'code models' 검색 결과

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

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• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

CodexBar의 로컬 비용 로그에서 Codex나 Claude의 비용을 모델별로 집계하고, 최근 대표 모델이나 전체 모델의 내역을 텍스트 또는 JSON으로 확인합니다.

• 최근 대표 모델의 비용을 확인합니다
• 전체 모델의 사용 비용을 비교합니다
• 내보낸 비용 로그를 집계합니다

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Get guidance on Codex, OpenAI APIs, and ChatGPT Work using official documentation with citations. Use it to investigate settings and errors, compare models, plan migrations, or work on prompts and API integrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check Codex settings and troubleshoot errors
• Compare models for a particular task
• Plan a model migration and prompt changes

Review estimated spending, token usage, and cache hit ratios across Claude Code, Codex, and Grok using local logs. Filter by time, project, or provider, while keeping currencies separate and identifying models without pricing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing estimated spending for the past seven days
• Comparing usage across providers and models
• Checking costs for a specific project or time range

Search local session histories across Codex, Claude Code, OpenCode, and other coding agents. Find earlier requests, inspect transcripts and child sessions, and reconstruct past work using recorded evidence, with filters for dates, projects, platforms, and models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding work done a few days ago
• Checking whether a migration was already completed
• Reviewing tasks delegated to child agents

Add OpenCode as an agent runtime in NanoClaw to use models from OpenAI, Google, OpenRouter, DeepSeek, and other supported services. The skill covers installation, authentication, group selection, and default model changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding OpenCode to an existing NanoClaw installation
• Switching an agent group to OpenCode
• Changing the default model without signing in again

Configure coding agents, AI SDKs, and HTTP clients to send model requests through AntSeed’s local proxy. Find available providers and models, select a provider, set compatible API endpoints, and verify request usage and payment activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect Claude Code or Codex to AntSeed
• Configure a local endpoint for AI SDKs
• Compare providers’ models, protocols, and prices

Find guidance on Codex settings, pricing, and OpenAI products and APIs using current official documentation. Get cited answers for setup, troubleshooting, model comparisons, migration planning, and prompt changes, with uncertainty stated when sources are inconclusive.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check Codex settings and troubleshoot problems
• Compare models for cost, speed, and quality
• Plan a model migration and adjust prompts

Get source-backed guidance on OpenAI products, APIs, and Codex from official documentation. Use it to compare models for a task, understand configuration options, or plan focused model and prompt updates while preserving an explicitly requested target model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking OpenAI API usage and parameters
• Choosing an OpenAI model for a specific task
• Planning model upgrades and related prompt changes

Scans code for security vulnerabilities with CodeQL by tracing data across functions. Builds and checks analysis databases, models project-specific APIs, and saves findings in SARIF, a structured results format, with broad or focused scan options.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Scanning a codebase for security vulnerabilities
• Tracing untrusted input through custom APIs
• Choosing broad or high-precision security scans

Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze Claude Code and Codex usage logs to identify spending drivers, including cache usage, model choices, reasoning, and long sessions. Receive evidence-backed recommendations and hypothetical savings estimates to help decide which changes to try.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating unexpectedly high agent costs
• Reviewing cache usage
• Reconsidering models used by sub-agents

Turn 3D models into printer instructions called G-code with OrcaSlicer. The skill selects existing printer and material presets, slices the model, and checks dimensions and output settings before handing over the files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare an STL model for a specific printer
• Slice models using existing material presets
• Check model size and print settings

Provides Codex CLI delegation workflows for complex code generation and development tasks using OpenAI's GPT-5.3-codex models, including English prompt formulation, execution flags, sandbox modes, and safe result handling. Use when the user explicitly asks to use Codex for complex programming tasks such as code generation, refactoring, or architectural analysis. Triggers on "use codex", "delegate to codex", "run codex cli", "ask codex", "codex exec", "codex review".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Combine code from local directories or GitHub into one structured file for AI analysis. Examine project structure, search for related code, and check estimated tokens, the units AI models use to process text.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a single codebase file for AI analysis
• Reviewing a project's directory structure
• Finding authentication code and database models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: "codex", "use gpt", "gpt-5", "let openai", "full-auto", "adversarial review", "second opinion review", "用codex", "让gpt实现", "对抗式审查", "让codex审查计划", "第二意见". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

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• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

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'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate a complete, beginner-friendly explanation of an entire code repository as a set of markdown files. Maps the repo structure with `treecap` (installing it if missing), studies the actual source, then writes a `CODE_EXPLANATION/` folder containing an `OVERVIEW.md` plus numbered deep-dive files (01_architecture.md, 02_models.md, ...) that explain how the code is built, what it does, and how the pieces fit together — illustrated with real code excerpts. Use this skill whenever the user wants to understand, document, onboard onto, or get a walkthrough of a codebase: phrases like "explain this repo", "explain the code", "code explanation", "walk me through this codebase", "help me understand how this is built", "document the architecture", or "what does this project do" — even if they don't say the word "skill". Prefer this over ad-hoc summaries whenever the goal is a durable, written explanation of a whole repo.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.