Claude Skills by thedixitjain

Use when preparing an accepted ACM IMC paper for the proceedings, covering systematic de-anonymization, finalizing the Ethics and artifact-availability statements, permanentizing dataset and code links to DOI-issuing archives, satisfying the availability shepherd, Community Contribution Award eligibility, and ACM production checks.

Use when deciding what belongs in an ACM IMC paper body versus its appendix and released artifact, covering the acmart page limits, the rule that decision-critical evidence and the Ethics discussion stay in the reviewed pages, double-blind supplementary material, and how to split a measurement paper between body, appendix, and dataset.

Use when an IMF Economic Review (IMFER) manuscript's headline cross-country estimate must be shown to survive specification, sample, country-composition, and inference choices before submission or in an R&R. Builds the robustness suite a dual academic/policy referee expects; it does not establish identification (imfer-identification) or format exhibits (imfer-tables-figures).

Implement Amazon Macie to automatically discover, classify, and protect sensitive data in S3 buckets using machine learning and pattern matching for PII, financial data, and credentials detection.

Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling

'Profile inference latency profiler operations. Auto-activating skill for ML Deployment. Triggers on: inference latency profiler, inference latency profiler Part of the ML Deployment skill category. Use when working with inference latency profiler functionality. Trigger with phrases like \"inference latency profiler\", \"inference profiler\", \"inference\". '

Run 150+ AI apps (image, video, LLM) via inference.sh CLI.

Use when packaging IEEE INFOCOM code, simulators, and datasets for credibility and optional IEEE reproducibility badges, given that INFOCOM runs no standing artifact-evaluation track, covering DOI-issuing archives, evaluator-proof documentation, what the absence of a formal track changes, and how a released package strengthens a no-rebuttal submission.

Use when targeting International Conference on Automated Machine Learning (AutoML Conference) 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 automated ML.

Use when targeting IEEE/CVF International Conference on Computer Vision (ICCV) 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 International Conference on Machine Learning (ICML) 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 flagship.

Use when packaging code, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready, checkpoint and recipe distribution, voice-data licensing and speaker-consent hygiene, and the ethics of releasing synthesis or cloning systems.

Codified expertise for demand forecasting, safety stock optimisation, replenishment planning, and promotional lift estimation at multi-location retailers.

Use when packaging an IPSN-lineage artifact for the ACM badges and the IPSN Best Research Artifact Award, covering hardware-plus-software artifacts (firmware, board files, datasets), what sensor-systems evaluators check first, DOI-issuing archives, evaluator-proof documentation for a physical system, and the separate post-acceptance timing.

Use when deciding what belongs in an IPSN-lineage paper body versus its artifact, appendix, and demo/poster track, covering the ≤12-page ACM two-column budget, the rule that decision-critical evidence stays inside the reviewed pages, double-blind supplementary material, dataset release, and how to split a sensor-systems paper between body and package.

Use when packaging IROS evidence for a skeptical reviewer even though IROS runs no formal artifact track — the robotics artifact stack of hardware ledger, logs, configs, code, and data, what an embodied-systems reviewer opens first, review-time anonymity versus acceptance-time public release, and making a claim auditable not asserted.

Use when designing or auditing IROS experiments — real-robot trial counts, success criteria set in advance, reset procedures, failure taxonomies, baseline fairness on matched hardware, sim-to-real gap reporting, small-n statistics, and the claim-to-evidence ladder that embodied-systems reviewers apply before they trust a demo.

Use when preparing an accepted ISCA paper's artifact for evaluation under the ACM Review and Badging policy — scoping which results are reproducible within evaluator budgets, packaging simulator-heavy workflows others can run, writing the evaluator-facing appendix, and earning badges that print on the paper.

Use when designing or auditing the evaluation of an ISCA paper — pinning simulator fidelity to the claims it must carry, documenting gem5-class configurations and sampling choices, selecting workload suites that represent the claim's domain, tuning baselines in good faith, and separating architectural effect from modeling artifact.

