All authors
Claude Skills by qhjqhj00
github.com/qhjqhj007,572 skills29 installs10,436 views
- Voiceassistant EvalEvaluates AI voice assistants across listening, speaking, and viewing capabilities. It probes audio understanding, multi-turn dialogue generation, role-play imitation, and multimodal vision-audio integration, measuring both content accuracy and speech naturalness. Use when the user wants to benchmark on VoiceAssistant-Eval, or asks about evaluating this task. Reports Final Task Score.Votes: 0GitHub stars: 3
- Voicebank Demand Sep EvalEvaluates zero-shot language-queried speech enhancement by isolating clean speech from noisy backgrounds. The benchmark tests the model's ability to enhance speech using a fixed text query. Use when the user wants to benchmark on Voicebank-DEMAND, or asks about evaluating this task. Reports PESQ.Votes: 0GitHub stars: 3
- Voicebbq EvalEvaluates social bias in Spoken Language Models (SLMs) by isolating content-induced bias and acoustic bias (gender/accent) using a synthesized speech version of the BBQ dataset. It measures how architectural differences in speech encoders affect bias propagation and whether acoustic cues override textual context. Use when the user wants to benchmark on VoiceBBQ, or asks about evaluating this task. Reports bias_score.Votes: 0GitHub stars: 3
- Voicebench Qa EvalEvaluates an agent's reasoning and tool-use capabilities in spoken question answering by requiring it to process audio queries, optionally perform web searches, and generate accurate responses. Use when the user wants to benchmark on OpenBookQA, AlpacaEval, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Voiceloop Tts EvalEvaluates a text-to-speech model's ability to synthesize perceptually natural speech and accurately mimic speaker identities from text and reference embeddings. It measures robustness across clean benchmarks, multi-speaker corpora, and noisy in-the-wild recordings, while testing few-shot voice fitting capabilities. Use when the user wants to benchmark on LJ (LJSpeech), Nancy (Blizzard 2011), Blizzard 2013 Audiobook, VCTK, In-the-wild YouTube speeches, or asks about evaluating this task. Repor...Votes: 0GitHub stars: 3
- Void EvalEvaluates a video generation model's ability to remove specified objects and their downstream physical interactions (e.g., collisions, shadows, reflections) while maintaining temporal consistency and visual quality. It probes counterfactual reasoning and intuitive physics simulation in dynamic scenes. Use when the user wants to benchmark on Real-world object removal dataset, Synthetic counterfactual dataset, or asks about evaluating this task. Reports Win %.Votes: 0GitHub stars: 3
- Voldor EvalEvaluates monocular visual odometry accuracy and depth estimation quality on urban/highway driving sequences and indoor environments. Probes robustness to non-Gaussian optical flow noise and scale ambiguity without relying on hand-crafted features or loop closure. Use when the user wants to benchmark on KITTI odometry benchmark, KITTI stereo benchmark, TUM RGB-D dataset, or asks about evaluating this task. Reports Trans. error (%), Rot. error (deg/m).Votes: 0GitHub stars: 3
- Vos Language ReferringEvaluates a model's ability to perform pixel-level video object segmentation guided by natural language referring expressions, testing both language grounding and temporal consistency in dynamic scenes. Use when the user wants to benchmark on DAVIS-16, DAVIS-17, or asks about evaluating this task. Reports performance score.Votes: 0GitHub stars: 3
- Votreuth Rehab Depth EvalEvaluates marker-less 2D pose estimation models on rehabilitation-specific depth images. It probes the model's ability to generalize from generic adult standing poses to complex clinical postures, including children, and tests robustness against varying subject scales and positions. Use when the user wants to benchmark on ITOP, VtR, VtR-O, or asks about evaluating this task. Reports PCK.Votes: 0GitHub stars: 3
- Vox Safe Bench EvalEvaluates social alignment in speech language models across safety, fairness, and privacy dimensions. It distinguishes between content-centric risks (Tier 1) where text alone suffices to trigger norms, and audio-conditioned risks (Tier 2) where benign transcripts become unsafe due to speaker identity, paralinguistic cues, or environmental context. Use when the user wants to benchmark on VoxSafeBench, or asks about evaluating this task. Reports RtA.Votes: 0GitHub stars: 3
