'opencode' 검색 결과

Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rewrite AI-generated copy in Spanish and English so it sounds direct, concrete and human while preserving meaning, facts and numbers exactly. Use when drafts feel robotic, verbose or translated and need short active sentences without em-dashes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run CodeQL database creation and security queries, add data-extension models, or process CodeQL SARIF. Use when CodeQL is explicitly requested; use security-review for a broader manual security review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create an isolated Python environment with a compatible Python version and boto3 for SageMaker or AWS automation. Use before executing AWS Python code; do not use for general Python environment setup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use the `hf` CLI for Hub authentication, downloads, uploads, repositories, cache, jobs, buckets, webhooks, and endpoint administration. Use for CLI operations, not model recommendations, paper analysis, training design, or building a Space.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agents should invoke this skill for academic or technical papers, arXiv/PubMed/IEEE/ACM links, PDFs, methodology review, limitations, practical implications, or extracting findings for engineering decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging. This is NOT a general code review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agents should invoke this skill for code reviews, linting/formatting setup, maintainability checks, complexity concerns, warning cleanup, coding standards, or quality gates in Rust, TypeScript, Python, shell, and mixed repos.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. Use when writing Semgrep rules or building custom static analysis detections.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create a repository-grounded AppSec threat model covering assets, trust boundaries, attackers, abuse paths, and mitigations. Use only for explicit threat-modeling requests, not general architecture or code review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners. Triggers on "parse sarif", "read scan results", "aggregate findings", "deduplicate alerts", or "process sarif output". Handles filtering, deduplication, format conversion, and CI/CD integration of SARIF data. Does NOT run scans — use the Semgrep or CodeQL skills for that.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide a feature implementation through a structured seven-phase workflow with deep codebase understanding, clarifying questions, parallel architecture design, and quality review. Use this skill when the user asks to build a new feature, add functionality, or wants a methodical approach to implementation rather than diving straight to code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit GitHub Actions that run AI agents for prompt injection, unsafe interpolation, sandbox gaps, and permissive actor rules. Use for agentic CI workflows, not general application code review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Systematically verifies suspected security bugs to eliminate false positives. Produces TRUE POSITIVE or FALSE POSITIVE verdicts with documented evidence for each bug.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run Semgrep static analysis across a codebase, optionally using Semgrep Pro for cross-file taint analysis. Use when Semgrep or a static-analysis scan is requested; use security-review for a manual audit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Select and verify the current region-specific serving container URI for a SageMaker model deployment. Use after the deployment pathway is chosen and before endpoint code; never infer an image URI from memory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify error-prone APIs, dangerous configuration, and footgun designs that enable security mistakes. Use for API ergonomics, misuse resistance, secure defaults, or pit-of-success reviews; use code-review for general defects.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.