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Claude Skills by qhjqhj00
github.com/qhjqhj007,572 skills29 installs10,436 views
- Text To Sql EvalEvaluates a model's ability to translate natural language questions into correct SQL queries for a complex, real-world industrial database. It also probes schema-linking precision by measuring how accurately the model identifies the required tables from the schema. Use when the user wants to benchmark on Industrial Energy Database Benchmark, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Text Video Alignment EvalThis evaluation protocol assesses how well text-to-video generation models align generated content with textual prompts across fine-grained attributes like object counts, colors, actions, and spatial relationships. It measures both semantic alignment and visual/motion quality to determine if refinement techniques successfully correct misalignments without degrading fidelity. Use when the user wants to benchmark on EvalCrafter, T2V-CompBench, or asks about evaluating this task. Reports Text-Vi...Votes: 0GitHub stars: 3
- Text2analysis EvalEvaluates large language models on table question answering with advanced data analysis tasks, including forecasting and chart generation, as well as unclear queries that lack explicit parameters. Probes the model's ability to perform semantic parsing, infer missing parameters, generate executable analysis code, and reason about data visualization. Use when the user wants to benchmark on Text2Analysis, or asks about evaluating this task. Reports ECR, pass@1.Votes: 0GitHub stars: 3
- Text2distbench EvalEvaluates large language models' ability to infer population-level statistics (e.g., sentiment proportions, topic frequencies) from aggregated natural language text. It probes marginal, conditional, and joint distribution estimation over discrete categories derived from real-world comments. Use when the user wants to benchmark on Text2DistBench, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Textatlas EvalEvaluates text-to-image generation models on their ability to render long, dense, and structurally complex text accurately within images. It probes both semantic alignment between the prompt and the generated image, and precise character/word-level OCR fidelity across diverse layouts, styles, and real-world scenes. Use when the user wants to benchmark on TextAtlasEval, or asks about evaluating this task. Reports OCR Accuracy (Acc.).Votes: 0GitHub stars: 3
- Textme EvalEvaluates zero-shot cross-modal retrieval and classification across six modalities (image, video, audio, 3D, X-ray, molecules) using a text-only expansion framework. It probes whether unpaired text descriptions can bridge the geometric modality gap to align diverse modalities into a unified LLM embedding space without paired supervision. Use when the user wants to benchmark on COCO, Flickr30k, MSR-VTT, MSVD, DiDeMo, AudioCaps, Clotho, DrugBank, AudioSet, ESC-50, ModelNet40, ScanObjectNN, RSNA...Votes: 0GitHub stars: 3
- Texttabbench EvalEvaluates foundation models on tabular prediction tasks that require leveraging mixed categorical, numerical, and free-text features across diverse real-world domains. The benchmark tests whether models can maintain predictive performance when text features contain semantic ambiguity, synonym variation, or noise, while preserving structural tabular signals. Use when the user wants to benchmark on fraud, kick, osha, cards, complaints, spotify, airbnb, beer, houses, laptops, mercari, permits, w...Votes: 0GitHub stars: 3
- Texture Sam EvalEvaluates a model's ability to perform texture-aware segmentation by measuring how well it segments regions based on repeating texture patterns rather than semantic shape cues. It tests generalization on both synthetic texture-only images and natural images, while also checking for catastrophic forgetting on standard semantic benchmarks. Use when the user wants to benchmark on RWTD, STMD, ADE20K, or asks about evaluating this task. Reports mIoU.Votes: 0GitHub stars: 3
- Textvr Retrieval EvalEvaluates cross-modal video retrieval models that must jointly process visual context and scene text (OCR tokens) to match sentence queries with relevant videos. Probes the model's ability to read, comprehend, and align fine-grained text semantics with visual frames in real-world scenarios. Use when the user wants to benchmark on TextVR, or asks about evaluating this task. Reports R@K (Recall@K).Votes: 0GitHub stars: 3
