Claude Skills by thedixitjain

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey.

Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.

Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.

Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions \"set up tracking,\" \"GA4,\" \"Google Analytics,\" \"conversion tracking,\" \"event tracking,\" \"UTM parameters,\" \"tag manager,\" \"GTM,\" \"analytics implementation,\" \"tracking plan,\" \"how do I measure this,\" \"track conversions,\" \"Mixpanel,\" \"Segment,\" \"are my events firing,\" or \"analytics isn't working.\" Use this whenever someone asks how to know if somethi...

'Execute this skill enables AI assistant to perform natural language processing and text analysis using the nlp-text-analyzer plugin. it should be used when the user requests analysis of text, including sentiment analysis, keyword extraction, topic modeling, or ... Use when analyzing code or data. Trigger with phrases like ''analyze'', ''review'', or ''examine''. '

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

Use when targeting International Conference on Artificial Intelligence and Statistics (AISTATS) 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 statistics.

Use when preparing an accepted ASE (IEEE/ACM Automated Software Engineering) paper's tool and data for the Artifact Evaluation track, targeting the ACM Artifacts Available and Artifacts Reusable badges on the track's own deadline, with the badge shown on the paper's front page in both IEEE Xplore and the ACM Digital Library.

Use when designing or auditing the evaluation of an ASE (IEEE/ACM Automated Software Engineering) paper, covering real subject systems, fair runnable tool baselines, task-matched effectiveness metrics, ablations that isolate a learned component, oracle and correctness validation, contamination-aware LLM handling, and provenance for mining.

Use when planning an ASE (IEEE/ACM Automated Software Engineering) research-track campaign backward from the deadline, through abstract registration, the double-anonymous submission, the early-rejection gate, rebuttal, the criteria-bound revision round, artifact evaluation, and the camera-ready in both IEEE Xplore and the ACM Digital Library.

Use when targeting Asian Conference on Computer Vision (ACCV) 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 regional flagship.

Use when targeting Asian Conference on Machine Learning (ACML) 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/ML regional flagship.

Use when preparing an ASPLOS artifact for the post-acceptance evaluation committee — writing the ae.tex Artifact Appendix with software/hardware/dataset dependencies, targeting the Available / Functional / Reproducible badges, archiving on a public repository, and planning the collaborative back-and-forth with evaluators.

Use when designing or auditing the evaluation of an ASPLOS paper — choosing among real silicon, FPGA prototypes, and simulators with cycle-accuracy caveats stated, selecting workload suites and baselines that hold up across three communities, attributing wins via ablation, and reporting energy, area, and overhead honestly.

Use when planning an ASPLOS campaign across the two-deadline cycle — sequencing evidence building, the September 9, 2026 submission gate, author-response windows, the December 21 notification, Major Revision resubmission six weeks later, artifact evaluation, and the April 2027 conference in Crete, with owners per risk.

Use when packaging an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) artifact for the USENIX-lineage evaluation scheme — earning the Artifacts Available, Artifacts Functional, and Results Reproduced badges from the Artifact Evaluation Committee on its separate post-acceptance deadline, with evaluator-proof documentation and a turnkey path to the paper's numbers.

Use when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting, workload realism, and honest cost accounting.

Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

| Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: \"azure-ai-ml\", \"MLClient\", \"workspace\", \"model registry\", \"training jobs\", \"datasets\".

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

使用此技能测量性能基线，检测PR前后的回归，并比较堆栈替代方案。

Performance regression detection using the browse daemon. (gstack)

Automate Benchmark Email tasks via Rube MCP (Composio). Always search tools first for current schemas.

>- Use after competitive-platform-analysis has produced a tiered competitor set. Scores each competitor across nine weighted dimensions (positioning, voice, visual craft, offer packaging, evidence, enterprise-readiness, thought leadership, pricing, client's strategic tension) with explicit 1–5 rubrics and a tension-plot. Precedes competitive-report-structure.

Cross-model benchmark for gstack skills. (gstack)

'Create benchmark suite creator operations. Auto-activating skill for Performance Testing. Triggers on: benchmark suite creator, benchmark suite creator Part of the Performance Testing skill category. Use when working with benchmark suite creator functionality. Trigger with phrases like \"benchmark suite creator\", \"benchmark creator\", \"benchmark\". '

このスキルを使用して、パフォーマンスベースラインを測定し、PR前後の回帰を検出し、スタック代替案を比較します。

>- Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, detect outliers, find key drivers, or leverage generative AI capabilities in BigQuery.

