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Claude Skills by qhjqhj00
github.com/qhjqhj007,572 skills29 installs10,436 views
- Kdd99 Intrusion Detection EvalEvaluates network intrusion detection models by measuring per-class detection rates and false positive rates across normal and attack traffic categories. Probes the classifier's ability to balance sensitivity to rare attack types while minimizing misclassification of benign traffic. Use when the user wants to benchmark on KDD99, or asks about evaluating this task. Reports Detection Rate.Votes: 0GitHub stars: 3
- Kddcup1999 Iot EvalEvaluates supervised machine learning classifiers for anomaly detection in IoT network traffic, specifically probing their ability to identify intrusion attack categories under severe class imbalance. Use when the user wants to benchmark on KDD Cup 1999, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Kdudzic CharmatchCompute kdudzic/charmatch via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of kdudzic/charmatch.Votes: 0GitHub stars: 3
- Kencorpus Qa EvalEvaluates machine reading comprehension and question answering on low-resource Kiswahili. It tests a model's ability to read short stories and accurately extract or generate answers to posed questions. The protocol uses a held-out test split to measure token-level overlap and exact string matching against ground truth answers. Use when the user wants to benchmark on KenSwQuAD, or asks about evaluating this task. Reports F1 score.Votes: 0GitHub stars: 3
- Kendall TargetEvaluates whether a reduced subset of benchmark tests preserves the relative performance ranking of software variants compared to a full test suite. It probes the fidelity of black-box performance comparisons when benchmark execution costs are minimized. Use when the user has predictions and gold and needs to compute Kendall target.Votes: 0GitHub stars: 3
- KendallrankcorrcoefCompute the KendallRankCorrCoef metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute KendallRankCorrCoef, or asks how to score with KendallRankCorrCoef.Votes: 0GitHub stars: 3
- KendalltauCompute the kendalltau metric — provided by scipy.stats. Use when the user has predictions and ground-truth and needs to compute kendalltau, or asks how to score with kendalltau.Votes: 0GitHub stars: 3
- Kernelbench EvalEvaluates large language models' ability to generate functionally correct and hardware-efficient CUDA kernels for PyTorch workloads. It probes the models' capacity for low-level systems programming, hardware-aware optimization, and debugging execution/functional errors under one-shot prompting. Use when the user wants to benchmark on KernelBench, or asks about evaluating this task. Reports fast_p.Votes: 0GitHub stars: 3
- Kevinspaghetti AccuracykCompute KevinSpaghetti/accuracyk via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of KevinSpaghetti/accuracyk.Votes: 0GitHub stars: 3
- Keyinst EvalThis evaluation probes a model's ability to formulate correct SQL queries from natural language questions, specifically focusing on capturing structural semantics like GROUP BY, HAVING, ORDER BY, and set operations. It measures how well prompt engineering techniques or fine-tuning improve SQL generation accuracy across different database schemas and question complexities. Use when the user wants to benchmark on StrucQL, Spider, Bird, or asks about evaluating this task. Reports execution accur...Votes: 0GitHub stars: 3
- Keyword Spotting Efficiency EvalEvaluates the energy efficiency and inference speed of various hardware platforms (CPU, GPU, neuromorphic chips) running a keyword spotting neural network on audio data. It measures how power consumption and latency scale with network size and batch configuration. Use when the user wants to benchmark on Keyword Spotting Dataset, or asks about evaluating this task. Reports energy cost per inference (J).Votes: 0GitHub stars: 3
- Kfineval Pilot EvalEvaluates Korean financial language models across three core capabilities: factual knowledge recall, multi-step legal/financial reasoning, and safety alignment against adversarial toxic prompts. It probes domain-specific understanding, procedural reasoning, and robustness to financial fraud or privacy-violating queries. Use when the user wants to benchmark on KFinEval-Pilot, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Kg Benchmark EvalEvaluates the query execution performance and scalability of various knowledge graph systems across e-commerce, academic, and digital twin domains. It measures how efficiently triple stores and property graphs handle subsumption, recursive queries, and large-scale RDF datasets under realistic workload conditions. Use when the user wants to benchmark on BSBM, LUBM, DTBM, or asks about evaluating this task. Reports average response time (seconds).Votes: 0GitHub stars: 3