- Tf2aif Inference Latency EvalEvaluates the execution latency and code-generation efficiency of automated, platform-specific AI inference engines across heterogeneous cloud-edge hardware (CPU, GPU, ARM, FPGA, SoC) for image classification tasks. Use when the user wants to benchmark on Image classification dataset (unspecified), or asks about evaluating this task. Reports execution latency.Votes: 0GitHub stars: 3
- Tfbs Classification EvalEvaluates a model's ability to classify short DNA sequences as transcription factor binding sites or not, capturing its capacity to learn regulatory sequence patterns from genomic data. Use when the user wants to benchmark on TFBS classification, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Tfq Bench EvalEvaluates a model's ability to understand visual metaphors and image implications by verifying multiple factual and inferential propositions per image. It probes fine-grained visual perception, multi-hop reasoning, and theory of mind through structured true-false questioning. Use when the user wants to benchmark on TFQ-Bench, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Tfrb EvalEvaluates the causal reasoning and forecasting accuracy of LLMs and time-series models on multi-domain time-series data. It probes whether step-by-step reasoning and external event context improve numerical predictions or introduce narrative bias, particularly in stochastic versus pattern-rich domains. Use when the user wants to benchmark on TFRBench, or asks about evaluating this task. Reports MASE.Votes: 0GitHub stars: 3
- Tg Redial EvalEvaluates a conversational recommender system's ability to naturally transition topics, recommend relevant items, and generate coherent responses within a dialogue. It probes the model's capacity to leverage historical interactions, user profiles, and topic sequences to maintain semantic flow and recommendation accuracy. Use when the user wants to benchmark on TG-ReDial, or asks about evaluating this task. Reports NDCG@k.Votes: 0GitHub stars: 3
- Tgbsseq EvalEvaluates temporal graph neural networks on future link prediction tasks, specifically probing their ability to generalize to unseen edges and capture complex sequential dynamics rather than memorizing repeated interactions. Use when the user wants to benchmark on ML-20M, Taobao, Yelp, GoogleLocal, Wikipedia, Reddit, Flickr, YouTube, Patent, WikiLink, or asks about evaluating this task. Reports MRR.Votes: 0GitHub stars: 3
- Thai Ser EvalEvaluates speech emotion recognition models on a culturally grounded Thai speech corpus, testing their ability to classify utterances into five emotion categories (neutral, angry, happy, sad, frustrated) across different recording environments and cross-corpus settings. Use when the user wants to benchmark on THAI-SER, or asks about evaluating this task. Reports weighted accuracy.Votes: 0GitHub stars: 3
- Thaiocrbench EvalEvaluates vision-language models on Thai-language text-rich visual tasks, including document parsing, table/chart recognition, handwritten content extraction, and visual question answering. It probes fine-grained text recognition, structural layout understanding, and semantic reasoning in a low-resource, script-complex language setting. Use when the user wants to benchmark on ThaiOCRBench, or asks about evaluating this task. Reports BMFL.Votes: 0GitHub stars: 3
- The Colosseum EvalEvaluates the generalization and robustness of robotic behavior cloning models under various environmental perturbations. It probes how well models trained on clean demonstrations can complete manipulation tasks when faced with changes in lighting, color, distractors, camera pose, and object properties. Use when the user wants to benchmark on The Colosseum, or asks about evaluating this task. Reports task-averaged success rate.Votes: 0GitHub stars: 3
- The Well EvalEvaluates deep learning surrogate models on autoregressive time-series prediction across 16 diverse physics simulations. It probes the model's ability to forecast future spatiotemporal states from short historical snapshots and maintain stability over longer rollout horizons. Use when the user wants to benchmark on The Well, or asks about evaluating this task. Reports VRMSE.Votes: 0GitHub stars: 3
- TheilsuCompute the TheilsU metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute TheilsU, or asks how to score with TheilsU.Votes: 0GitHub stars: 3