- Voxceleb1 Verification EvalEvaluates speaker verification capability by measuring how well a model distinguishes same-speaker from different-speaker audio pairs. It probes robustness across different evaluation conditions, including a standard test set, an extended large-scale set, and a constrained same-nationality/gender set. Use when the user wants to benchmark on VoxCeleb1, or asks about evaluating this task. Reports EER (%).Votes: 0GitHub stars: 3
- Voxeval EvalVoxEval probes the knowledge understanding and mathematical reasoning capabilities of end-to-end spoken language models (SLMs). It specifically evaluates how well these models comprehend spoken questions and generate accurate spoken answers under diverse audio conditions, including different speakers, speaking styles, and audio qualities. Use when the user wants to benchmark on VoxEval, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Voxknesset Demographic EvalEvaluates whether the VoxKnesset dataset encodes meaningful demographic signals (gender, religion, birthplace) in pretrained speech representations. It probes the dataset's metadata quality and demographic coverage by training lightweight classifiers on extracted embeddings. Use when the user wants to benchmark on VoxKnesset Speaker-Attributed Longitudinal Subset, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Voxpopuli EvalBenchmarks multilingual speech representation learning and semi-supervised ASR/ST performance across multiple languages and domains, measuring phoneme discriminability, recognition accuracy, and translation quality. Use when the user wants to benchmark on VoxPopuli, Common Voice, ZeroSpeech 2017, EuroParl-ST, CoVoST 2, or asks about evaluating this task. Reports WER.Votes: 0GitHub stars: 3
- Voxprivacy EvalEvaluates how well speech language models handle interactional privacy across three tiers: obeying direct secrecy commands, using voice identity for conditional access, and proactively inferring contextually sensitive information to withhold secrets. Use when the user wants to benchmark on VoxPrivacy Benchmark, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Voxstream Tts EvalEvaluates a streaming text-to-speech model's ability to generate high-quality, speaker-similar, and intelligible audio from text prompts in both non-streaming and full-stream (word-by-word) settings. It probes zero-shot voice cloning, cross-sentence continuity, and real-time synthesis latency. Use when the user wants to benchmark on LibriSpeech test-clean, SEED-TTS test-en, or asks about evaluating this task. Reports Naturalness.Votes: 0GitHub stars: 3
- Voxtream2 EvalThis evaluation probes a full-stream text-to-speech model's ability to generate intelligible, natural-sounding speech while dynamically controlling the speaking rate in real-time. It measures objective intelligibility, speaker similarity, audio quality, generation latency, and the accuracy of speaking-rate control against target rates. Use when the user wants to benchmark on Emilia speaking-rate dataset, or asks about evaluating this task. Reports WER (%).Votes: 0GitHub stars: 3
- VpevalEvaluates text-to-image generation models by decomposing assessment into five specialized skills (object presence, count, spatial relations, scale, and text rendering) and open-ended prompts, producing interpretable binary scores with visual and textual explanations. Use when the user has predictions and gold and needs to compute vpeval.Votes: 0GitHub stars: 3
- Vprochart EvalEvaluates a model's ability to understand chart visuals and perform multi-step numerical and logical reasoning to answer natural language questions. It specifically probes visual perception alignment and programmatic solution reasoning over structured chart data. Use when the user wants to benchmark on ChartQA, PlotQA, DVQA, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vpt Minecraft EvalEvaluates an agent's ability to perform complex, multi-step sequential decision-making tasks in a 3D sandbox environment (Minecraft) using a native human-like interface. It probes zero-shot generalization, behavioral cloning fine-tuning, and reinforcement learning fine-tuning for long-horizon crafting and exploration. Use when the user wants to benchmark on webClean, contractor_house, earlygame_keyword, or asks about evaluating this task. Reports reliability.Votes: 0GitHub stars: 3