Use when packaging an ISSTA tool, benchmark, and results for the artifact-evaluation track, covering the ACM badges (Artifacts Available via Zenodo, Evaluated Functional and Reusable, Results Reproduced), the anonymous review-time copy, containerization, a runnable entry point, and what ISSTA artifact evaluators actually try first.

Use to adapt the idea of an \"artifact\" to a pure-theory venue — at ITCS there is no artifact-evaluation track, no code, and no badges; the analogue is making every claim independently verifiable through complete proofs in the submitted PDF and a matching public full version, plus checkable finite objects for any computational content.

Use when designing or auditing the figures and tables of a JAMA manuscript, including the required flow diagram, baseline table, and main-result exhibits. Shapes exhibits for JAMA conventions; it does NOT run the statistics or write the abstract.

Use when responding to JAMA editor and reviewer comments after a revise decision, including the statistical-review queries. Structures the point-by-point response and revision; it does NOT fabricate new analyses or overstate changes.

Use when preparing or auditing the statistical analysis and reporting of a JAMA manuscript so it survives JAMA's dedicated statistical review. Enforces effect sizes with 95% CIs, multiplicity control, and pre-specification; it does NOT choose the study design or write prose.

Use when writing or auditing the JAMA structured abstract and the Key Points box for a JAMA manuscript. Enforces the exact JAMA heading set and quantified results; it does NOT design the study or run statistics.

在为《计算机辅助设计与图形学学报》(Journal of Computer-Aided Design & Computer Graphics, JCAD&CG) 准备代码与数据可用性、撰写诚实的可用性声明时调用。说明本刊是否设正式制品评审(artifact evaluation)属待核实的现状，给出无论政策如何都应做到的图形学制品自律标准：代码/数据/网格/预训练模型/渲染脚本的公开与托管、双盲外审下的匿名化、演示视频与结果可复现、受限数据的替代核验途径，以及可用性声明的写法。适用于把图形/几何论文的制品打包到经得起审阅、避免声明与实际不符的场景。

Use when assigning JEL classification codes to a Journal of Economic Literature (JEL) survey and relating the survey to the JEL code taxonomy that the journal itself maintains. Explains and applies the codes correctly; it does not write prose (jel-writing-style) or run the submission preflight (jel-submission).

Use when targeting Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING) 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 language resources.

在处理《通信学报》(Journal on Communications, JOC) 稿件的代码与数据可用性问题时调用。讲清本刊作为中文通信期刊目前没有独立于正文评审的\"制品/工件徽章\"(artifact badge) 制度这一现状，指导在没有正式制品评审的前提下，如何通过开放代码/数据/复现包、规范数据可用性声明、匿名评审仓库、仿真参数与信道实现公开等方式，主动提升通信实验的可信度与可复现性，帮助作者把可用性材料组织到既符合本刊三审制评审关注点、又不误称本刊有官方徽章制度的程度，适用于物理层、网络、安全与信号处理方向的中文长文。

当你要判断《软件学报》(Journal of Software, JOS) 对代码与数据可用性（artifact）的现状与要求、并据此准备可评估材料时使用。覆盖本刊尚无强制 artifact 评审轨道的现实、如何把可复现材料做成加分项、专刊（special issue）与 CCF ChinaSoft 联动可能带来的材料要求、归档与许可选择、以及与国际会议 artifact badging 的差异，帮助你在《软件学报》(Journal of Software) 语境下做出恰当而不过度的 artifact 准备。

Use when targeting Journal of Business and Economic Statistics (JBES) or deciding whether a manuscript at the statistics/econometrics interface fits this venue. Encodes the journal's fit, framing, method-and-evidence bar, house style, official-submission re-check, and desk-reject heuristics.

