Results for “question-answering”

Builds document question-answering systems with PDF parsing, context retrieval, and answer generation

Ingests EPUBs and ebooks into a local vector index, then answers questions and searches passages via a command-line interface.

Ingests EPUB and ebook files into a local vector index and provides a command-line interface for asking questions about the books.

Ask an AI coding agent to identify which files it needs to examine before answering a question.

Use when answering difficult or disputed TypeScript questions about assignability, inference, narrowing, variance, generics, conditional or mapped types, declarations, or compiler-option semantics.

Evaluates the faithfulness of abstractive summaries by generating questions from summary sentences and verifying if the answers can be extracted from the source document, reporting Pearson and Spearman correlations with human judgments.

Query Google NotebookLM notebooks using Gemini's source-grounded answers. Automates browser-based authentication, notebook management, and question-answering workflows.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Investigates a question systematically before answering — verifying claims through sources, distinguishing known facts from inferences, and calibrating expressed confidence to actual certainty. Use when asked to research a topic, verify a claim, evaluate a technology, or answer a question where accuracy matters more than speed.

Builds operational monitoring dashboards for Grafana, SigNoz, and similar platforms, focusing on answering operator questions about health, bottlenecks, and changes rather than displaying every metric.

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

Provides expertise for analyzing DWARF debug files and understanding the DWARF debug format (v3-v5). Triggers when understanding DWARF information, interacting with DWARF files, answering DWARF-related questions, or working with code that parses DWARF data.

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

Interviews users relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree.

Turn a decision you can't fully answer into a questionnaire for someone else to fill in.

Runs a relentless interview that asks every frontier question at once, round by round. Use when the user wants to be grilled on a design or map a design tree.

对 wiki 提问，综合检索相关页面后回答，好的回答可 crystallize 回 wiki

Runs a relentless interview that sharpens a plan or design. Use when the user wants to be grilled on an idea, pressure-test a plan, or refine a design through questioning.

Turns a decision the user can't fully answer into a questionnaire for someone else to fill in. Use when the user says "turn this into a questionnaire" or needs knowledge pulled out of an expert.

Structure spoken product-sense interview answers with assumptions, segmentation, pain-point prioritization, and MVP tradeoffs.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Interviews the user one question at a time to stress-test a plan or design, exploring the codebase when possible, until reaching a shared understanding.

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

A relentless interview to sharpen a plan or design.

Answers questions using a structured wiki knowledge base, returning cited synthesis with wikilinks, counter-arguments, and knowledge gaps.

Guides users to uncover the real question behind surface problems, reframe decisions, and challenge assumptions through structured questioning.

Ask which skill or flow fits your situation. A router over the skills in this repo.

Runs a relentless interview session to sharpen a plan or design.

Stress-test a plan or design by interviewing the user relentlessly about every aspect until reaching a shared understanding.

Answers factual questions by searching the web, fetching primary sources, and cross-checking claims before citing them.