「code models」の検索結果

Reviews FastAPI code for routing patterns, dependency injection, validation, and async handlers. Use when reviewing FastAPI apps, checking APIRouter setup, Depends() usage, or response models.

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。

Discoverability shim for the Retail-Agentic-Commerce backend feature Codex skill. Use when writing or modifying Python backend code, routes, services, models, tests, or protocol logic.

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。

Expert guide for converting UI screenshots, mockups, Figma frames, and whiteboard sketches into production-ready frontend code using vision-capable frontier models (Gemini 4 Pro, Claude 5.5, GPT Astra 6) — multi-pass visual extraction, component decomposition, responsive generation, and design token mapping / Panduan ahli konversi screenshot UI, mockup, frame Figma, dan sketsa whiteboard menjadi kode frontend siap produksi menggunakan model frontier berkemampuan vision — ekstraksi visual multi-pass, dekomposisi komponen, generasi responsif, dan pemetaan design token.

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。

Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine 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。

Get external agent review and feedback. Routes Anthropic models through Claude Agent SDK (uses local subscription) and other models through OpenRouter API. Use for code review, architecture feedback, or any external consultation.

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。

Review Odoo addon code for correctness, security, performance, migrations, tests, and official Odoo coding guidelines. Use when reviewing Odoo modules, diffs, pull requests, or changed files involving models, fields, XML views, data, controllers, reports, OWL/assets, manifests, access rules, record rules, or OCA 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。

Run GGUF models directly, load LoRA adapters, benchmark inference speed, and serve models via llama-server using llama.cpp. Includes Qwen 3.5 serve scripts (9B dense + F16, 35B MoE) with asymmetric KV cache and thinking mode. Secondary to Ollama; use when needing direct model control or LoRA hot-loading. Triggers on 'llama.cpp', 'GGUF', 'LoRA adapter', 'benchmark inference', 'llama-server'.

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。

Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

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 durable feature spec before touching code. Reads the codebase map, reads related models/migrations to ask technical questions (not generic ones), then produces a structured spec doc in docs/specs/. Designed to pipeline with /implementation --issue N. Use before any feature implementation.

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。

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill 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。

Build structured 3-scenario financial models (Base / Bull / Bear) for business planning or investment decisions. Sensitize revenue and cost drivers across scenarios, produce P&L summaries, cash flow bridges, and executive narratives. Aligns with FP&A 2.0 and CIMA Management Accounting standards. Use when leadership needs to understand the financial impact range of a decision. Do not use for statutory financial reporting or audit-ready financials.

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 customer health scores and churn risk models using leading indicators. Define health dimensions (Usage, Engagement, Support, Sentiment, Contract), weight and score customers, segment into Healthy / At-Risk / Red, and generate intervention playbooks. Use during QBR prep, customer success reviews, or when churn spikes.

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 working with SQLAlchemy 2.0 in Python - declarative models, queries, sessions, async engines, Alembic migrations, relationship loading strategies, N+1 detection, pool exhaustion, or 2.0 migration

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 validating and serializing Python data with Pydantic v2 - models, field and model validators, constrained and special types, TypeAdapter, discriminated unions, BaseSettings, or FastAPI 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。

Divergent ideation across model families — the same brainstorm, but each cognitive frame is handed to a different model (Claude sub-agent, OpenAI Codex, a cheap third via OpenCode) so the ideas diverge by frame and by model priors at once. Use for open design questions, architecture and API shape, naming, schema choices, fuzzy bugs with no known root cause, or on "multi adhd", "brainstorm with everyone", "wide ideas from all models". Skip for lookups, syntax, and anything phrased as "quick", "standard" or "canonical".

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 any installed skill on several models at once — you, OpenAI Codex, OpenCode and whoever else the profile names — and show what each one made of it side by side. Every model gets the same prepared copy of the skill and works read-only. Use for "/multi:skill <name>", "run this skill on every model", "run <skill> through multi", or any skill whose answer you want from more than one model.

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。

Analyze system architecture, module structure, API contracts, data models, and code patterns. Use when designing systems, reviewing module boundaries, evaluating API designs, analyzing data models, checking pattern conformance, tracing decisions, or reviewing frontend architecture. Triggers: "architecture", "module design", "API design", "data model", "boundaries", "patterns", "decisions", "frontend architecture".

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 financial models — revenue forecasts, unit economics, scenario analysis, and investor-grade spreadsheets.

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。

Teach models new tasks from a handful of examples — prompting strategies, example selection, and in-context learning mechanics.

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。

Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

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。

Fast serverless inference on Fireworks AI — open models, fine-tuning, and function-calling optimized endpoints.

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。

Work with text embeddings — choosing models, pooling, similarity measures, evaluation, and multilingual considerations. Use when building semantic search, clustering, or any similarity-based feature.

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。

Program language models instead of prompting them — DSPy signatures, modules, and optimizers that tune prompts automatically.

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。