- Vqa Benchmarks EvalEvaluates vision-language models' ability to answer questions across general knowledge, OCR, mathematics, and science domains using few-shot in-context learning. It also probes the model's capacity to attend to interleaved image-text contexts through a 'cheat test' protocol. Use when the user wants to benchmark on TextVQA, OKVQA, MathVista, MathVision, MathVerse, ScienceQA-IMG, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vqa Biomedical EvalEvaluates multimodal models on biomedical visual question answering tasks. It probes the model's ability to interpret medical images (e.g., X-rays, pathology slides) and generate accurate answers to clinical or radiological questions in both open-ended and closed-ended formats. Use when the user wants to benchmark on VQA-RAD, SLAKE, PathVQA, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vqa Captioning EvalEvaluates multimodal language models on visual question answering and image captioning tasks, probing their zero-shot and few-shot in-context learning capabilities with interleaved image-text inputs. Use when the user wants to benchmark on OKVQA, TextVQA, COCO, Flickr30k, VQAv2, VizWiz, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vqa Cot Reasoning EvalProbes the ability of vision-language models to perform multi-step chain-of-thought reasoning on visual inputs across diverse domains like charts, documents, science diagrams, and math. It measures both direct answer accuracy and structured reasoning accuracy. Use when the user wants to benchmark on A-OKVQA, ChartQA, DocVQA, InfoVQA, TextVQA, AI2D, ScienceQA, MathVista, OCRBench, MMStar, MMMU, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vqa Gen EvalThis benchmark evaluates a model's ability to generalize in Visual Question Answering under coordinated visual and textual distribution shifts. It probes robustness to image corruptions, style transfers, and linguistic variations by measuring in-domain and cross-domain accuracy. Use when the user wants to benchmark on VQA-GEN, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Vqa Generalization EvalEvaluates a model's ability to answer visual questions by generalizing from synthetic template-based training data to complex, human-written questions. It probes both closed-form accuracy and open-form reasoning capabilities across 3D-rendered and medical imaging domains. Use when the user wants to benchmark on CLEVR-Human, VQA-RAD, SLAKE, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vr Bench EvalThis benchmark evaluates the spatial reasoning and trajectory planning capabilities of video generation models and vision-language models through maze-solving tasks. It probes whether models can generate coherent, rule-compliant movement sequences or videos that faithfully navigate complex, multi-type mazes such as regular, irregular, 3D, Sokoban, and trap fields. Use when the user wants to benchmark on VR-Bench, or asks about evaluating this task. Reports MF.Votes: 0GitHub stars: 3
- Vsdx EvalEvaluates vision-language models' ability to perceive and reason about non-RGB sensor data (thermal, depth, X-ray). It probes low-level perception (existence, counting, position, description) and high-level understanding (contextual reasoning, sensor-specific physical property interpretation). Use when the user wants to benchmark on VS-TDX, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vsi Bench EvalEvaluates multimodal large language models' ability to reason about 3D spatial relationships from visual inputs. It probes capabilities across configurational tasks (counting, relative distance/direction, route planning), measurement estimation (object/room size, absolute distance), and spatiotemporal ordering. Use when the user wants to benchmark on VSI-Bench, or asks about evaluating this task. Reports score.Votes: 0GitHub stars: 3
