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Claude Skills by Amey-Thakur
github.com/Amey-Thakur1,001 skills16 installs1,473 views
- Support Response WritingWrite replies that answer the question, respect the customer's time, and avoid the phrases that make people angrier. Use when writing any customer-facing support message.Votes: 0GitHub stars: 7
- Support Sla ManagementSet, measure, and honour response and resolution commitments that reflect real capacity rather than aspiration. Use when promising response times to customers or in contracts.Votes: 0GitHub stars: 7
- Support Ticket TriageSort incoming tickets by urgency and type so the ones that matter are answered first and nothing sits unread. Use when volume exceeds what can be handled in arrival order.Votes: 0GitHub stars: 7
- Batch Vs StreamingChoose batch or streaming from honest latency requirements and operate the complexity you actually need. Use when designing a data flow or reviewing whether a streaming system earns its cost.Votes: 0GitHub stars: 7
- Change Data CaptureReplicate database changes via log-based CDC with correct snapshot handling, ordering, and schema-change survival. Use when streaming OLTP changes to warehouses, caches, or search without touching app code.Votes: 0GitHub stars: 7
- Data LineageCapture table and column-level lineage to answer impact and provenance questions before changes and during incidents. Use when planning schema changes, debugging bad numbers, or building data-platform trust.Votes: 0GitHub stars: 7
- Data PartitioningPartition datasets by pruning-friendly keys with healthy file sizes and scheduled compaction. Use when laying out lake or warehouse tables, or fixing slow scans and small-file explosions.Votes: 0GitHub stars: 7
- Data Pipeline DesignBuild data pipelines that rerun safely, backfill cleanly, and tolerate late data. Use when designing batch or streaming pipelines or fixing ones that need manual babysitting.Votes: 0GitHub stars: 7
- Data Quality ChecksLayer freshness, volume, schema, and distribution checks with quarantine and alert discipline. Use when adding quality gates to pipelines or when consumers keep finding bad data first.Votes: 0GitHub stars: 7
- Data RetentionImplement retention tiers, legal holds, and deletion pipelines that actually delete, including GDPR-style erasure. Use when defining how long data lives or building the machinery that enforces it.Votes: 0GitHub stars: 7
- Etl Vs EltPlace transformations before or after loading based on warehouse economics, governance, and reuse. Use when architecting a data platform or deciding where a transformation should live.Votes: 0GitHub stars: 7
- Incremental ProcessingProcess only new and changed data with watermarks, merge strategies, and a full-refresh escape hatch. Use when full-table rebuilds get slow or costly and models must go incremental.Votes: 0GitHub stars: 7
- Pipeline OrchestrationOrchestrate data DAGs with data-aware scheduling, bounded retries, and SLAs that page the right owner. Use when structuring workflows in an orchestrator or fixing 3am cron archaeology.Votes: 0GitHub stars: 7
- Schema EvolutionEvolve data schemas with compatibility rules, producer contracts, and registries so downstream never breaks silently. Use when changing event or table schemas that other teams consume.Votes: 0GitHub stars: 7
- Warehouse ModelingModel warehouse tables with explicit grain, conformed dimensions, and deliberate SCD handling. Use when designing analytics schemas or fixing double-counted metrics and unjoinable tables.Votes: 0GitHub stars: 7
- Consent ManagementAsk for, record, and honour consent so processing has a lawful basis and withdrawal actually stops it. Use when adding tracking, marketing, or any optional processing of personal data.Votes: 0GitHub stars: 7
- Cookie ComplianceHandle cookies and similar storage so non-essential ones only load after consent and the banner reflects reality. Use when adding analytics, embeds, or any client-side storage on a website.Votes: 0GitHub stars: 7
- Cross Border TransfersKnow where personal data physically goes and keep transfers lawful when it crosses a border. Use when choosing a region, adding a vendor, or designing replication and backups.Votes: 0GitHub stars: 7
- Data AnonymizationRemove or blunt identifiers so a dataset can be analysed or shared without re-identifying people, and know when it is only pseudonymous. Use when preparing data for analytics, testing, sharing, or research.Votes: 0GitHub stars: 7
- Data ClassificationLabel data by sensitivity so controls, retention, and access follow the label instead of being argued case by case. