Claude Skills by thedixitjain

'Configure distributed training setup operations. Auto-activating skill for ML Training. Triggers on: distributed training setup, distributed training setup Part of the ML Training skill category. Use when working with distributed training setup functionality. Trigger with phrases like \"distributed training setup\", \"distributed setup\", \"distributed\". '

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.

Use when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist, then as a public post-acceptance ACL Anthology release, with attention to licensing, dataset documentation, and reproduction instructions.

Use when drafting an EACL author response during the ACL Rolling Review rebuttal window, covering how to triage reviewer points, address the action editor who writes the meta-review, answer NLP objection patterns, run in-window experiments, flag deficient reviews, and decide respond-now versus revise-for-a-later-venue given EACL's single cycle.

Use when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance floors, human-evaluation agreement, data-contamination controls, ablations, and error taxonomies, so that every stated result is measured rather than asserted.

'Manage early stopping callback operations. Auto-activating skill for ML Training. Triggers on: early stopping callback, early stopping callback Part of the ML Training skill category. Use when working with early stopping callback functionality. Trigger with phrases like \"early stopping callback\", \"early callback\", \"early\". '

Use when planning the reproducibility/artifact story for an ECAI paper — noting that ECAI/FAIA has NO ACM/IEEE-style artifact-badging committee, so credibility is carried by the paper and its supplement and judged by the same reviewers, and adapting an ML-style reproducibility package (or a complete proof appendix for theory work) to that reality.

Use when packaging code, models, and data around an ECCV paper — the anonymous review-time archive under the trailing supplement deadline, the do-not-cite-your-own-repo rule, and the June-to-September post-acceptance runway for a public release aligned with the ECVA and Springer copies of the paper.

Use when designing or auditing the experimental program of an ECCV paper — benchmark selection that survives a September conference, matched-substrate baseline fairness in the foundation-model era, ablations that isolate the claimed mechanism, qualitative failure evidence, and run sequencing toward a March freeze.

Use when preparing an EDBT database-systems artifact and reproducibility package, covering what evaluators check first for systems papers, a turnkey run path, pinned workloads and environments, DOI-issuing archival for the open-access OpenProceedings record, and the higher stakes for Experiments & Analysis papers.

Use when packaging the artifacts of an EMNLP paper — datasets, annotation guidelines, prompts, evaluation code, and model outputs — as anonymous review-time evidence or public post-acceptance releases, with licensing, data statements, and the inspection order NLP reviewers actually follow.

Use when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers correctly, tracing dataset and benchmark ancestry, verifying every reference resolves under the hallucinated-citation policy, and keeping self-citation double-blind.

Use when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the contribution to EMNLP's welcomed paper types including negative results and reproductions, and choosing between main conference, Findings, industry, and demo pipelines.

Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.

Use when targeting European Conference on Computer Vision (ECCV) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for computer vision flagship.

Use when targeting European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for AI/data mining.

Use when preparing a EuroSys artifact for the sysartifacts-run evaluation — choosing among the Available, Functional, and Reproduced badges, timing the post-notification artifact submission, building for an evaluator on foreign hardware, and aiming at the Gilles Muller Best Artifact Award rather than a minimal pass.

Use when designing or auditing the evaluation of a EuroSys paper — choosing workloads that earn the word realistic, tuning baselines beyond their defaults, decomposing end-to-end wins into per-mechanism gains, measuring overheads and worst cases, and sizing experiments to the claims the paper actually makes.

Quality and performance evaluation with baseline comparison. Sub-modes: correctness, performance, quality, regression. Outputs PASS/WARN/FAIL per dimension. Use for pre-ship evaluation or regression checks.

'Build this skill allows AI assistant to evaluate machine learning models using a comprehensive suite of metrics. it should be used when the user requests model performance analysis, validation, or testing. AI assistant can use this skill to assess model accuracy, p... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '

This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.

Provides weighted scoring, rubrics, and decision-threshold patterns. Use when designing quality gates, evaluation systems, or decision frameworks.

PluginEval quality methodology — dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when calibrating scoring thresholds for your marketplace, or when explaining quality badges to external partners like Neon.

Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time.