- Vtab Md Fewshot EvalEvaluates few-shot classification performance across diverse visual domains by comparing transfer learning and meta-learning approaches on a unified benchmark combining VTAB and Meta-Dataset. Use when the user wants to benchmark on VTAB+MD, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vtc Bench EvalEvaluates multimodal large language models' ability to perform agentic visual reasoning by composing multiple OpenCV-based tool calls. It probes long-horizon planning, precise tool selection, and the capacity to chain coarse- to fine-grained visual operations to solve complex multi-step problems. Use when the user wants to benchmark on VTC-Bench, or asks about evaluating this task. Reports Average Pass Rate (APR).Votes: 0GitHub stars: 3
- Vtc R1 EvalThis evaluation protocol assesses a vision-language model's ability to perform long-context mathematical and scientific reasoning using vision-text compression. It measures both reasoning accuracy across diverse benchmarks and computational efficiency in terms of token usage and inference latency. Use when the user wants to benchmark on GSM8K, MATH500, AIME25, AMC23, GPQA-Diamond, or asks about evaluating this task. Reports Accuracy (ACC).Votes: 0GitHub stars: 3
- Vtcbench EvalEvaluates the ability of visual token compression methods to retain task-relevant visual information across five dimensions: global understanding, spatial and counting, reasoning and common sense, style and emotion, and local details. Use when the user wants to benchmark on VTCBench, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Vtikz EvalEvaluates Large Language Models' ability to customize and edit TikZ code based on a specified visual intent. The benchmark probes three core capabilities: locating relevant code features, synthesizing correct code variants, and validating that the modified code produces the intended visual output. Use when the user wants to benchmark on vTikZ, or asks about evaluating this task. Reports visual result validation.Votes: 0GitHub stars: 3
- Vukuzenzele Za Gov EvalThis benchmark evaluates multilingual sentence alignment and machine translation capabilities across 11 official South African languages using government-themed corpora. Use when the user wants to benchmark on Vuk'uzenzele, ZA-gov-multilingual, or asks about evaluating this task. Reports cosine similarity.Votes: 0GitHub stars: 3
- W Boson Regression EvalEvaluates a model's ability to reconstruct the 4-momentum and mass of a W-boson from jet constituents, testing regression accuracy and physical consistency under truth-level and detector-simulated (Delphes) conditions. It compares equivariant neural networks against traditional physics-based taggers. Use when the user wants to benchmark on W-boson 4-momentum regression dataset, or asks about evaluating this task. Reports resolution ($\sigma_{p^{T}}$, $\sigma_{m}$, $\sigma_{\Delta R}$).Votes: 0GitHub stars: 3
- Wa Hls4ml EvalProbes the ability of surrogate models to accurately predict FPGA resource usage (e.g., LUTs, BRAMs) and inference latency (clock cycles) for neural networks synthesized via hls4ml. It evaluates how well learned models approximate time-consuming hardware synthesis processes without running the full compilation pipeline. Use when the user wants to benchmark on wa-hls4ml benchmark, or asks about evaluating this task. Reports R².Votes: 0GitHub stars: 3
- Wac EvalEvaluates models on detecting online abuse (personal attacks, aggression, toxicity) in conversational contexts reconstructed from Wikipedia talk pages. It probes the ability to classify individual messages as abusive or non-abusive while leveraging or ignoring conversational structure depending on the method. Use when the user wants to benchmark on WAC (Wikipedia Conversations Corpus), or asks about evaluating this task. Reports Macro F-measure.Votes: 0GitHub stars: 3
- Wake Vision EvalEvaluates the robustness and accuracy of TinyML person detection models across diverse demographic, environmental, and visual conditions. It benchmarks binary classification performance on large-scale, real-world image datasets tailored for resource-constrained devices. Use when the user wants to benchmark on Wake Vision, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Wals Metalinguistic EvalProbes large language models' ability to recall and identify structural and grammatical properties of languages across diverse linguistic domains. It measures whether models have internalized typological facts from the World Atlas of Language Structures (WALS) by answering multiple-choice questions about specific language features. Use when the user wants to benchmark on WALS, WALS-100, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Wan Image Understanding EvalEvaluates the model's multimodal and text understanding capabilities across a suite of standard academic benchmarks covering visual question answering, reasoning, hallucination detection, and text-based reasoning. Use when the user wants to benchmark on MMMU, MMStar, MathVista, HalluBench, MMBench, OCRBench, AI2D, AIME, GPQA, HLE, LCBV6, or asks about evaluating this task. Reports average score.Votes: 0GitHub stars: 3
