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概要と使いどころ
Control FriendlyBet operating cost, free-tier viability, provider/API cost, GitHub Actions/Supabase/Vercel usage, documentation hygiene, company memory updates, playbook maintenance, and skill validation. Use when evaluating cost, operational burden, tooling, internal docs, company memory, or whether agent profiles should become full skills.
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 ambiguity resolution through tiered information gathering. Use when facing unclear requirements, unknown context, uncertain implementation choices, or any situation where guessing would be risky.
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 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.
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 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 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。
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 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 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.
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。
Build comparable-company valuation workbooks in Excel.
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。
Build discounted cash flow valuation workbooks in Excel.
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。
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
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。
We need to find the structural connection between the two described situations. The user gave two bu GAP: OpenHands has a modular SDK design — our too
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。
We need to parse user query. They ask: "Find the structural connection between these two situations:
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'.
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。
Hijacking, Red-Team Evals, and Continuous Evaluation A technical reference on agent hijacking, red-t
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.
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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
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。
Picks the right Netlify protection layer for a deployed site and disambiguates the three unrelated things people call "auth". Use when a developer wants to password-protect a site or previews, restrict a project to their team, make a project public/private, set team visibility defaults, require SSO to view a site, or debug SSO-session symptoms like being logged out mid-session / getting 401s on an SSO-protected site / token expiry or refresh. Routes app-user login ("who is this user in my app") to the netlify-identity skill and dashboard/team SSO SSO elsewhere; this skill only chooses the perimeter layer for site/preview access.
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。
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.
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。
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。
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.
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 for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python code.
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。
Use only when the user explicitly requests strict code-cell-only output for a Jupyter or undergraduate data-science task, or when an ITX2007 assignment requires that format. Do not activate for ordinary data analysis or explanation requests.
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。