Claude Skills by ADu2021

Categorize ML/AI research papers into 11 types based on their title and abstract. Returns structured JSON with a primary category, optional secondary categories, extractability rating, and rationale. Designed for the SkillXiv paper2skill pipeline as a triage step. Use this skill whenever the user wants to classify, categorize, sort, or triage research papers — whether a single paper or a batch. Also trigger when someone asks "what kind of paper is this?", wants to filter papers by type, or ne...

Convert arXiv papers that apply ML techniques to real-world domains into application-transfer skills. Extracts problem formulation, domain adaptation gaps, and deployment recipes. Use this skill when extracting skills from Category 1 (Application Transfer) papers — papers about AlphaFold-style domain applications, robotics deployment, interdisciplinary ML crossings, or any paper where the novelty is in what was solved rather than the method itself.

Convert component innovation papers into drop-in replacement guides. Extracts what was swapped, why, conditions for when it helps, and the performance delta. Use this skill when extracting skills from Category 5 (Component Innovation) papers — BatchNorm-style papers, ResNet skip connections, new loss functions, or any paper proposing one elegant modification with outsized impact.

Convert dataset and benchmark papers into evaluation infrastructure skills. For datasets: extracts collection protocol, annotation design, quality control. For benchmarks: extracts task definition, metric selection, leaderboard design. Use this skill when extracting skills from Category 2 (Evaluation Infrastructure) papers — ImageNet-style dataset papers, SWE-bench-style benchmark papers, or any paper whose primary contribution is evaluation methodology.

Convert foundational papers that create new subfields into conceptual framework skills. Extracts problem definitions, vocabulary, founding experiments, and opened research directions. Use this skill when extracting skills from Category 8 (Field Foundation) papers — MAML-style paradigm-creating papers or 'Deep Learning' review-style papers that define entire research communities.

Convert insight-driven papers into minimal reproducible recipes built around a single non-obvious observation. Extracts the key insight, why the problem seemed hard, and the minimal implementation. Use this skill when extracting skills from Category 6 (Insight-Driven) papers — 'Learning to See in the Dark'-style papers where one clever observation unlocks a seemingly hard problem.

Convert mechanistic analysis papers into transferable analytical methodology skills. Extracts the research question, analytical instrument, controlled confounds, and practitioner implications. Use this skill when extracting skills from Category 9 (Mechanistic Analysis) papers — Network Dissection-style interpretability work or any paper whose goal is exploratory understanding of why something works.

Convert papers that disprove conventional wisdom into paradigm-challenge skills. Extracts the prior belief, the falsifying experiment, and the revised principle. Use this skill when extracting skills from Category 3 (Paradigm Challenge) papers — papers that say 'rethinking', 'revisiting', or 'do we really need X', where the core move is adversarial (proving the community wrong).

Convert research infrastructure papers into design pattern guides. Extracts capability gaps addressed, API design decisions, performance/usability trade-offs, and integration patterns. Use this skill when extracting skills from Category 7 (Research Infrastructure) papers — PyTorch-style framework papers, evaluation harness tooling, or any paper where the tool itself is the contribution.

Convert scaling and efficiency papers into practical resource planning guides. Extracts empirical scaling laws, compute-optimal allocation rules, and budget-performance trade-offs. Use this skill when extracting skills from Category 11 (Scaling and Efficiency) papers — Chinchilla-style scaling law papers, Flash Attention efficiency papers, or knowledge distillation studies.

Convert survey and synthesis papers into field navigation guides. Extracts taxonomies, method selection decision trees, literature navigation heuristics, and open problems. Use this skill when extracting skills from Category 10 (Survey and Synthesis) papers — comprehensive reviews, position papers, tutorials, or roadmaps that organize a research landscape.

Convert systematic empiricism papers into ranked practitioner checklists. Extracts implementation tricks, hyperparameter findings, and design choice ablations with conditions of applicability. Use this skill when extracting skills from Category 4 (Systematic Empiricism) papers — '37 PPO details'-style papers, hyperparameter studies, or ablation-heavy guides that systematize scattered knowledge.

Convert arXiv and ML/AI research papers into ready-to-use Claude agent skills in seconds — so anyone can apply cutting-edge techniques without reading the full paper. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, create a paper-to-skill pipeline, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting...

Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill lib...

Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operational complexity. We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation. We frame classification as representation selection over the full token-layer hidden-state tensor, rather than committing to a fixed token or fixed lay...

The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we i...

Asymmetric Confidence-aware Error Penalty (ACE) dynamically penalizes overconfident mistakes in RL training, improving reasoning quality without requiring additional computation.

Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.

Train a single LLM to decompose complex queries into subquestions and integrate retrieved contexts through two-stage supervised and preference-based reinforcement fine-tuning, achieving 7.6% average improvement and matching 685B models with 32B parameters.

Reduce memory overhead of long-horizon LLM agents by learning task-specific context compression strategies. A learnable compressor adapts by analyzing failure cases, achieving 26-54% memory reduction while preserving 95%+ accuracy, enabling smaller models to act as efficient long-context agents.

Compress agent interaction histories and environment observations through natural language guideline optimization, reducing token usage by 26-54% while preserving 95%+ accuracy. Use for cost/latency reduction in multi-step agent tasks.

Vision-Language-Action (VLA) models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model (VLM) embeddings. Recent advancements have introduced explicit intermediary reasoning, such as sub-task prediction (language) or goal image synthesis (vision), to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in th...

Route queries to specialized reasoning modes (internal reasoning, tool calling, or instant answers) using task-aware routing and Adaptive Policy Optimization to reduce inference costs by 45% while maintaining accuracy.