Use when the empirical core of a 《数量经济技术经济研究》 (JQTE) manuscript is an econometric model — time series, cointegration, mixed-frequency, VAR/SVAR, state-space, or panel / macro-econometrics. Enforces correct model setup, stationarity / unit-root and cointegration diagnostics, and lag/specification justification. Use when the method itself is the contribution rather than a causal identification claim.

Use when a 《数量经济技术经济研究》 (JQTE) manuscript makes a forecasting or prediction claim — macro forecasting, business-cycle / sentiment indices, mixed-frequency nowcasting, or model-based prediction. Enforces genuine out-of-sample evaluation (RMSE / MAE / directional accuracy / Diebold-Mariano), a proper benchmark, and a recursive / rolling design. The fastest desk-reject here is reporting in-sample fit only.

Kasuta, kui kasutaja palub luua, üles ehitada või redigeerida Jupyteri märkmikke (`.ipynb`) katsetuste, uurimiste või juhendite jaoks; eelista kaasasolevaid malle ja käivita abiskript `new_notebook.py`, et genereerida puhas algne märkmik.

زمانی استفاده شود که کاربر درخواست ایجاد، ساختاردهی یا ویرایش دفترچه‌های Jupyter (`.ipynb`) برای آزمایش‌ها، کاوش‌ها یا آموزش‌ها را دارد؛ از قالب‌های همراه ترجیحاً استفاده کنید و اسکریپت کمکی `new_notebook.py` را اجرا کنید تا یک دفترچهٔ آغازین تمیز تولید شود.

Käytetään, kun käyttäjä pyytää luomaan, alustamaan tai muokkaamaan Jupyter-muistikirjoja (`.ipynb`) kokeita, tutkimuksia tai opetusohjelmia varten; käytä mieluummin mukana olevia mallipohjia ja suorita apuohjelmaskripti `new_notebook.py` luodaksesi puhtaan aloitusmuistikirjan.

À utiliser lorsque l'utilisateur demande de créer, structurer ou modifier des notebooks Jupyter (`.ipynb`) pour des expériences, explorations ou tutoriels ; privilégiez les modèles fournis et exécutez le script d'aide `new_notebook.py` pour générer un notebook de départ propre.

צור מחברות Jupyter נקיות וניתנות לשחזור עבור שני מצבים עיקריים:

उपयोग तब करें जब उपयोगकर्ता प्रयोगों, खोजों, या ट्यूटोरियल्स के लिए Jupyter नोटबुक (`.ipynb`) बनाने, स्कैफोल्ड करने, या संपादित करने के लिए कहे; पैकेज किए गए टेम्पलेट्स को प्राथमिकता दें और एक साफ शुरुआत वाला नोटबुक बनाने के लिए सहायक स्क्रिप्ट `new_notebook.py` चलाएँ।

Kreirajte čiste, reproducibilne Jupyter bilježnice za dva primarna načina:

Használható, amikor a felhasználó Jupyter notebookok (`.ipynb`) létrehozását, felépítését vagy szerkesztését kéri kísérletekhez, felfedezésekhez vagy oktatóanyagokhoz; részesítse előnyben a csomagolt sablonokat, és futtassa a segédprogramot `new_notebook.py`, hogy egy tiszta kezdő notebookot generáljon.

Gunakan saat pengguna meminta untuk membuat, membangun kerangka, atau mengedit notebook Jupyter (`.ipynb`) untuk eksperimen, eksplorasi, atau tutorial; utamakan templat bawaan dan jalankan skrip pembantu `new_notebook.py` untuk menghasilkan notebook awal yang bersih.

Usa quando l'utente chiede di creare, strutturare o modificare notebook Jupyter (`.ipynb`) per esperimenti, esplorazioni o tutorial; privilegia i modelli inclusi ed esegui lo script di supporto `new_notebook.py` per generare un notebook di partenza pulito.

Използвайте, когато потребителят иска да създаде, да изгради основна структура за или да редактира Jupyter тетрадки (`.ipynb`) за експерименти, изследвания или уроци; предпочитайте вградените шаблони и стартирайте помощния скрипт `new_notebook.py`, за да генерирате чист начален файл на тетрадка.

