3570 个 · 按相关度排序
Use for Nauthilus Redis client, Redis Cluster/Sentinel/topology behavior, Redis-backed token/session/flow/cache/bruteforce state, Lua Redis scripts, `NOSCRIPT` recovery, script upload/retry behavior, redismock tests, local cache coordination, and fail-closed runtime state changes.
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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.
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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.
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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.
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Use when working with Dynamodb — amazon DynamoDB table analysis, capacity mode evaluation, GSI/LSI usage, item access patterns, and cost optimization.
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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.
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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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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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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.
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Recognize when disagreements reveal valuable context, preserve multiple valid approaches instead of forcing premature resolution
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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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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.
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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.
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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.
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Rank multiple design solutions and explain why. Separate personal preference from quality judgment. Use when comparing design options, evaluating competing approaches, or making design decisions with clear reasoning.
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Animation as communication. Feedback, orientation, emphasis, delight. If motion doesn't serve one of these four purposes, it shouldn't exist. Use when evaluating animation quality, designing transitions, or deciding whether motion adds or subtracts.
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Palette roles, restraint, semantic consistency, and when fewer colors means better design. Use when building color palettes, evaluating color usage, or diagnosing color-related design problems.
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Micro-decision analysis. Catalog the border radii, spacing scales, type sizes, and state coverage that most people miss. Use when auditing design systems, evaluating craft quality, or training granular perception.
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Use taste as a competitive moat and business advantage. In the AI and vibe-coding era, execution is commoditized. Taste is the defensible edge. Use when advising founders on product differentiation, building product culture, evaluating why some products win despite fewer features, or understanding taste as a strategic asset.
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Density, whitespace, rhythm, and how space communicates relationships between elements. Use when building layouts, evaluating spacing consistency, or diagnosing why a design feels cramped or empty.
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Visual weight, attention order, and the decisions that guide the eye. Use when building layouts, evaluating information architecture, or diagnosing why a design feels confusing.
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Name what was prioritized, what was sacrificed, and whether the tradeoff was right. Every design decision trades something. Use when evaluating design decisions, defending choices, or helping teams understand what they're giving up.
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Read a designer's taste through their typography decisions alone. Type is the fastest taste signal in any design. Use when evaluating design maturity, assessing portfolio quality, or understanding what typography reveals about design thinking.
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