1.3천건 · 관련도순
Use when working with Aws Config — aWS Config compliance dashboard, rule evaluation status, conformance pack management, and resource timeline analysis. Covers configuration recorder status, compliance summary, non-compliant resource investigation, remediation tracking, and aggregator management.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Prowler — prowler cloud security assessment tool. Covers AWS, Azure, and GCP security posture assessment, CIS benchmark evaluation, compliance frameworks (PCI-DSS, HIPAA, SOC2), multi-account scanning, and remediation guidance. Use when assessing cloud security posture, running compliance audits, or investigating security findings across cloud providers.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build high-quality MCP servers with strong tool design, structured outputs, clear error handling, and realistic evaluations. Use when creating or improving MCP servers in TypeScript or Python for external APIs, services, or internal platforms.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
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.
Expert en évaluation d'agents IA (benchmarking, human eval, latence, coût, précision, safety testing)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematic design quality evaluation. Hierarchy, type, color, space, craft, system. Use when evaluating whether a design is ready to ship, running quality audits, or setting quality standards.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Understand the constraints a design was built under and evaluate quality within that context. Use when reviewing work that had limited time, budget, or team size, or when assessing whether constraints were handled well.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
LLM Agent engineering: pattern selection (12-mode matrix), data simulation, convergence iteration. Covers intent routing, function calling, ReAct, MCP, prompt chaining, guardrails, evaluation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit and improve SwiftUI runtime performance. Use when diagnosing slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a 3-leg evaluation against a newly released LLM. Leg 1 is trap questions with deterministic answers, leg 2 is an agentic orchestration task with a planted failure, leg 3 is a brownfield feature build in a real repo. Use when the user wants to vibe-check, test, evaluate, or compare a new frontier model, or mentions model-bench, model vibe check, or launch-day protocol.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze a job description, hiring post, or recruitment page for role requirements, evaluation criteria, keywords, risks, and resume or cover letter angles. Use when the user provides a JD URL/text, asks what a company is looking for, wants JD-resume gap analysis, or needs inputs for a tailored cover letter. 한국어 요청도 포함합니다 - 채용공고 분석, 공고 분석, JD 분석, 직무기술서 분석, 자격요건, 우대사항, 이 공고 뭘 원하는지, 공고랑 내 경력 비교.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide for using completers — TokenCompleter and MessageCompleter for text generation during RL rollouts and evaluation. Use when the user asks about generating text, completing messages, or using completers in RL environments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when: 'intake this repo/skill', 'evaluate external skill', 'should we adopt X', a candidate repo URL plus an adoption question, or any external skill/workflow/tool surfaces as a possible fit for Agistra. Structured front-door evaluation of external skills, workflows, and similar capability artifacts before any install, promotion, or adaptation decision. Ends in exactly one classification: Install candidate, Adapt concept, or Reject. Never install first — intake first.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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