'code mode' 검색 결과

View and configure status displays for Codex CLI and OMX workflows. Show the model, context usage, active modes, worker counts, and turns, with compact or detailed presets, live updates, and JSON output for scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check active modes and worker counts
• Monitor workflow status with live updates
• Choose compact or detailed status displays

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

Analyze a codebase to identify valuable assets, attack entry points, and realistic abuse paths. Produce a concise Markdown threat model that explains risk priorities, distinguishes existing protections from proposed mitigations, and makes assumptions explicit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Map attack entry points in a codebase
• Assess threats within a specific project directory
• Prioritize risks to sensitive data and credentials

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Create, update, or reuse a repository threat model: a document describing system architecture, attacker capabilities, trust boundaries, and potential security failures. Produce a Markdown model grounded in source evidence for standalone use or a security scan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a threat model for a repository
• Updating a model after architecture changes
• Preparing a model for a security scan

Analyze git history to map contributors to security-sensitive files and identify ownership gaps. Estimate bus factor, a measure of dependence on few people, group files that change together, and export CSV or JSON for further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding sensitive code with stale, concentrated ownership
• Checking reliance on one person for authentication code
• Comparing CODEOWNERS with contribution history

Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking supported locations for rule files
• Understanding when file-specific rules are applied
• Configuring rule modes and text limits

Run an independent code review using the OpenAI Codex CLI in headless mode. Gets a second opinion from a different model family (the current Codex models) on recent changes, a PR, a commit, or the whole app — covering bugs, regressions, security, data consistency, UX/state bugs, performance risks, and testing gaps. Saves a severity-prioritised report to .jez/reviews/. Triggers: 'codex review', 'review with codex', 'independent code review', 'what does codex think', 'get codex to review'.

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.

Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ponytail의 세 가지 실행 모드와 관련 스킬, 호출 명령을 한눈에 확인합니다. 중지와 재개, 기본 모드 설정 및 업데이트 방법도 찾아볼 수 있습니다.

• 세 가지 실행 모드를 비교할 때
• 관련 스킬의 호출 명령을 확인할 때
• 중지와 재개 방법을 확인할 때

Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking supported locations for rule files
• Understanding when file-specific rules are applied
• Configuring rule modes and text limits

Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning product requirements into development tasks
• Coordinating implementation, testing, and reviews
• Resuming extended work from saved progress

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Find current official documentation for OpenAI products and APIs, then get concise guidance with source citations. Use it to check implementation approaches, model capabilities, and limits, with code examples grounded in the documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check how to implement features with Responses API
• Look up Codex usage
• Explore how to build ChatGPT apps with Apps SDK

Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Studying language model architecture through readable code
• Fine-tuning a model on custom instruction data
• Configuring LoRA training for a single GPU

Have a different AI model review code, analysis, or documents against the original skill's stated requirements. Receive evidence-based findings and a comparison of agreement and disagreement, then decide which recommendations to accept.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Getting a second opinion on substantial code changes
• Reviewing authentication and other security-sensitive changes
• Checking work before a bulk operation

Switch a NanoClaw agent group to a local Ollama model by configuring environment variables and model settings. The skill covers setup checks, optional blocking of Anthropic API access, verification, troubleshooting, and switching back to Claude.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a NanoClaw agent with a local model
• Reducing use of the Anthropic API
• Trying open-weight models through Ollama

Test Qwen Code CLI behavior from model requests through tool execution. Reproduce bugs, check terminal interactions and MCP tool connections, and inspect API logs using real models or mock servers for controlled scenarios.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reproduce reported CLI bugs and verify fixes
• Test MCP tool connections through the CLI
• Check terminal rendering and keyboard interactions

Analyze a codebase to identify valuable assets, attack entry points, and realistic abuse paths. Produce a concise Markdown threat model that explains risk priorities, distinguishes existing protections from proposed mitigations, and makes assumptions explicit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Map attack entry points in a codebase
• Assess threats within a specific project directory
• Prioritize risks to sensitive data and credentials

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Send a test prompt to Gemini
• Call Llama or DeepSeek with the OpenAI SDK
• Write code to call an existing custom endpoint

Configure agents, tools, app deployments, and scheduled work in Kortix. This reference explains project files, command-line operations, permissions, and the differences between pi and OpenCode, helping agents follow the platform's configuration and change-review process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure agents, skills, and custom tools
• Understand pi and OpenCode configuration differences
• Find procedures for deploying a Kortix App

Create, update, or reuse a repository threat model: a document describing its architecture, attacker capabilities, trust boundaries, and potential security failures. The result is a Markdown reference grounded in the repository's actual code and scope.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Documenting architecture and trust boundaries
• Creating or updating a repository threat model
• Preparing a threat model for a specific security review

Build remote MCP servers, which let AI agents call external tools, on Cloudflare Workers. This skill covers server code, tool definitions, OAuth authentication, local testing, deployment, and connecting a Codex client.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a remote MCP server
• Adding OAuth authentication to MCP tools
• Exposing external APIs as tools for AI agents