Mitigate long-tail distribution problems in VLM training data through adaptive rebalancing and diffusion-based synthesis. Uses entity distribution analysis to identify head/tail imbalance and applies targeted data augmentation, improving LLaVA 1.5 performance by 4.36% without increasing training data volume.

Accelerate LLM inference 1.5-2x using a universal draft model that adapts to user data in real-time, handling different target models and tokenizers via online n-gram cache and hybrid distillation.

Adaptively prune visual tokens from vision encoders by reconstructing discarded features from retained ones, reducing computational cost by 50% while maintaining task performance on OCR and image understanding tasks.

Generate adversarial test suites that catch LLM code errors by analyzing constraint differences between correct and incorrect solutions. SAGA framework improves test detection rate 9.55% and verifier accuracy 12.14% by using human bug patterns and multi-input differential analysis.

Combines adversarial training with optimal transport constraints enabling deterministic, transport-optimal one-step image generation with FID 2.38 on ImageNet-256. Apply when you need fast generative models without teacher-student distillation complexity.

Uncover and fix reward hacking vulnerabilities in LLM-based judges. Simple tokens like punctuation or generic reasoning phrases trigger false positive rewards without substantive content. Defend using data augmentation with truncated model outputs as adversarial negatives, creating robust Master Reward Models resistant to superficial inputs.

Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single agentic capability, failing to capture long-horizon real-world scenarios. Moreover, the reliance on human-in-the-loop feedback for realistic tasks creates a scalability bottleneck, hindering automated rollout collection and evaluation. To bridge this gap, we introduce AgencyBench, a comprehensive be...

Transition from simple LLM-based evaluation to agentic judges that employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory. Survey of sophisticated evaluation paradigms for complex, specialized, and multi-step assessment tasks across diverse domains.

Optimize multi-agent collaboration by learning task-specific interaction topologies. Use an LLM orchestrator to generate layered DAG topologies that adapt to inferred problem difficulty, treating agent interactions as a learned graph structure rather than fixed patterns.

Standardizes agent training data representation across diverse sources (API use, web browsing, coding, software engineering). Single lightweight protocol unifies 13 datasets enabling 20% performance gains without domain-specific tuning. Enables reproducible agent training and scalable data combination.

Bridge imitation learning and experience-driven RL by collecting state-based supervision from agents' own actions. Trigger: improve agent generalization when expert demonstrations are limited and environments lack dense rewards.

Enables web agents to handle long-horizon tasks by actively managing context workspace. Implements granular condensations of recent steps and deep consolidations of multi-step sub-tasks, preventing context saturation. Achieves 36.2% on BrowseComp with 30B model, matching larger proprietary agents.

Train RL on diverse agent frameworks (LangChain, AutoGen, custom) via unified data interface and transition-based RL decomposition.

Build controllable benchmarks for evaluating long-context agents using environment rollouts. Generate diverse multi-step agent tasks that require maintaining context across extended interaction sequences, enabling evaluation of agent reasoning quality in scenarios with long history requirements.

Compress agent interaction history by converting observation-action sequences into compact visual representations (images), leveraging visual tokens' superior information density. Implements segment optical caching with 20x rendering speedup and enables dynamic compression rates. Preserves over 95% of agent performance while reducing token consumption by 50%+, enabling agents to maintain longer interaction histories within fixed budgets.

Train LLM-based agents with end-to-end RL by extending MDPs to handle tool invocation and environmental stochasticity—enable dense process rewards for intermediate steps and masked policy gradients for learnable actions.

Build multi-faceted reward models for agent trajectories that provide structured feedback on intermediate reasoning quality. Implement explicit reasoning traces, focused critiques with refinement guidance, and overall process scores to train more effective agentic agents without relying solely on sparse outcome rewards.

Empirically analyzes 31,132 agent skills to identify 14 distinct vulnerability patterns, finding 26.1% contain security flaws including data exfiltration, privilege escalation, and malicious intent risks that require mandatory vetting.

Implements The Agent's First Day from arXiv:2601.08173

Automatically synthesize executable RL training environments with database backends, Python tools, and task descriptions. Generate 1000+ diverse domains with 10K+ tasks enabling data-efficient tool-use agent training without manual scenario design.

Train agents from scratch without human-annotated data via symbiotic competition—curriculum agent proposes progressively harder tasks while executor agent learns to solve them, creating autonomous self-reinforcing loops.

Enable vision-language agents to self-evolve by grounding verification in tool outputs rather than text: implement nested loops where Solver+Verifier generate trajectories and tool-based feedback, then optimize via GRPO using self-generated rewards without external supervision.

Enable research agents to interleave evidence-based drafting with reasoning-driven deepening, automatically expanding outlines based on discovered gaps, using trajectory pruning for efficient RL training.

Decompose agent work across four specialized modules (planner, executor, verifier, generator) coordinated via evolving memory. Use Flow-GRPO to convert multi-turn sparse-reward optimization into sequential single-turn updates with outcome broadcasting, achieving 4-15% accuracy gains on benchmarks while scaling better than monolithic agent policies.

Enable agent learning through episodic memory and neural case selection without fine-tuning the underlying LLM, achieving efficient continual adaptation via policy updates in memory space.

Diagnose and correct overconfidence failures in autonomous agents using Holistic Trajectory Calibration (HTC), analyzing process-level features across entire execution paths. Use when building reliable autonomous systems that need better confidence estimates and reduced overconfidence failures.

Enable language models to improve via context adaptation rather than weight updates. Use ACE (Agentic Context Engineering) to treat contexts as evolving playbooks that accumulate, refine, and organize strategies through modular generation, reflection, and curation processes. Achieve +10.6% agent benchmark gains and +8.6% on finance tasks using small open-source models matching production-level performance.