「eval」の検索結果

Use when working with Aws Iam — aWS IAM deep analysis for policy evaluation, role trust relationship review, access advisor data, credential reports, permission boundary inspection, and identity-based vs resource-based policy analysis. Covers IAM users, roles, groups, policies, SCPs, and cross-account access patterns. Read this skill before any IAM operations — it enforces discovery-first patterns and strict read-only safety rules.

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Use when working with Aws S3 — aWS S3 bucket analysis, storage class distribution, lifecycle policies, access patterns, and cost optimization. Covers bucket inventory, object metrics, versioning status, encryption audit, public access analysis, and intelligent tiering evaluation.

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 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.

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Use when working with Checkov — checkov infrastructure-as-code security scanning. Covers Terraform, CloudFormation, Kubernetes, and Dockerfile scanning, policy management, custom checks, compliance frameworks, and suppression management. Use when scanning IaC for security misconfigurations, evaluating compliance, managing custom policies, or reviewing scan results.

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 Dynamodb — amazon DynamoDB table analysis, capacity mode evaluation, GSI/LSI usage, item access patterns, and cost optimization.

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 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.

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。

FutureSearch: Search the Future — AI forecasting with a public track record. First in Metaculus's Summer 2026 FutureEval tournament, above the superfo

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LLM) can be manipulated through prompt injection, whether a retrieval pipeline leaks confidential da

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。

Plan and prepare an OpenAmer-specific fine-tune from the collected brain data: data curation, ChatML JSONL, training, evaluation. 'The organism becomes its own brain'.

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Train an OpenAmer-specific small model from the collected brain data (QLoRA on NVIDIA, CPU smoke-test on laptop). 'The organism becomes its own brain' — build a fine-tune dataset, train, eval, export.

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Use when the agent needs recursive reasoning, episodic memory retrieval, internet learning, or world-model prediction. Mini-OpenAmer: the 2B hybrid Mamba core with 9 tools, running 24/7 on Damir's laptop.

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。

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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。

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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。

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.

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NVIDIA RAG Blueprint performance-tuning guidance for profiling retrieval stacks, comparing bottlenecks, and validating latency or throughput improvements.

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NVIDIA RAG Blueprint deployment, configuration, troubleshooting, and shutdown guidance for Docker, Helm, and library-based RAG stacks.

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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.

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Microsoft docs lookup, code samples, and SDK reference for Azure, .NET, Microsoft 365, Windows, and Power Platform via Microsoft Learn MCP. Use for API reference or official MS documentation retrieval.

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NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering workflows.

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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.

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。

Python research assistant with Context7 MCP. Use for Python library research, evaluating packages, enforcing strict Python coding standards, or fetching up-to-date library docs via Context7.

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。

Guide for optimizing MSBuild build parallelism and multi-project scheduling. Only activate in MSBuild/.NET build context. USE FOR: builds not utilizing all CPU cores, speeding up multi-project solutions, evaluating graph build mode (/graph), build time not improving with -m flag, understanding project dependency topology. Note: /maxcpucount default is 1 (sequential) — always use -m for parallel builds. Covers /maxcpucount, graph build for better scheduling and isolation, BuildInParallel on MSBuild task, reducing unnecessary ProjectReferences, solution filters (.slnf) for building subsets. DO NOT USE FOR: single-project builds, incremental build issues (use incremental-build), compilation slowness within a project (use build-perf-diagnostics), non-MSBuild build systems. INVOKES: binlog MCP server tools (expensive_projects, expensive_targets, project_target_times); falls back to dotnet build -m, dotnet build /graph, binlog replay + grep.

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。

Guide for organizing MSBuild infrastructure with Directory.Build.props, Directory.Build.targets, Directory.Packages.props, and Directory.Build.rsp. Only activate in MSBuild/.NET build context. USE FOR: structuring multi-project repos, centralizing build settings, implementing NuGet Central Package Management (CPM) with ManagePackageVersionsCentrally, consolidating duplicated properties across .csproj files, setting up multi-level Directory.Build hierarchy with GetPathOfFileAbove, understanding evaluation order (Directory.Build.props → SDK .props → .csproj → SDK .targets → Directory.Build.targets). Critical pitfall: $(TargetFramework) conditions in .props silently fail for single-targeting projects — must use .targets. DO NOT USE FOR: non-MSBuild build systems, migrating legacy projects to SDK-style (use msbuild-modernization), single-project solutions with no shared settings. INVOKES: no tools — pure knowledge skill.

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。

Fix MSBuild targets that generate files during the build but those files are missing from compilation or output. Only activate in MSBuild/.NET build context. USE FOR: generated source files not compiling (CS0246 for a type that should exist), custom build tasks that create files but they are invisible to subsequent targets, globs not capturing build-generated files because they expand at evaluation time before execution creates them, ensuring generated files are cleaned by the Clean target. Covers correct BeforeTargets timing (CoreCompile, BeforeBuild, AssignTargetPaths), adding to Compile/FileWrites item groups, using $(IntermediateOutputPath) instead of hardcoded obj/ paths. DO NOT USE FOR: C# source generators that already work via the Roslyn pipeline, T4 design-time generation that runs in Visual Studio, non-MSBuild build systems. INVOKES: no tools — pure knowledge skill.

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。