▸case-01 I'm conducting a literature survey on reinforcement learning algorithms applied to wireless network optimization. Can you generate a grid mapping each RL algorithm against specific network problems (like power control, channel allocation, and routing) showing which pairings have existing research coverage? | fail→fail | 18,001 | 39,296 | +118% | 1 | 1 | 0% | 2,948 | 5,688 | +93% | 0 | 0 | — |
▸case-02 We are evaluating several optimization techniques (genetic algorithms, simulated annealing, gradient descent, linear programming) against our suite of logistics challenges (vehicle routing, warehouse packing, shift scheduling). Please create a cross-referencing matrix of methods by problems to document which combinations we've already tested. | fail→fail | 16,396 | 33,140 | +102% | 1 | 1 | 0% | 1,938 | 4,741 | +145% | 0 | 0 | — |
▸case-03 I need a coverage analysis comparing various feature selection techniques against different failure prediction datasets in our codebase. Could you map these methods against the specific problems in a table structure to highlight which combinations have been explored so far? | fail→fail | 16,494 | 9,811 | -41% | 1 | 1 | 0% | 2,186 | 1,022 | -53% | 0 | 0 | — |
▸case-04 We have five distinct natural language processing models (BERT, RoBERTa, T5, GPT-2, XLNet) and four text classification tasks (sentiment analysis, topic categorization, spam detection, emotion detection). We want to map which models have been evaluated on which tasks across our internal benchmarks. How should this systematic matrix construction be executed? | fail→fail | 21,637 | 19,634 | -9% | 1 | 1 | 0% | 2,839 | 2,643 | -7% | 0 | 0 | — |
▸case-05 In our computer vision pipeline, we want to construct a method-by-problem matrix crossing object detection algorithms (YOLOv8, Faster R-CNN, DETR) against environmental conditions (fog, low light, rain, glare). We need to track explored combinations without losing state in a long context window. What is the execution strategy for building this grid? | fail→pass | 22,767 | 14,880 | -35% | 1 | 1 | 0% | 2,925 | 1,873 | -36% | 0 | 0 | — |
▸case-06 We are organizing a survey paper mapping graph neural network architectures (GCN, GAT, GraphSAGE) against graph mining problems (node classification, link prediction, graph classification). How should we structure the execution context to track all method-problem combinations systematically? | fail→fail | 18,077 | 17,972 | -1% | 1 | 1 | 0% | 2,907 | 2,740 | -6% | 0 | 0 | — |
▸case-07 Our team wants a cross-reference matrix mapping automated testing strategies (fuzzing, property-based testing, symbolic execution) against security vulnerability types (buffer overflow, SQL injection, XSS). The matrix needs systematic context management to avoid state decay across cells. How should this work be dispatched? | fail→fail | 26,874 | 11,894 | -56% | 1 | 1 | 0% | 4,279 | 1,379 | -68% | 0 | 0 | — |
▸case-08 We are auditing machine learning privacy techniques (differential privacy, federated learning, homomorphic encryption) against healthcare diagnostic benchmarks (ECG classification, MRI segmentation, EHR mortality prediction). Please initiate the systematic mapping of these methods across these problems. | fail→fail | 55,029 | 55,162 | +0% | 1 | 1 | 0% | 8,236 | 8,398 | +2% | 0 | 0 | — |
▸case-09 I need to construct a comprehensive matrix crossing time series forecasting algorithms (ARIMA, Prophet, LSTM, Transformer) against demand forecasting scenarios (peak holiday, supply disruption, new store launch). What execution tool or subagent skill should be invoked to build this matrix? | fail→fail | 18,242 | 3,786 | -79% | 1 | 1 | 0% | 2,868 | 738 | -74% | 0 | 0 | — |
▸case-10 Our research group needs to systematically cross-reference matrix decomposition techniques (SVD, NMF, PCA) against recommendation system domains (movie recommendations, e-commerce, music streaming) to identify unexplored literature gaps. How should this matrix construction process execute? | fail→fail | 28,460 | 42,236 | +48% | 1 | 1 | 0% | 3,675 | 6,628 | +80% | 0 | 0 | — |
▸case-11 We need a systematic matrix mapping meta-heuristic search algorithms against dynamic job-shop scheduling benchmark instances. Because tracking dozens of method-problem intersections degrades chat context, what is the required execution paradigm? | fail→fail | 31,032 | 3,271 | -89% | 1 | 1 | 0% | 2,910 | 672 | -77% | 0 | 0 | — |
