「rag」の検索結果

Assesses whether study results are trustworthy by auditing design integrity, sample structure, statistical handling, bias control, validation chain, and claim discipline. It identifies where results are robust, fragile, overfit, under-validated, or overclaimed. Always separate reported findings from reliability judgment. Never fabricate references, PMIDs, DOIs, trial identifiers, study features, or validation claims.

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。

Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".

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。

Acknowledges limitations in sample, design, measurement, and validation in a professional way that improves credibility without undermining the whole paper. Use when writing the limitations paragraph of a Discussion section, preparing a grant risk assessment, responding to reviewers about study weaknesses, or framing scope boundaries for a paper. Also triggers on "write my limitations", "how should I address the limitation of", "reviewer said my sample is too small", or "help me word this limitation".

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 provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.

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。

Fail closed on published draft XQUIC code pull requests before ready-for-review state. Use when asked to pre-review a code PR or move it out of draft; verify current base-to-head code, protocol conformance, validation coverage, adversarial bad cases, hot-path performance, and memory safety in a five-part local report.

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。

Emulated Vercel REST API for local development and testing. Use when the user needs to interact with Vercel API endpoints locally, test Vercel integrations, emulate projects/deployments/domains, set up Vercel OAuth flows, manage environment variables, create API keys, configure protection bypass, emulate Vercel Blob storage, or test without hitting the real Vercel API. Triggers include "Vercel API", "emulate Vercel", "mock Vercel", "test Vercel OAuth", "Vercel integration", "Vercel Blob", "local Vercel", or any task requiring a local Vercel API.

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 evaluating A-share short-term technical setups through Tang Nengtong's classic TA lens: volume-price relationships, moving average systems, breakout signals, and short-term momentum entry/exit.

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 evaluating Mark Minervini-style VCP setups: volatility contraction, tight price action, moving-average alignment, relative strength, breakout trigger, and structural stop placement.

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 evaluating crypto decisions through a Cobie-style cycle filter: common-sense risk, narrative traps, leverage humility, and avoiding obvious stupid trades.

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 evaluating crypto charts through an Hsaka-style technical lens: market structure, trend, invalidation, leverage discipline, and narrative-aware timing.

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 this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration

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 this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

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 asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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 workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to balance work distribution, improve parallel efficiency, reduce stragglers, implement load balancing, or optimize task scheduling. Covers static/dynamic partitioning, work stealing, and adaptive load balancing strategies.

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。

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

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。

Choose placements that preserve useful residual capacity. Use for bin packing, GPU sharing, accelerator placement, and multi-resource scheduling where stranded capacity hurts future fit.

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。

Curated bank of interview problems organized by company, pattern, and difficulty. Provides problem recommendations, coverage tracking, weak area identification, and premium problem alternatives for FAANG interview preparation.

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。

Milvus distributed vector database configuration for large-scale RAG applications

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。

Batch embedding generation with caching, rate limiting, and multiple provider support

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。

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

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。

LlamaIndex agent and query engine setup for RAG-powered agents

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。

Pinecone vector database setup, configuration, and operations for RAG applications

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。

Cross-encoder reranking and MMR diversity filtering for improved retrieval quality

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。

Query expansion, HyDE, and multi-query generation for improved retrieval

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。