- Wanderland EvalEvaluates the fidelity of geometrically grounded 3D reconstruction and novel view synthesis for open-world urban environments, and benchmarks the performance of embodied navigation policies trained and evaluated in these simulated environments. Use when the user wants to benchmark on Wanderland, or asks about evaluating this task. Reports Navigation Error (NE).Votes: 0GitHub stars: 3
- Wangchanthaiinstruct EvalEvaluates instruction-following capabilities of LLMs in Thai across culture-aware, domain-specific (Medical, Law, Finance, Retail), and multitask settings. Probes factual accuracy, reasoning quality, and fluency in both zero-shot and fine-tuned regimes. Use when the user wants to benchmark on WangchanThaiInstruct, Thai LLM Leaderboard, Thai MT-Bench, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Warbert Web Api Recommendation EvalEvaluates a model's ability to recommend relevant Web APIs for a given mashup application based on textual descriptions. It also probes multi-task learning capability through an auxiliary mashup category classification task. Use when the user wants to benchmark on ProgrammableWeb, or asks about evaluating this task. Reports Precision@N.Votes: 0GitHub stars: 3
- Wasm Bench EvalEvaluates the performance trade-offs of a WebAssembly in-place interpreter against state-of-the-art Wasm engines across translation time, translation space overhead, and execution time on a standard benchmark suite. Use when the user wants to benchmark on PolyBenchC-4.2.1 MEDIUM, or asks about evaluating this task. Reports translation_time.Votes: 0GitHub stars: 3
- Wave Unet Musdb EvalEvaluates end-to-end time-domain audio source separation models on singing voice and multi-instrument separation tasks. Probes the model's ability to isolate specific audio sources from mixed recordings using raw waveform inputs. Use when the user wants to benchmark on MUSDB, CCMixter, or asks about evaluating this task. Reports MSE.Votes: 0GitHub stars: 3
- Wavenet Audio EvalEvaluates the capability of autoregressive generative models to synthesize high-quality raw audio waveforms for speech and music, and to perform discriminative tasks like speech recognition directly on raw audio without intermediate feature extraction. Use when the user wants to benchmark on VCTK (CSTR Voice Cloning Toolkit), Google TTS (NA English & Mandarin), MagnaTagATune, YouTube Piano, TIMIT, or asks about evaluating this task. Reports Mean Opinion Score (MOS).Votes: 0GitHub stars: 3
- Waver Bench EvalEvaluates a video generation model's capacity to synthesize physically coherent motion, high-fidelity visuals, and strict adherence to textual or image prompts across diverse scenarios. Use when the user wants to benchmark on Waver-Bench 1.0, Hermes Motion Testset, or asks about evaluating this task. Reports Human Preference Win Rate.Votes: 0GitHub stars: 3
- Waymo Open Dataset EvalThis benchmark evaluates 3D and 2D object detection, as well as multi-object tracking, for autonomous driving perception. It probes a model's ability to accurately localize vehicles and pedestrians using synchronized LiDAR and camera data, while measuring robustness to geographic domain shifts and varying training data scales. Use when the user wants to benchmark on Waymo Open Dataset, or asks about evaluating this task. Reports APH.Votes: 0GitHub stars: 3
- Wcep Mds EvalEvaluates multi-document summarization systems on news event clusters by measuring how well generated summaries match human-written reference summaries. It probes the model's ability to extract or generate concise, informative summaries from highly redundant, large-scale document collections. Use when the user wants to benchmark on WCEP, or asks about evaluating this task. Reports ROUGE F1-score.Votes: 0GitHub stars: 3