Results for “gage-rr”

Builds or modifies a RAG pipeline with intent-based routing, vector store search, citation rendering, and book fidelity enforcement.

Giskard RAGET (RAG Evaluation Toolkit): automatic testset generation (simple / complex / distracting / conversational), component-level scoring (retriever / generator / rewriter), hallucination and bias tests, CI integration. Compared to RAGAS and DeepEval. USE WHEN: user mentions "Giskard", "RAGET", "Giskard RAG toolkit", "automatic testset generation", "component-level RAG scoring", "hallucination test Giskard" DO NOT USE FOR: general RAGAS usage - use `rag-evaluation`; Stanford ARES - use `ares-framework`; CI/CD wiring - use `continuous-evaluation`

Evaluate RAG pipelines with Ragas — measure faithfulness, answer relevancy, context precision/recall, and noise sensitivity using LLM-as-judge metrics; run automated test suite generation with TestsetGenerator; integrate with LangChain, LlamaIndex, and CI pipelines.

Evaluate retrieval quality from the local RAG index

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Expert en RAG agentique (retrieval multi-étapes, self-query, chunking adaptatif, tool-augmented generation)

Create a clear project status brief with progress, risks, blockers, decisions, and next actions.

Google Tag Manager expertise. Audit GTM containers, design tag and trigger architecture, debug firing issues, implement consent mode v2, and advise on server-side tagging. Use when the user asks about GTM, tag management, container audits, consent mode, server-side tagging, or tracking implementation.

Redux-Saga (generator-based) middleware for async side effects — yields declarative effects (call/put/select/takeLatest/delay) instead of imperative async code, watcher-per-slice composition, optimistic updates with rollback. Use when authoring per-slice operations.js, the rootSagas composer, or reviewing saga workers.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

Drafts a Request for Equitable Adjustment (REA) for U.S. federal government contracts under FAR provisions. Enforces element-driven cost/schedule narratives, FAR Part 31 cost structures, FAR 52.233-1 certification, and contemporaneous evidence marshaling. Use when a contractor seeks contract modification relief due to government-directed changes, differing site conditions, defective specifications, or government-caused delays.

基于 Modular RAG MCP Server 项目生成定制化简历项目经历。结合项目技术亮点与用户业务场景，按简历编写原则输出高质量项目描述（中英文）。Use when user says '写简历', 'resume', '简历', 'write resume', '项目经历', 'project experience', '简历项目', or asks to generate resume content based on this project.

Caching strategies across the RAG stack. Semantic caching with GPTCache and LangChain, Redis-based embedding-similarity cache, cache key design, TTL/invalidation, partial caching (cache retrieval only), provider-native prompt caching (Anthropic, OpenAI), and hierarchical L1/L2 caches. USE WHEN: user mentions "semantic cache", "GPTCache", "LLM cache", "prompt caching", "Redis vector cache", "cache invalidation for RAG", "reduce LLM cost", "latency reduction LLM" DO NOT USE FOR: retrieval accuracy - use `rag-patterns`; groundedness checks - use `rag-guardrails`; incremental indexing - use `rag-production`

RAG Builder with Parallel Document Processing Vector database construction with local embeddings (zero cost) Handles PDF download, text extraction, chunking, and vector database creation Absorbed B5 (Parallel Document Processor) capabilities Use when: building RAG, creating vector database, downloading PDFs, embedding documents, batch processing Triggers: build RAG, create vector database, download PDFs, embed documents, batch PDF processing

Facilitates a read-only standup across git worktrees, branches, or PRs to compare changes and produce one consolidation plan.

为 MODULAR-RAG-MCP-SERVER 添加 Agentic RAG 层并跑出真实评测数据。 作者背景：应届生 + 无实习，目标 Agent 方向求职。 使用场景： 1. 用户说"加 Agent 层" / "实现 ReAct Agent" / "Agentic RAG" / "多步推理" 2. 用户说"跑评测" / "出评测数字" / "Ragas 实测" / "对比指标" 3. 用户说"继续扩建项目" / "Stage J" / "Stage K" 4. 用户在新对话中提到这个 RAG 项目需要扩展 该 skill 记录了所有背景知识、架构决策和实现细节，使 Codex 无需重新探索即可直接执行。

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

基于 Modular RAG MCP Server 项目生成定制化简历项目经历。结合项目技术亮点与用户业务场景，按简历编写原则输出高质量项目描述（中英文）。Use when user says '写简历', 'resume', '简历', 'write resume', '项目经历', 'project experience', '简历项目', or asks to generate resume content based on this project.

基于 Modular RAG MCP Server 项目生成定制化简历项目经历。结合项目技术亮点与用户业务场景，按简历编写原则输出高质量项目描述（中英文）。Use when user says '写简历', 'resume', '简历', 'write resume', '项目经历', 'project experience', '简历项目', or asks to generate resume content based on this project.

Acquires resources from URLs, PDFs, and local files, categorizes them, and organizes content and media into a structured workspace with a lowercase assets folder.

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

Builds Retrieval-Augmented Generation pipelines with vector stores, chunking strategies, and reranking

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Agent-driven RAG patterns. Self-RAG, Corrective RAG (CRAG) with web fallback, Adaptive RAG with routing classifier, ReAct with retrieval tool, multi-hop retrieval, plan-and-execute, LangGraph state machines for RAG. USE WHEN: user mentions "agentic RAG", "Self-RAG", "Corrective RAG", "CRAG", "Adaptive RAG", "multi-hop retrieval", "LangGraph RAG", "ReAct RAG", "plan and execute RAG" DO NOT USE FOR: static retrieval pipelines - use `rag-architecture`; query rewriting only - use `query-transformations`; evaluation - use `rag-evaluation`

Guardian of V.A.I.R.E. quality standards (Value/Agency/Identity/Resilience/Echo). Handles pre-release evaluation, scorecard assessment, and pass/fail determination. Use when UX quality gates are needed. Does not write code.

Reconcile a fund general-ledger account to its supporting subledger or source — investments, cash, income, expense, or capital — prove the bridge, and clear differences before the NAV is struck.

Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.

Turn revenue reporting from a rear-view mirror into an operating rhythm. It narrates where you are against target, decomposes the variance by channel, reads your forecast for where you land, then adds an action layer of hot leads and deals to work now. Built for GTM teams reporting on ARR and bookings, customizable to your sheet and your metrics. Trigger on "weekly revenue wrap-up", "monthly revenue report", "how are we pacing", "revenue position", "forecast confidence", "run the weekly report", or any ARR, bookings, pipeline, or channel-performance review.

Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, vector stores, retrieval, evaluation. Use when building a RAG pipeline, choosing chunking strategies or embedding models, debugging retrieval quality or hallucinations, evaluating an existing RAG system, or scaling/migrating vector stores.

Session-end memory garbage collector. Promotes valuable L2 session facts to L1 atomic memory, wipes L2, and rotates oversized audit logs. Run at end of session to prevent context inflation and storage bloat.