- Themis Coderewardbench EvalEvaluates code reward models on their ability to rank code pairs across five quality dimensions (correctness, efficiency, security, readability, maintainability) and eight programming languages. It probes whether models can generalize beyond functional correctness to assess non-functional code attributes and cross-lingual code preferences. Use when the user wants to benchmark on Themis-CodeRewardBench, or asks about evaluating this task. Reports preference accuracy.Votes: 0GitHub stars: 3
- Themisio Io Sharing EvalEvaluates a policy-driven I/O sharing framework for burst buffers by measuring how effectively it allocates bandwidth, maintains fairness, and reduces interference across concurrent workloads. It probes the system's ability to enforce primitive and composite sharing policies under varying load conditions and compares performance against baseline schedulers. Use when the user wants to benchmark on ThemisIO Benchmark & Application Suite, or asks about evaluating this task. Reports sustained I/O...Votes: 0GitHub stars: 3
- Theoremqa EvalEvaluates LLMs' ability to apply domain-specific theorems from mathematics, physics, computer science, and finance to solve complex scientific problems. It probes theorem-driven reasoning, numerical computation, and program generation capabilities. Use when the user wants to benchmark on TheoremQA, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Theory Of Mind Qa EvalEvaluates a model's ability to track first-order and second-order false beliefs, distinguishing an agent's mental state from physical reality and memory. It probes whether systems can maintain consistent world-state representations when agents hold incorrect beliefs about object locations or events. Use when the user wants to benchmark on Sally-Anne & Icecream Van Tasks, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Thinkjepa Ego Dex EvalEvaluates a model's ability to forecast future 3D hand/joint trajectories and latent video representations from egocentric video inputs. It probes long-horizon temporal consistency and physical plausibility in dexterous manipulation scenarios. Use when the user wants to benchmark on EgoDex, EgoExo4D, or asks about evaluating this task. Reports ADE.Votes: 0GitHub stars: 3
- Thinkswitcher Math EvalEvaluates a model's ability to dynamically switch between short and long chain-of-thought reasoning modes based on task complexity, balancing mathematical problem-solving accuracy against computational efficiency. It probes whether a single reasoning model can adaptively select concise or elaborate reasoning paths without architectural changes or post-training. Use when the user wants to benchmark on GSM8K, MATH-500, AIME24, AIME25, LiveAoPS, Omni-MATH-500, OlympiadBench, or asks about evalua...Votes: 0GitHub stars: 3
- Thiomi Baseline EvalEvaluates the quality and utility of a multimodal corpus for low-resource African languages. It does so by training and testing baseline models for automatic speech recognition, machine translation, and text-to-speech across multiple languages. Use when the user wants to benchmark on Thiomi Dataset, or asks about evaluating this task. Reports WER.Votes: 0GitHub stars: 3
- Threat Intelligence EvalThis benchmark evaluates an AI system's ability to extract actionable insights from threat intelligence reports and perform security reasoning. It probes multi-document comprehension, attack chain reconstruction, and MITRE ATT&CK framework mapping capabilities. Use when the user wants to benchmark on CyberSOCEval Threat Intelligence Reasoning, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Thyme Multimodal EvalEvaluates multimodal large language models on image manipulation, visual perception, mathematical reasoning, and general vision-language tasks. It probes whether autonomous code generation and execution for image processing improves downstream accuracy and reduces hallucination. Use when the user wants to benchmark on MME-RealWorld, HR Bench, MathVista, Hallucination bench, MMStar, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Thyme Scene Graph EvalEvaluates a model's ability to generate dynamic video scene graphs by predicting inter-object relationships and attributes across multiple temporal frames. It specifically probes temporal consistency, handling of occlusions, and modeling of long-range dependencies in both ground-level and aerial video footage. Use when the user wants to benchmark on ASPIRe, AeroEye-v1.0, or asks about evaluating this task. Reports R@20, mR@20.Votes: 0GitHub stars: 3
