2,109 件 ・ 関連度順
概要と使いどころ
Use when creating or editing a skill in this offensive-claude repo — for the SKILL.md conventions (trigger descriptions, technique map, runnable scripts, OPSEC/detection, red-flags tables, flowchart 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。
Generate an interactive PR review walkthrough as an HTML page. Fetches PR data via gh API, categorizes files into core vs mechanical changes, adds reviewer annotations, and renders diffs with moved-code detection. Use when the user pastes a GitHub PR URL and asks for a review, walkthrough, or summary, or says "review this PR".
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 analyzing an existing TypeScript or JavaScript codebase to decide where and how to introduce Inngest. Covers repository discovery, framework and package detection, finding durability gaps in HTTP handlers, webhooks, cron jobs, queues, long-running jobs, AI agents, Agent Evals, polling loops, eval loops, and side-effect-heavy code, then producing and implementing an incremental integration plan.
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。
Structure-activity relationship (SAR) analysis guide for drug discovery including molecular descriptor analysis, scaffold analysis, and activity cliff detection.
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。
Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats. Use when given a data file for initial exploration or to pick an analysis before a pipeline.
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。
Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.
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。
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.
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。
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
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。
Protocols and best practices for western blot quantification and analysis including band detection, normalization, and statistical methods.
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。
Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.
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。
Remove the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".
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。
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
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。
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
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。
Analyze BIM clash detection results and suggest resolutions. Prioritize clashes, identify patterns, assign responsibility, and track resolution status.
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。
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
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。
Detect and map data silos in construction organizations. Identify disconnected data sources and integration opportunities
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。
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
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。
Structured deal assessment using MEDDIC, BANT. Risk scoring, required evidence by stage, red flag detection, coaching points.
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。
Detects AI-generated writing patterns in prose. Use when reviewing docs for slop, vague language, or identity leaks before publishing.
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。
Updates documentation after code changes with quality gates, slop detection, and accuracy checks. Use when code changes require corresponding doc updates.
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。
Detects workflow failures and inefficient patterns then files GitHub issues. Use when a workflow step repeatedly fails or produces inconsistent output.
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。
Provides sem semantic-diff detection, install-on-first-use, and fallback patterns. Use when building skills that consume git diff output.
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。
Detects architectural clusters and coupling boundaries via community detection on the code graph. Use when identifying module groupings or refactoring targets.
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 you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
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。