「model debugging」の検索結果

Use detrex for detection-transformer configs, training/evaluation, demos, model zoo conversion, and package API debugging.

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。

Operate Core ML Tools workflows for model conversion, Core ML artifact I/O, optimization, MIL debugging, and platform-aware troubleshooting.

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。

Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.

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。

Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.

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 MiniMax's first-party Hailuo video API safely and reproducibly across global and mainland-China platforms. Covers current text-to-video, image-to-video, first/last-frame, and subject-reference models; prompt and camera craft; region/account routing; exact cost approval; asynchronous jobs and callbacks; artifact provenance; and rights, likeness, disclosure, and data-governance review. Use when designing, implementing, debugging, or evaluating direct MiniMax/Hailuo video-generation API workflows. Do not use for the consumer Hailuo site, Video Agent templates, gateways, or unrelated MiniMax modalities.

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。

Operate Stability AI image generation, image-to-image, edit, control, background, and upscale APIs safely and reproducibly. Use when selecting or calling Stable Image Ultra, Core, Stable Diffusion 3.5, v2beta image edit/control services, or Stability open-weight image models; when debugging schemas, moderation, rate limits, cost, lifecycle, licenses, privacy, provenance, or migrations; and when planning production QA for Stability-generated images.

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。

Build and trace AI agents in Python with AgentScope, an open-source agent framework with a ReAct loop, tools, middleware, team pipelines and OpenTelemetry tracing. Use when building production agents that need observability, debugging an agent's model and tool calls, capping token spend per reply, or coordinating a leader with specialist agents.

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 this skill for any Python backend work in this project: building FastAPI endpoints, writing service functions, defining Pydantic/SQLModel schemas, running Alembic migrations, or debugging 422 errors. Essential for authentication and authorization patterns — setting up get_current_user, is_superuser checks, admin-only guards, role-based access, and dependency injection chains like Depends(). Also covers middleware, background tasks, async SQLAlchemy sessions, ORM relationship loading, and request/response design. Activate whenever the question involves Python API code, FastAPI patterns, or backend architecture in this codebase. Not for frontend, Docker, CI/CD, or infrastructure.

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。

A harness that makes Opus (or any Claude model) behave like Fable — it enforces seeing a task through to the end, with evidence and verification, as procedure. Use when starting a multi-step task (2+ sequential stories), long autonomous work, debugging or root-cause investigation, building render/executable artifacts (HTML, SVG, games, charts), or when the user says "fablize", "see it through", "verify as you go", "split into goals".

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。

Craft CMS 5 plugin and module development — extending Craft with PHP. Covers elements, element queries, services, models, records, controllers, migrations, queue jobs, console commands, field types, native fields, events, behaviors, Twig extensions, widgets, filesystems, permissions, project config, GraphQL, testing, and debugging.

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。

Claude Code subagent runtime and plugin-agent reference. Use when creating, configuring, or debugging subagent definitions, nested spawning, startup context, model precedence, or plugin field restrictions.

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。

Integrate MCP servers into Claude Code plugins — covers .mcp.json configuration, plugin.json mcpServers field, server types (stdio, SSE, HTTP, WebSocket), environment variable expansion, tool naming conventions, OAuth and token authentication, security best practices, and testing workflows. Use when adding an MCP server to a plugin, configuring MCP authentication, debugging MCP tool discovery, setting up Model Context Protocol integration, or choosing between stdio and SSE transport types.

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 whenever the user wants to find, shortlist, vet, or enrich US software development firms — custom software, web development, mobile app development, backend/API development, DevOps/cloud, system integration, and hosting. Triggers on "find a software dev shop in Austin", "shortlist three custom-software firms with healthcare experience", "we need a mobile app developer for our iOS launch", or "pull contact info for these 10 dev shop domains", even when described indirectly (build a tool, ship a feature, technical partner). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-web-developer for strictly website/landing-page projects. Defer AI/ML, ML pipelines, model building, and data-engineering asks — those are a sibling industry, not software development. Skip in-house engineer hires, code-writing/debugging tasks, cloud-product comparisons, hardware/civil engineering, non-US firms, individual freelancers.