- Tian Gong St Click EvalEvaluates click models on predicting user click sequences and estimating document relevance from search logs. It also measures how well models recover the underlying distribution of real click data (distributional coverage) and perform when document lists are poorly ranked. Use when the user wants to benchmark on TianGong-ST, or asks about evaluating this task. Reports LL.Votes: 0GitHub stars: 3
- Tib Bench EvalEvaluates vision-language models on their ability to generate accurate and linguistically coherent summaries of text-heavy multimodal presentations. It probes cross-modal alignment, long-context understanding, and the impact of different input modalities (raw video, slides, transcripts, interleaved pairs) and token budgets on summarization quality. Use when the user wants to benchmark on TIB-bench, or asks about evaluating this task. Reports $IbR_{overall}$.Votes: 0GitHub stars: 3
- Tictactoe Manipulation EvalEvaluates a robot's ability to perform adversarial interaction and temporal reasoning through a structured pick-and-place board game. It tests the integration of vision-based contour segmentation, game-state reasoning via Minimax, and precise motion planning under hardware constraints. Use when the user wants to benchmark on Yale-CMU-Berkeley objects set, or asks about evaluating this task. Reports t_sub.Votes: 0GitHub stars: 3
- Tid 8 EvalProbes a model's ability to learn from inherently subjective or disagreed-upon annotations by treating each annotator's label as a separate example, rather than aggregating them into a single ground truth label. Use when the user wants to benchmark on TID-8, or asks about evaluating this task. Reports exact match accuracy.Votes: 0GitHub stars: 3
- Tidmad Denoising EvalEvaluates the ability of traditional and deep learning denoising algorithms to recover sinusoidal dark matter signals from ultra-long, noisy time series data collected by the ABRACADABRA experiment. Use when the user wants to benchmark on TIDMAD, or asks about evaluating this task. Reports mean square error.Votes: 0GitHub stars: 3
- Tiered Data Management EvalEvaluates the impact of tiered data management (L1–L3) on model performance across general knowledge, reasoning, math, and code domains. It compares models trained on different data quality tiers and different training strategies (mix vs. tiered) to validate data curation and scheduling efficacy. Use when the user wants to benchmark on OpenCompass Benchmarks, or asks about evaluating this task. Reports Average benchmark scores.Votes: 0GitHub stars: 3
- Tifa 100 EvalEvaluates how well an automatic multimodal transformer predicts fine-grained human feedback (quality scores, region-level misalignment heatmaps, and text misalignment annotations) on text-to-image generation tasks. Use when the user wants to benchmark on TIFA, or asks about evaluating this task. Reports correlation.Votes: 0GitHub stars: 3
- Tii Ssrc 23 EvalEvaluates the relative contribution of network traffic features to intrusion detection models by measuring performance degradation when features are shuffled. Probes the model's reliance on specific packet-level and flow-level statistics for distinguishing benign from malicious traffic and classifying attack types. Use when the user wants to benchmark on TII-SSRC-23, or asks about evaluating this task. Reports Permutation Feature Importance (PFI).Votes: 0GitHub stars: 3
- Time EvalEvaluates large language models' ability to reason about temporal information in real-world scenarios. It probes capabilities ranging from basic time retrieval and localization to complex event ordering, duration comparison, and counterfactual temporal reasoning across knowledge-intensive, dynamic news, and long-form dialogue contexts. Use when the user wants to benchmark on TimE-Wiki, TimE-News, TimE-Dial, or asks about evaluating this task. Reports Exact Match (EM).Votes: 0GitHub stars: 3
- Time Moe Forecasting EvalEvaluates long-term time series forecasting capabilities of foundation models in both zero-shot (unseen datasets) and in-distribution (fine-tuned) settings across multiple prediction horizons. Use when the user wants to benchmark on ETTh1, ETTh2, ETTm1, ETTm2, Weather, Global Temp, or asks about evaluating this task. Reports MSE.Votes: 0GitHub stars: 3
- Time Ra EvalEvaluates the ability of LLMs and MLLMs to diagnose anomalies in univariate and multivariate time series data. It probes the models' capacity for structured reasoning (generating a 'Thought') and precise action classification ('ActionID') based on raw or visualized temporal data. Use when the user wants to benchmark on RATs40K, or asks about evaluating this task. Reports Label Matching F1.Votes: 0GitHub stars: 3
