to Google. Use when the user has decent rankings but poor CTR or poor on-page conversion, wants better dwell time / engagement, less bounce, or a page experience aligned with search intent. Trigger on: "SXO", "search experience optimization", "good rankings but low CTR", "high bounce rate from search", "pogo-sticking", "improve dwell time", "SEO and conversion", "ranking but not converting", "engagement signals", "make this page convert search traffic". For pure landing-page conversion in ads use /google-ads-landing; for content quality use /seo-page.

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-page SEO audit: content quality under Google's E-E-A-T framework and Helpful Content guidelines, on-page factors, search intent, technical signals and readability. Pulls GSC data for the page, crawls the live HTML, checks metadata, schema, internal linking and depth, and produces a scored report with fixes. Use whenever the user wants to analyze a specific page or URL, not the whole site. Trigger on: "analyze this page", "audit this URL", "how is this page doing", "evaluate my blog post", "check this landing page", "page SEO", "content quality check", "is this page good enough", "review this page's SEO", "what's wrong with this page", "how can I improve this page", "page analysis", "single page audit", "content audit for [URL]", or any request naming a specific URL/page for SEO evaluation. If the user provides a specific URL (not just a domain), this is likely the right skill; use /seo-analysis for full-site audits instead.

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。

Local SEO and Google Business Profile audit: diagnose why a business isn't ranking in the local pack / map results and produce a fix plan. Covers GBP completeness, NAP (name/address/phone) consistency across the site and citations, local pack and "near me" ranking factors, review velocity and responses, local landing pages, service-area pages, and LocalBusiness JSON-LD. Use whenever the user asks about local rankings, map results, Google Maps ranking, NAP consistency, store/branch pages, or multi-location SEO. Trigger on: "local SEO", "Google Business Profile", "GBP audit", "rank on Google Maps", "local pack", "map pack", "near me ranking", "NAP", "local citations", "my business isn't on the map", "why don't I show up on Google Maps", "store locator SEO", "multi-location SEO", "service area pages", or any location-based ranking question. For full-site (non-local) audits use /seo-analysis; for a single URL use /seo-page.

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。