4,035 件 ・ 関連度順
概要と使いどころ
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).
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。
For frontend engineers authoring UI — how to make components reliably locatable by tests and AI agents. Covers the official Playwright locator priority, semantic/accessible markup first, and how to add valid, unique, stable `data-testid` attributes (naming convention, uniqueness in lists, stability rules) for React, Vue, Angular, and Svelte. Use when writing or reviewing components, or when an agent needs durable locators.
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 reverse-engineering a binary or firmware — static triage + decompilation (Ghidra/IDA/Binary Ninja), dynamic instrumentation (GDB/Frida 17/angr), anti-reversing & packer bypass, OLLVM/VM deobfuscation, UEFI/BIOS RE & Secure Boot research, patch-diffing for n-days
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 responding to or forensically investigating an incident — triage acquisition (Velociraptor/KAPE), Volatility 3 memory forensics, Chainsaw/Hayabusa EVTX timelining, anti-forensics detection, cloud IR, ransomware/ESXi response
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。
Design lens critique covering visual hierarchy, clarity, and anti-slop patterns — produces a findings table, no code edits unless asked. Use when the user wants a design review, says "what's wrong with this UI", or needs a second opinion before a handoff or presentation. Invoke when the user asks for critique on their UI, or mentions 'critique' alongside design / UI / frontend work.
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。
Dense dashboard / admin / Bloomberg / Retool / data-heavy internal tools. Locked knobs: CRAFT=7, MOTION=3, DENSITY=9. IBM Plex + mono numbers, semantic palette, 4/8px grid, sparklines, tabular-nums. Trigger on: dashboard, admin panel, data-dense, analytics, Bloomberg-like, Retool-like.
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 UI design and implementation work to avoid generic AI-looking interfaces. Provides anti-slop rules, a required discovery phase before coding, and guidance for layout, typography, color, motion, accessibility, dashboards, tables, landing pages, theming, and polish. Trigger when editing UI code or reviewing and refining components, pages, screens, layouts, animations, responsive behavior, or design systems.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Test-driven development for substantive feature, bug-fix, and behavior-changing work. Use automatically when implementing features, fixing bugs, or changing behavior (red → green → refactor). Also use when the user mentions TDD, /tdd, /TDD, or red-green-refactor. Do not use for documentation-only, formatting-only, exact generated-mirror, or provenance-only changes.
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。
Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.
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 the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
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 UI with Core's shadcn/Base UI system. Use for selecting, installing, composing, or customizing shadcn/ui, registry items, themes, and wrappers. Always preserve exact style `base-maia`, Zinc-oriented semantic CSS-variable tokens, and Base UI primitives. Do not use to switch styles, run `shadcn init`, or introduce Radix/React Aria. Pair with `packages/ui/AGENTS.md`.
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。
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
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。
HTML, CSS, and JavaScript craft guidelines from bendc/frontend-guidelines — semantics, a11y basics, selector discipline, motion-friendly CSS, and readable JS. Use for markup review, static HTML/CSS, email HTML, or general front-end hygiene; pair with repo frontend rules and TypeScript lint policy.
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。
Prepare a proportionate work plan with outcomes, steps, dependencies and acceptance evidence. Use to plan substantial work or coordinate an approved team; a plan-only request does not authorize implementation.
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。
Check reported lighting quantities and results in supplied simulation PDFs without adding repeated calculation surfaces. Use for scoped luminaire inventories and illuminance results; not simulation or compliance.
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。
Counts RNA-seq reads overlapping GTF gene features. Takes sorted STAR BAMs plus GTF; outputs a per-gene tab-delimited matrix across samples. Handles strandedness (0/1/2), paired-end, multi-sample batch counting in one command, and outputs assignment statistics. Use Salmon for alignment-free quantification; use featureCounts when STAR BAMs already exist.
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。
Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
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。
API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.
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 library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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 Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
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。