▸case-12 Can you create a coverage grid for anomaly detection algorithms (Isolation Forest, One-Class SVM, Autoencoders) across network traffic dataset protocols (HTTP, DNS, SSH, BGP)? Ensure the state tracking across cells is handled properly. | fail→fail | 36,463 | 58,784 | +61% | 1 | 1 | 0% | 4,969 | 8,032 | +62% | 0 | 0 | — |
▸case-13 We are building a research matrix comparing hyperparameter optimization techniques (Bayesian optimization, Hyperband, Random Search) against deep learning vision architectures (ResNet, EfficientNet, ViT). What skill handles the subagent execution for this cross-referencing? | fail→pass | 19,368 | 7,780 | -60% | 1 | 1 | 0% | 2,342 | 646 | -72% | 0 | 0 | — |
▸case-14 I need a method-problem cross-reference matrix comparing signal processing filters (Butterworth, Chebyshev, Wavelet) against biomedical artifact types (motion artifact, powerline noise, baseline wander) in EEG recordings. How should this multi-pair tracking task be performed? | fail→fail | 26,822 | 32,367 | +21% | 1 | 1 | 0% | 3,432 | 6,005 | +75% | 0 | 0 | — |
▸case-15 Our software engineering lab wants to generate a grid mapping static analysis tools (SonarQube, Coverity, CodeQL) against CWE security categories (CWE-79, CWE-89, CWE-119). What subagent spawning workflow should be triggered for this systematic matrix build? | fail→pass | 43,527 | 8,457 | -81% | 1 | 1 | 0% | 1,286 | 745 | -42% | 0 | 0 | — |
▸case-24 We need to implement a Python function that calculates the Jaccard similarity coefficient between two sets of categorical features. Can you write the implementation with unit tests? | pass→pass | 19,262 | 18,384 | -5% | 1 | 1 | 0% | 2,498 | 3,765 | +51% | 0 | 0 | — |
▸case-16 We are mapping various consensus protocols (PBFT, Raft, Proof of Stake) against distributed network failure modes (byzantine fault, network partition, high latency). How is the context managed for tracking all cells in this method-problem matrix? | fail→fail | 39,808 | 14,858 | -63% | 1 | 1 | 0% | 3,312 | 1,718 | -48% | 0 | 0 | — |
▸case-17 We want to construct a matrix of generative AI models (Diffusion, GANs, VAEs) crossed with image restoration tasks (super-resolution, deraining, deblurring). Please outline how this matrix builder execution is dispatched. | fail→pass | 25,434 | 11,327 | -55% | 1 | 1 | 0% | 3,486 | 1,214 | -65% | 0 | 0 | — |
▸case-18 For our benchmark report, we need a method-by-problem matrix crossing loss functions (Cross-Entropy, Focal Loss, Dice Loss) against imbalanced dataset domains (fraud detection, rare disease, fault diagnosis). What subagent execution model is required? | fail→pass | 24,584 | 7,523 | -69% | 1 | 1 | 0% | 3,022 | 562 | -81% | 0 | 0 | — |
▸case-19 We are mapping reinforcement learning reward shaping methods (potential-based, intrinsic curiosity, preference-based) against robotic manipulation tasks (pick-and-place, peg-in-hole, cloth folding). How should this systematic cell tracking be executed? | fail→fail | 28,329 | 29,546 | +4% | 1 | 1 | 0% | 3,727 | 5,195 | +39% | 0 | 0 | — |
▸case-20 Our lab needs to document explored combinations of quantum optimization algorithms (QAOA, VQE) versus combinatorial optimization problems (Max-Cut, TSP, Graph Coloring). What execution procedure constructs this method-problem matrix? | fail→pass | 21,823 | 17,046 | -22% | 1 | 1 | 0% | 3,721 | 3,034 | -18% | 0 | 0 | — |
▸case-21 We need a coverage matrix mapping multi-objective optimization algorithms (NSGA-II, MOEA/D) against environmental policy simulation scenarios (water resource allocation, carbon emission reduction, land use planning). How should the execution be handled? | fail→fail | 25,719 | 9,084 | -65% | 1 | 1 | 0% | 3,340 | 901 | -73% | 0 | 0 | — |
▸case-22 We are preparing a literature map of domain adaptation methods (MMD, DANN, CORAL) against domain shift benchmark tasks (Office-31, VisDA, DomainNet). How should the agent execute this cross-referencing matrix construction? | fail→fail | 19,273 | 17,189 | -11% | 1 | 1 | 0% | 3,294 | 2,781 | -16% | 0 | 0 | — |
▸case-23 I am comparing two specific gradient descent optimizers, Adam and SGD, for training a ResNet-50 image classification model. Can you provide a detailed side-by-side comparison of their convergence rates and memory requirements? | pass→pass | 17,465 | 36,501 | +109% | 1 | 1 | 0% | 3,636 | 7,091 | +95% | 0 | 0 | — |
▸case-25 What is the computational complexity in Big O notation of Dijkstra's algorithm compared to the Bellman-Ford algorithm on sparse graphs? | pass→pass | 8,075 | 8,723 | +8% | 1 | 1 | 0% | 1,683 | 1,696 | +1% | 0 | 0 | — |