- Kg Vip EvalEvaluates multi-modal LLMs' ability to perform visual question answering by grounding visual inputs with external knowledge graphs. It probes the model's capacity for multi-hop reasoning, visual perception, and knowledge retrieval-augmented generation. Use when the user wants to benchmark on FVQA 2.0+, MVQA, or asks about evaluating this task. Reports LLM-J.Votes: 0GitHub stars: 3
- Kgqa EvalThis benchmark evaluates the ability of conversational AI models and traditional knowledge graph question-answering systems to accurately answer natural language questions over structured knowledge graphs. It probes factual grounding, recall on exhaustive lists, robustness to linguistic variations, and determinism across general and academic domains. Use when the user wants to benchmark on QALD-9, YAGO, DBLP, MAG, or asks about evaluating this task. Reports Micro F1 score.Votes: 0GitHub stars: 3
- Kgqa4mat EvalEvaluates a model's ability to translate natural language questions into correct graph database queries (Cypher or SPARQL) for knowledge graph question answering. It probes the model's capacity for logical reasoning, schema understanding, and formal language generation in both a domain-specific materials science setting and a general-domain multilingual benchmark. Use when the user wants to benchmark on KGQA4MAT, QALD-9, or asks about evaluating this task. Reports F1-score.Votes: 0GitHub stars: 3
- Kgquiz EvalEvaluates large language models' ability to store, retrieve, and reason over factual knowledge encoded in parametric memory across five progressively complex tasks. It probes basic fact verification, multiple-choice discrimination, open-ended entity generation, multi-hop factual editing, and comprehensive entity description generation. It measures how well models generalize encoded knowledge across commonsense, encyclopedic, and biomedical domains under increasing reasoning complexity. Use wh...Votes: 0GitHub stars: 3
- Khaliq88 Execution AccuracyCompute Khaliq88/execution_accuracy via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of Khaliq88/execution_accuracy.Votes: 0GitHub stars: 3
- Kidney Histopathology EvalEvaluates histopathology foundation models on kidney-specific downstream tasks, including tile-level morphological classification, molecular information estimation, and slide-level diagnostic/prognostic inference across diverse staining protocols (H&E, PAS, PASM, IHC). Use when the user wants to benchmark on Kidney Digital Pathology Benchmark, or asks about evaluating this task. Reports Matthews Correlation Coefficient (MCC).Votes: 0GitHub stars: 3
- Kilian Group Arxiv ScoreCompute kilian-group/arxiv_score via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of kilian-group/arxiv_score.Votes: 0GitHub stars: 3
- Kilt EvalEvaluates a model's ability to perform knowledge-intensive language tasks by jointly assessing output generation accuracy and evidence retrieval from a fixed Wikipedia snapshot. It measures how well models can produce correct answers while providing verifiable text-span provenance to justify predictions. Use when the user wants to benchmark on KILT, or asks about evaluating this task. Reports KILT scores.Votes: 0GitHub stars: 3
- Kimi Audio EvalEvaluates an audio foundation model's capabilities across automatic speech recognition, general audio understanding, audio-to-text conversational reasoning, and end-to-end speech conversation. Use when the user wants to benchmark on LibriSpeech, FLEURS, AISHELL-1, AISHELL-2, WenetSpeech, Kimi-ASR Internal Testset, MMAU, ClothoAQA, VocalSound, Nonspeech7k, MELD, TUT2017, CochlScene, OpenAudioBench, VoiceBench, or asks about evaluating this task. Reports Word Error Rate (WER).Votes: 0GitHub stars: 3
- Kimi K1.5 Benchmark EvalEvaluates multimodal reasoning, coding, and instruction-following capabilities across text, code, and vision tasks using a standardized suite of academic benchmarks. Use when the user wants to benchmark on MMLU, IF-Eval, CLUEWSC, C-EVAL, HumanEval-Mul, LiveCodeBench, Codeforces, AIME 2024, MATH-500, MMMU, MATH-Vision, MathVista, or asks about evaluating this task. Reports exact-match accuracy (EM).Votes: 0GitHub stars: 3
- Kinect Action Recognition EvalEvaluates the robustness of Kinect-based action recognition algorithms across single-view and cross-view scenarios. Probes how well models handle viewpoint variation, motion variability, and different sensor modalities (depth, skeleton, RGB-D) on standardized benchmarks. Use when the user wants to benchmark on MSRAction3D Dataset, 3D Action Pairs Dataset, Cornell Activity Dataset (CAD-60), UWA3D Single View Dataset, UWA3D Multiview Dataset, or asks about evaluating this task. Reports average ...Votes: 0GitHub stars: 3
- Kiracurrie22 PrecisionCompute kiracurrie22/precision via the HuggingFace `evaluate` library. Use when the user has predictions + references and wants the canonical implementation of kiracurrie22/precision.Votes: 0GitHub stars: 3