Use when targeting British Machine Vision Conference (BMVC) 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.

| Internal process for the bug-clusterer agent. Defines the step-by-step procedure for parsing, classifying, redacting, scoring, and clustering bug candidates from raw X/Twitter posts. Not user-invocable — loaded by the bug-clusterer agent through its skills frontmatter.

'Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like \"build automl pipeline\", \"automate ml workflow\", or \"create automated training pipeline\". '

'Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting \"build a classifier\", \"create classification model\", or \"train classifier\". Trigger with relevant phrases based on skill purpose. '

'Execute this skill allows AI assistant to construct and configure neural network architectures using the neural-network-builder plugin. it should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '

Use when packaging a CAV (Computer Aided Verification) artifact for the Artifact Evaluation Committee (AEC), covering the three badges (Available / Functional / Reusable), the smoke-test and full-review phases, ≥2 AEC reviewers per artifact, DOI-issuing archives, verification-tool packaging (solvers, benchmarks, seeds, resource limits, proof witnesses), and the fact that AE is invited, post-notification, and non-conditional.

Use when drafting a CAV (Computer Aided Verification) author response (rebuttal) for a paper that has passed the two-stage review's first filter, covering how to answer soundness/proof objections, benchmark-fairness challenges, and novelty-delta doubts with verifiable evidence while preserving double-anonymity for Regular and Application papers.

Use when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.

Use when positioning a CAV (Computer Aided Verification) submission against the verification literature across CAV, TACAS, FMCAD, VMCAI, POPL/PLDI, and the journals (FMSD, JAR, TOCL, STTT), writing delta-first contrast rather than a citation catalog, crediting the right benchmark and tool lineages, keeping self-citations double-anonymous for the anonymized categories, and handling concurrent and prior-version overlap.

Use when reasoning about how a CAV (Computer Aided Verification) submission is evaluated, covering the two-stage reviewing process (two reviews then an early-reject filter, then two more reviews with a rebuttal), the partial double-anonymity by category, the accept/reject outcome, the optional non-conditional artifact evaluation, and how CAV differs from TACAS and FMCAD.

Use when deciding what belongs in a CAV (Computer Aided Verification) paper body versus its optional appendix and artifact, covering the LNCS page limits, the rule that reviewers are not obliged to read the appendix so decision-critical content stays in the body, where full proofs and benchmark tables live, and double-anonymous supplementary material for the anonymized categories.

Use when planning a CAV (Computer Aided Verification) project timeline from venue and category selection through submission, the two-stage review with early reject and rebuttal, artifact evaluation by the AEC, and the LNCS open-access camera-ready, with backward-planning offsets for a verification-tool paper and honest handling of the single-annual-deadline cycle.

Use when defining n, choosing statistical tests, correcting for multiple comparisons, and reporting error bars for a Cancer Cell (Cell Press) manuscript. Focuses on biological statistics and avoiding pseudo-replication; it does not design experiments or build figures.

Use when writing the Cancer Cell (Cell Press) front matter — the Summary, eTOC blurb, Highlights, and graphical abstract. It crafts these short items for accuracy and impact; it does not write Results or calibrate statistics.

Use when running the final pre-submission preflight for Cancer Cell (Cell Press) — file completeness, STAR Methods / Key Resources Table, deposition accessions, statistics reporting, ethics statements, figure integrity, and portal logistics. It is the last gate before submitting.

Use when packaging ACM CCS artifacts for the artifact-evaluation committee and the ACM badges — Artifacts Available, Artifacts Evaluated Functional, Artifacts Evaluated Reusable, and Results Reproduced — covering what security evaluators inspect, how to make attacks and defenses turnkey, and how to justify withheld artifacts.

Use when preparing an accepted ACM CCS paper for the ACM Digital Library proceedings, covering de-anonymization, the ACM sigconf final format, ACM rights and eRights forms, badge placement from artifact evaluation, incorporation of shepherd and minor-revision requirements, disclosure-timing coordination, registration, and in-person presentation.

Use to build Cell's mandatory STAR Methods — the Key Resources Table, the three Resource Availability subsections, Experimental Model and Subject Details, Method Details, and Quantification and Statistical Analysis, in the exact required order.