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。

Odoo engineering workflows for addon development, codebase exploration, debugging, architecture/refactor review, manifest/docs sync, and routing to migration work. Use when the user mentions Odoo, addons, modules, manifests, models, XML views, security CSVs, record rules, Odoo shell, OWL/QWeb/assets, or when an Odoo codebase is detected.

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。

Trace Odoo execution flow from an entry point through controllers, button actions, cron jobs, model methods, overrides, computes, onchanges, constraints, database operations, security checks, and side effects. Use before implementation, during debugging, impact analysis, performance review, or code review when behavior depends on Odoo call chains.

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。

Odoo OWL frontend engineering guidance covering components, templates, reactivity, hooks, props, plugins, registries, debugging, assets, and OWL 1/2/3 migration. Use when working with Odoo frontend components, @odoo/owl, static/src JavaScript/XML templates, signals, proxy, props, t-on, t-ref, t-model, registries, plugins, or OWL migrations.

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 Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions, multi-tab/popup flows, mobile/responsive layouts, touch gestures, GraphQL, error handling, offline mode, multi-user collaboration, third-party services (payments, email verification), console error monitoring, global setup/teardown, test annotations (skip, fixme, slow), test tags (@smoke, @fast, @critical, filtering with --grep), project dependencies, security testing (XSS, CSRF, auth), performance budgets (Web Vitals, Lighthouse), iframes, component testing, canvas/WebGL, service workers/PWA, test coverage, i18n/localization, Electron apps, or browser extension testing. Covers E2E, component, API, visual, accessibility, security, Electron, and extension testing.

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 should be used to spawn specialized OpenAI Codex CLI subagents for code review, debugging, architecture analysis, security audits, refactoring, documentation, comparative evidence adjudication, and autonomous /goal runs. It adds each persona through process-local developer instructions while preserving the project's AGENTS.md chain, including during parallel launches. Supports GPT-6-Astra across low, medium, high, xhigh, max, and ultra reasoning with default or priority service tiers, plus GPT-5.6 and GPT-5.5 models. Triggers on 'delegate to Codex', 'Codex Astra', 'Astra subagent', 'Codex subagent', 'code review agent', 'security audit', 'refactor with Codex', 'goal run', 'autonomous goal', 'have Codex weigh this'.

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 cutting, debugging, or verifying a pythinker-code (TSC monorepo) release — changesets flow, the ci release-packages version PR, release.yml anatomy, npm Trusted Publishing OIDC, native assets, brew tap, CDN redeploy, and their failure modes. Invoked by /release.

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 building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.

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。

Evaluate LLM agents and tool-using workflows—task success, tool accuracy, latency/cost, safety, and regression suites. Use when shipping agent features, comparing prompts/models, or debugging agent failures.

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 creating, maintaining, debugging, or reviewing real-browser end-to-end tests with Playwright or Cypress, including Page Object models, CI artifacts, traces, flaky tests, cross-page visual regression, and critical user journeys such as login, payment, permissions, or CRUD. For layer planning or tests close to UI components, choose the matching testing workflow first; Chinese triggers include E2E, end-to-end testing, Playwright, Cypress.

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 creating, configuring, reviewing, or debugging TypeScript project standards across frontend apps, libraries, SDKs, CLIs, monorepo packages, tsconfig, strictness, module/moduleResolution, path aliases, project references, declaration files, package exports, public API types, DTOs, advanced generics, discriminated unions, type guards, type narrowing, or type-level regressions. Prefer framework project skills for React/Vue/Next/Nuxt component architecture; Chinese triggers include TypeScript project specification, TS project specification, TypeScript type safety, type modeling, generics, discriminative unions, type narrowing, tsconfig, declaration 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。

Use when testing PyQt/PySide6 applications with pytest-qt - qtbot fixture, signal/wait patterns, mouse/keyboard simulation, dialog testing, model/view testing, threaded code testing, or manual debugging techniques

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。