- Time Series Benchmark EvalEvaluates zero-shot forecasting performance of time series foundation models across diverse datasets and horizons. Probes model capability to capture structural temporal patterns (trend, seasonality, stationarity, complexity) and generalizes to unseen data without leakage. Use when the user wants to benchmark on TIME Benchmark, or asks about evaluating this task. Reports MASE, CRPS.Votes: 0GitHub stars: 3
- Time Series Forecasting EvalEvaluates the capability of LLM-based models to forecast multivariate time series across multiple prediction horizons and real-world datasets. It probes pattern-aware temporal modeling and semantic alignment by measuring prediction accuracy under a channel-independent, rolling forecasting setup. Use when the user wants to benchmark on ETTh1, ETTh2, ETTm1, ETTm2, Weather, ECL, Traffic, or asks about evaluating this task. Reports MSE.Votes: 0GitHub stars: 3
- Time Series Transfer EvalEvaluates the effectiveness of transfer learning on time series data by comparing pre-trained models against models trained from scratch across intra-domain and cross-domain settings. It probes whether shared temporal structures enable knowledge transfer and how dataset size and domain similarity affect predictive performance and training convergence. Use when the user wants to benchmark on LEN-DB, SPEECH, EMG, S&P 500, LOMAX, STEAD, or asks about evaluating this task. Reports MAE, weighted F...Votes: 0GitHub stars: 3
- Timer EvalThis benchmark evaluates a model's ability to perform temporal reasoning and extract accurate information from longitudinal electronic health records (EHRs). It probes whether models can correctly synthesize evidence across multiple time-stamped clinical visits, adhere to specified temporal boundaries, and maintain accuracy over long patient timelines. Use when the user wants to benchmark on TIMER-Bench, MedAlign, or asks about evaluating this task. Reports Correct.Votes: 0GitHub stars: 3
- Timer Time Series EvalEvaluates a large decoder-only Transformer model on standard time series benchmarks for forecasting, imputation, and anomaly detection. It probes the model's few-shot generalization, scalability, and robustness in data-scarce scenarios compared to encoder-only baselines. Use when the user wants to benchmark on ETT, ECL, Traffic, Weather, PEMS, UCR Anomaly Archive, or asks about evaluating this task. Reports MSE.Votes: 0GitHub stars: 3
- Timerrecipe EvalEvaluates the effectiveness of individual architectural modules (e.g., normalization, decomposition, embedding, feedforward types) across diverse time-series forecasting scenarios to identify optimal configurations and predict performance without training. Use when the user wants to benchmark on PEMS03, ETT, Electricity, Social (Unemployment), or asks about evaluating this task. Reports MSE.Votes: 0GitHub stars: 3
- Timeseries Exam EvalThis benchmark evaluates vision-language models' ability to reason over time series data across medical, financial, and meteorological domains. It probes pattern recognition, anomaly detection, and causal reasoning using synthetically generated multiple-choice questions derived from real-world datasets. Use when the user wants to benchmark on PTB-XL, MIT-BIH, MIMIC-IV Waveform, Yahoo Finance, WeatherBench 2, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Timeseries Forecasting EvalEvaluates the forecasting accuracy of a time series foundation model across diverse real-world and synthetic datasets. It probes the model's ability to capture temporal dynamics, periodicity, and multi-scale patterns over a fixed context window to predict future values. Use when the user wants to benchmark on ETT1, ETT2, Exchange Rate, M1 Monthly, M1 Quarterly, M1 Yearly, M5, Monash M3, NN5, Traffic, Weather, M4 Monthly, Entsoe, Solar with Weather, UK Covid, Sensor Data, or asks about evaluat...Votes: 0GitHub stars: 3
- Timetom EvalEvaluates Large Language Models' Theory of Mind (ToM) reasoning capabilities across reading comprehension and interactive dialogue scenarios. It specifically probes the model's ability to track character beliefs over time, assess answerability, and determine information access, with a strong focus on first-order and higher-order (up to third-order) belief reasoning. Use when the user wants to benchmark on ToMI, BigToM, FanToM, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3