- Kitchen Sink Anomaly Detection EvalEvaluates the robustness and sensitivity of various jet substructure feature sets (Energy Flow Polynomials, subjettiness, and their combination) for model-agnostic resonant anomaly detection in high-energy physics dijet events. It compares performance across Ideal Anomaly Detection (IAD) and CWoLa hunting setups using multiple Beyond Standard Model signal topologies. Use when the user wants to benchmark on LHCO & BSM dijet signals, or asks about evaluating this task. Reports max(SIC), sigma_0...Votes: 0GitHub stars: 3
- Kits21 Segmentation EvalThis benchmark evaluates the ability of deep learning models to perform multi-organ and multi-lesion semantic segmentation on 3D medical imaging data. It specifically probes a model's capacity to accurately delineate kidneys, renal tumors, and renal cysts from corticomedullary-phase CT scans, testing both volumetric overlap and boundary precision. Use when the user wants to benchmark on KiTS21, or asks about evaluating this task. Reports dice.Votes: 0GitHub stars: 3
- Kitsune Iot Nids EvalEvaluates online unsupervised anomaly detection systems for network intrusion detection on IoT surveillance and network traffic. It measures how well models distinguish between normal traffic and various attack types (e.g., DoS, MITM, malware) using streaming packet features. Use when the user wants to benchmark on Kitsune IoT Network Datasets, or asks about evaluating this task. Reports AUC.Votes: 0GitHub stars: 3
- Kitti Depth EvalEvaluates self-supervised monocular depth estimation models on outdoor driving scenes, measuring both the geometric accuracy of predicted depth maps and the reliability of associated uncertainty estimates. Use when the user wants to benchmark on KITTI, or asks about evaluating this task. Reports Abs Rel.Votes: 0GitHub stars: 3
- Kitti Depth Flow Pose EvalEvaluates a model's ability to jointly estimate monocular depth, optical flow, and camera ego-motion from consecutive video frames in driving scenes. It probes geometric consistency, motion handling, and self-supervised learning robustness on standard autonomous driving benchmarks. Use when the user wants to benchmark on KITTI Raw, KITTI Flow 2012, KITTI Flow 2015, KITTI Odometry, KITTI Eigen Split, or asks about evaluating this task. Reports EPE.Votes: 0GitHub stars: 3
- Kitti Eigen Depth EvalEvaluates the accuracy of self-supervised monocular depth estimation models on urban driving scenes. It probes the model's ability to predict per-pixel depth from a single image or video sequence, handling occlusions, moving objects, and scale ambiguity. Use when the user wants to benchmark on KITTI 2015 (Eigen split), Make3D, or asks about evaluating this task. Reports Abs Rel, δ < 1.25.Votes: 0GitHub stars: 3
- Kitti Fc EvalEvaluates the robustness of optical flow estimation models when subjected to various digital, illumination, weather, noise, and blur corruptions. It measures both absolute performance degradation and relative robustness compared to clean data across in-domain and out-of-domain training settings. Use when the user wants to benchmark on KITTI-FC, or asks about evaluating this task. Reports EPE.Votes: 0GitHub stars: 3
- Kitti Lidar Flow EvalThis evaluation probes a model's ability to estimate dense optical flow directly from sparse, noisy LiDAR range scans without using RGB images. It measures prediction accuracy against real-world ground truth flow maps and evaluates robustness to occlusions and foreground/background motion. Use when the user wants to benchmark on KITTI Tracking & Flow 2015, or asks about evaluating this task. Reports EPE (End-Point-Error).Votes: 0GitHub stars: 3
- Kitti Optical Flow EvalEvaluates the accuracy of unsupervised optical flow estimation methods on standard driving scenes. It measures the pixel-wise displacement error between predicted and ground-truth flow fields to quantify estimation quality. Use when the user wants to benchmark on KITTI2012, or asks about evaluating this task. Reports EPE.Votes: 0GitHub stars: 3
- Kitti Speed Estimation EvalEgo-vehicle longitudinal speed estimation from monocular video sequences. It probes a model's ability to infer real-world velocity by combining optical flow magnitude and monocular depth/disparity cues over time. Use when the user wants to benchmark on KITTI, or asks about evaluating this task. Reports RMSE.Votes: 0GitHub stars: 3
- Kitti Subt Vo Depth EvalEvaluates unsupervised monocular visual odometry and depth estimation methods on challenging driving and subterranean environments. Probes the model's ability to predict consistent 6-DoF ego-motion and recover accurate depth maps without ground-truth supervision. Use when the user wants to benchmark on KITTI, DARPA Subterranean Challenge, or asks about evaluating this task. Reports relative translation error ($t_{err}$), relative rotation error ($r_{err}$).Votes: 0GitHub stars: 3
- Kl ReductionProbes the statistical alignment between a candidate pretraining dataset and a target reference distribution (e.g., The Pile or Wikipedia/books). It quantifies how well the dataset's hashed n-gram frequencies match the desired language model pretraining distribution, serving as a proxy for downstream pretraining performance. Use when the user has predictions and gold and needs to compute KL reduction.Votes: 0GitHub stars: 3
