“rag”的搜索结果

Orchestrate any tdmcp test-coverage work: run the coverage harness, inspect coverage gaps, plan focused Vitest/msw or bridge tests, delegate coverage writers, verify coverage deltas, and re-run gates. Use whenever the user asks to raise coverage, add broad regression coverage, create a test harness, improve tests, re-run a coverage wave, or fix a coverage gate.

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Inspect browser storage and caches in a CTF sandbox to explain changes in authentication, page rendering, or requests. Compare clean and modified states, then document the smallest sequence that reproduces the relevant behavior.

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• Investigating login behavior affected by stored browser state
• Explaining page differences caused by caches
• Reproducing behavior with clean and modified storage

Choose embedding models, which represent text as numerical vectors, for meaning-based search and RAG applications. Compare options for code, prose, and multilingual content, and plan document splitting, preprocessing, caching, and dimension reduction to improve retrieval quality.

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• Choosing an embedding model for RAG
• Reviewing document chunk size and boundaries
• Planning code or specialist document search

Analyze access logs from AWS S3, GCS, and Azure Blob Storage to spot unusual downloads, unfamiliar IP addresses, and request spikes. Compare activity with normal patterns and prioritize findings for suspected data theft investigations.

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• Investigating suspected data theft from cloud storage
• Finding bulk downloads outside normal working hours
• Checking unfamiliar IP addresses and sudden request spikes

This retirement notice directs users from the removed `$ralph` command to `$ultragoal`. It explains how the replacement's single-goal workflow covers persistent progress tracking, implementation, verification evidence, and resuming work previously handled by Ralph.

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• Update instructions that still reference $ralph
• Find the replacement for the retired workflow
• Check how the successor tracks and resumes work

Create a short launch video from the current website or app project using Hyperframes. The skill reads the code, plans scenes, adds music and optional narration, checks and renders the video, and prepares a poster and share copy.

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• Creating a launch video from the current app project
• Making a vertical video with a chosen tone
• Adding narration to a short product introduction

Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.

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• Design an AI application that answers using internal documents
• Compare vector databases and document splitting strategies
• Improve search through keyword matching and result reranking

Build reusable React components that save settings in browser storage and support keyboard selection. The patterns cover settings dialogs and command palettes, with guidance on TypeScript types, storage keys, and managing keyboard focus.

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• Keeping app settings across page reloads
• Building command selection with arrow keys and Enter
• Creating reusable settings dialogs

Routes AI development questions to focused references on agents, language model security, retrieval-augmented generation, which uses retrieved information in answers, and prompt design and evaluation. It also outlines principles for validation, safety boundaries, version control, and ongoing assessment.

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• Find guidance for agent development
• Locate references on prompt injection defenses
• Explore retrieval and reranking for RAG

Assess the storage and retrieval layers of RAG systems, which search documents to support AI answers. Test persistent document poisoning, cross-tenant access, source-text leaks, and manipulated search rankings using independently verifiable evidence.

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• Reviewing shared knowledge bases and document chatbots
• Testing access isolation between accounts or organizations
• Checking source text and metadata in search results

Diagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).

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Track a country's average tariffs over time, including rates for a trading partner or product group. Review simple averages and averages weighted by import value, with coverage dates and distinctions between tariff measures.

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• Review changes in a country's average tariffs
• Check average tariffs on imports from a trading partner
• Compare tariff trends for textiles or machinery

Find code that lacks test coverage and get suggestions for tests to add next. Focus on a chosen path, inspect gaps against a coverage threshold, or use a background worker for recurring checks after feature changes.

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• Check for missing tests after adding a feature
• Find coverage gaps in a specific directory
• Focus on the most critical coverage gaps

Build Java applications that store and manage records in Azure Table Storage or Cosmos DB Table API. It guides creating tables, adding and updating records, filtering queries, and grouping changes into transactions that succeed or fail together.

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• Implement product record creation, updates, and deletion
• Filter records and retrieve only the required fields
• Update records together within the same partition

Combine vector search, which finds similar meanings, with keyword search for exact terms. Design retrieval for search engines and RAG systems that supply reference material to AI, using methods to merge and rerank results.

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• Improving retrieval for RAG systems
• Finding names and codes through exact matching
• Supporting searches with specialized vocabulary

Assess the security of RAG systems, which retrieve documents to support AI answers. Test whether stored vectors expose source text, leak documents between tenants, or allow inserted content to dominate search results, then document findings and controls.

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• Assessing source-text exposure from stored vectors
• Checking data isolation between tenants
• Testing retrieval manipulation in a test collection

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

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• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Turn text into embeddings, numeric representations of meaning, through 9Router. Choose a model and process individual texts or batches to prepare data for meaning-based search, similarity comparisons, or RAG systems that retrieve reference material.

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• Preparing text vectors for meaning-based search
• Generating embeddings for text similarity comparisons
• Batch-processing reference passages for RAG

Compare application pages, interactive components, API endpoints, and key user journeys with existing tests. Get a coverage matrix, an estimated coverage percentage, and a prioritized plan for missing tests with suggested templates and effort estimates.

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• Finding untested pages, components, and API endpoints
• Prioritizing tests for sign-in, payments, and core features
• Planning test counts, templates, and effort for coverage gaps

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

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• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

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Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).

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Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.

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Expert knowledge for Azure Table Storage development including best practices, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when securing Table Storage, tuning telemetry, planning scale limits, designing keys, or scripting with PowerShell, and other Azure Table Storage related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Blob Storage (use azure-blob-storage), Azure Queue Storage (use azure-queue-storage).

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