Use when an Experimental Economics (ExpEcon) result may be a power artifact, multiple-comparisons artifact, or sensitive to the inference unit, exclusions, or design choices. Hardens the statistical case; it does not design the experiment or draft prose.

Choose the right product experiment type: superiority, non-inferiority, equivalence, A/B/n, or holdback-backed validation. Use when deciding what kind of A/B test to run, when the question is not simply \"is variant better,\" when validating no degradation, proving similarity, comparing multiple variants, or selecting an experiment design for a mature product.

'Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '

Use when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment documentation, and released code/data — since FAccT's culture centers documentation and accountability infrastructure rather than a formal ACM artifact-badging track; covers what makes each genre credible, anonymized-review versus public-release versions, and consistency with the paper's harm claims.

Use when packaging a USENIX FAST artifact for the USENIX Artifact Evaluation scheme (Artifacts Available, Artifacts Functional, Results Reproduced), covering what a storage AEC checks first, DOI-issuing archives, the artifact appendix, and the special challenges of storage artifacts that need specific devices, large traces, or long endurance runs on the separate post-acceptance timeline.

Use when planning a USENIX FAST project timeline from venue fit through choosing a Spring or Fall deadline, double-blind submission, the author-response period, shepherding or a one-shot revision, artifact evaluation, and the open-access camera-ready, with backward-planning offsets tuned to storage evaluation and honest handling of the two-deadline cycle.

'Configure fastapi ml endpoint operations. Auto-activating skill for ML Deployment. Triggers on: fastapi ml endpoint, fastapi ml endpoint Part of the ML Deployment skill category. Use when working with APIs or building integrations. Trigger with phrases like \"fastapi ml endpoint\", \"fastapi endpoint\", \"fastapi\". '

'Configure with feature engineering helper operations. Auto-activating skill for ML Training. Triggers on: feature engineering helper, feature engineering helper Part of the ML Training skill category. Use when working with feature engineering helper functionality. Trigger with phrases like \"feature engineering helper\", \"feature helper\", \"feature\". '

'Analyze feature importance analyzer operations. Auto-activating skill for ML Training. Triggers on: feature importance analyzer, feature importance analyzer Part of the ML Training skill category. Use when analyzing or auditing feature importance analyzer. Trigger with phrases like \"feature importance analyzer\", \"feature analyzer\", \"analyze feature importance r\". '

TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF.

Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.

Fine-tune models on Azure AI Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents...

Read, inspect, and write Flow Cytometry Standard (FCS) 2.0, 3.0, and 3.1 files with FlowIO. Use for low-level FCS metadata and channel inspection, NumPy event extraction, multi-dataset files, table export, and FCS 3.1 creation; use FlowKit for compensation, cytometry transforms, gating, or FlowJo workspaces.

Use when planning the durable evidence objects around a FOCS (IEEE Symposium on Foundations of Computer Science) paper — a venue with no artifact track where the public arXiv/ECCC full version is the artifact of record, plus proof certificates, checker code, and the IEEE Xplore version's supporting role.

Use when deciding whether and how computation appears in a FOCS (IEEE Symposium on Foundations of Computer Science) paper — a venue that accepts on theorems with no evaluation section expected — covering machine-verified case analyses, computer-discovered constructions, and honest illustrative plots.

'Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '

'Reviews contracts from a freelancer''s perspective across 14 evaluation lenses including misclassification risk, IP ownership, payment terms, kill fees, and non-compete scope. Use when a freelancer or independent contractor needs to evaluate a client agreement. Trigger with \"/freelancer-review\" or \"review this contract as a freelancer\". '

Use when packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what SIGSOFT evaluators check first, DOI-issuing archives, evaluator-proof documentation, and the separate post-acceptance artifact deadline.

Use when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and mixed-methods rigor, contamination-aware LLM ablations, provenance for mining studies, and matching evidence to the shape of each software-engineering claim.

Walk through a complete GAIA benchmark→submit flow — from key resolution through HAL-compatible package generation

Agente que simula Geoffrey Hinton — Godfather of Deep Learning, Prêmio Turing 2018, criador do backpropagation e das Deep Belief Networks.

Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples...

>- Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).

Use when testing the golden_jupyter_dir golden build

Use when testing the golden_jupyter_kw golden build

Use when testing the golden_jupyter_topics golden build