- KldivergenceCompute the KLDivergence metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute KLDivergence, or asks how to score with KLDivergence.Votes: 0GitHub stars: 3
- Klip Postprocessing EvalEvaluates the accuracy and computational efficiency of PCA-based PSF subtraction algorithms for high-contrast astronomical imaging. Specifically, it measures how well the algorithm mitigates speckle noise while recovering planetary signals compared to a reference implementation. Use when the user wants to benchmark on Beta Pictoris ($\beta$ Pic), HR8799, or asks about evaluating this task. Reports SNR.Votes: 0GitHub stars: 3
- Klue Dp EvalEvaluates a model's ability to predict syntactic dependency relations between words in Korean sentences, testing grammatical structure understanding. Use when the user wants to benchmark on KLUE-DP, or asks about evaluating this task. Reports LAS.Votes: 0GitHub stars: 3
- Klue Dst EvalEvaluates a model's ability to track and update dialogue state across turns in Korean conversations, testing multi-turn reasoning and slot filling. Use when the user wants to benchmark on KLUE-DST, or asks about evaluating this task. Reports Joint Accuracy.Votes: 0GitHub stars: 3
- Klue Ner EvalEvaluates a model's ability to identify and classify named entities (e.g., person, location, organization) within Korean text, testing token-level understanding. Use when the user wants to benchmark on KLUE-NER, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Klue Re EvalTests a model's ability to extract relational triples between entities in Korean text, probing structured information extraction capabilities. Use when the user wants to benchmark on KLUE-RE, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Klue Tc EvalEvaluates a model's ability to classify Korean text into predefined topic categories, testing core semantic understanding and categorization capabilities in Korean. Use when the user wants to benchmark on KLUE-TC, or asks about evaluating this task. Reports Accuracy.Votes: 0GitHub stars: 3
- Kmmlu EvalThis benchmark evaluates large language models' ability to understand and answer expert-level multiple-choice questions in Korean across diverse academic domains. It specifically probes cultural and linguistic alignment, testing whether models can handle native-language nuances and localized knowledge without relying on translated or English-centric training data. Use when the user wants to benchmark on KMMLU, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Kmmmu EvalEvaluates multimodal understanding in Korean language and context across nine academic disciplines. Probes localized knowledge recall, discipline-specific conventions, and the ability to map visual and textual cues to correct answers in Korean institutional settings. Use when the user wants to benchmark on KMMMU, or asks about evaluating this task. Reports accuracy.Votes: 0GitHub stars: 3
- Knesset Corpus Extraction EvalEvaluates the accuracy and robustness of an automated extraction pipeline that converts raw Hebrew parliamentary documents into structured metadata, speaker lists, and text. It probes the system's ability to correctly parse dates, identify speakers, extract sentences, and match speaker names to an official database of Knesset members. Use when the user wants to benchmark on Knesset Corpus, or asks about evaluating this task. Reports success_rate.Votes: 0GitHub stars: 3
- Knight Knave EvalEvaluates whether large language models rely on memorization versus genuine logical reasoning by measuring performance drops on logically equivalent but locally perturbed Knights and Knaves puzzles. It probes the model's ability to maintain consistent logical deductions when superficial or structural elements of the problem are altered. Use when the user wants to benchmark on Knights and Knaves (K&K), or asks about evaluating this task. Reports LiMem.Votes: 0GitHub stars: 3
- Knowcusbench EvalEvaluates a model's ability to bind natural language knowledge to visual concepts for high-fidelity image reconstruction and customized generation without full retraining. It probes cross-modal knowledge transfer, concept fidelity, and prompt alignment in diffusion-based image generation. Use when the user wants to benchmark on KnowCusBench, or asks about evaluating this task. Reports CLIP-I-Seg.Votes: 0GitHub stars: 3
- Knowgen EvalEvaluates an agentic search agent's ability to gather external knowledge and reference images to enhance text-to-image generation for knowledge-intensive, real-world prompts. It measures how well the agent's search-grounded prompts improve visual correctness, text accuracy, faithfulness, and aesthetics compared to direct generation. Use when the user wants to benchmark on KnowGen, or asks about evaluating this task. Reports K-Score.Votes: 0GitHub stars: 3