ユーザーが実験、探索、またはチュートリアル用の Jupyter Notebook（`.ipynb`）を作成、スキャフォールド、または編集するよう依頼したときに使用します。バンドルされたテンプレートを優先し、ヘルパースクリプト `new_notebook.py` を実行してクリーンな開始ノートブックを生成してください。

ប្រើនៅពេលដែលអ្នកប្រើស្នើឲ្យបង្កើត រៀបចំ (scaffold) ឬកែសម្រួល Jupyter notebooks (`.ipynb`) សម្រាប់សាកល្បង ស្វែងរក ឬមេរៀន; អនុសាសន៍ឲ្យប្រើទំរង់គំរូដែលភ្ជាប់មកជាមួយ និងរត់ស្គ្រីបជំនួយ `new_notebook.py` ដើម្បីបង្កើតសៀវភៅចាប់ផ្តើមដែលស្អាត។

ಬಳಕೆದಾರರು ಪ್ರಯೋಗಗಳು, ಅನ್ವೇಷಣೆಗಳು ಅಥವಾ ಟ್ಯುಟೋರಿಯಲ್‌ಗಳಿಗಾಗಿ Jupyter ನೋಟ್ಬುಕ್‌ಗಳು (`.ipynb`) ರಚಿಸಲು, ಮೂಲ ರಚನೆ ಸಿದ್ಧಪಡಿಸಲು ಅಥವಾ ಸಂಪಾದಿಸಲು ಕೇಳಿದಾಗ ಬಳಸಿರಿ; ಸಂಯೋಜಿತ ಟೆಂಪ್ಲೇಟ್‌ಗಳಿಗೆ ಪ್ರಾಧಾನ್ಯ ನೀಡಿ ಮತ್ತು ಪ್ರಾರಂಭಿಕ ಸ್ವಚ್ಛ ನೋಟ್ಬುಕ್ ರಚಿಸಲು ಸಹಾಯಕ ಸ್ಕ್ರಿಪ್ಟ್ `new_notebook.py` ಅನ್ನು ರನ್ ಮಾಡಿ.

사용자가 실험, 탐색 또는 튜토리얼을 위한 Jupyter 노트북(`.ipynb`)을 생성, 스캐폴딩하거나 편집해 달라고 요청할 때 사용합니다; 번들로 제공되는 템플릿을 우선 사용하고 깨끗한 시작 노트북을 생성하기 위해 헬퍼 스크립트 `new_notebook.py`를 실행하는 것을 권장합니다.

Naudokite, kai vartotojas prašo sukurti, paruošti arba redaguoti Jupyter užrašų knygeles (`.ipynb`) eksperimentams, tyrimams arba pamokoms; teikite pirmenybę pridedamiems šablonams ir paleiskite pagalbinį scenarijų `new_notebook.py`, kad sugeneruotumėte švarų pradinį užrašų knygelę.

ഉപയോക്താവ് പരീക്ഷണങ്ങൾ, അന്വേഷണങ്ങൾ, അല്ലെങ്കിൽ ട്യൂട്ടോറിയലുകൾക്കുള്ള Jupyter നോട്ട്ബുക്കുകൾ (`.ipynb`) സൃഷ്ടിക്കാൻ, സ്‌കാഫോൾഡ് ചെയ്യാൻ, അല്ലെങ്കിൽ തിരുത്താൻ ആവശ്യപ്പെടുമ്പോൾ ഉപയോഗിക്കുക; ബണ്ടിൽ ചെയ്ത ടെംപ്ലേറ്റുകൾ പ്രാഥമ്യം നൽകുക, കൂടാതെ ശുദ്ധമായ ആരംഭ നോട്ട്ബുക്ക് സൃഷ്ടിക്കാൻ സഹായി സ്ക്രിപ്റ്റ് `new_notebook.py` ഓടിക്കുക.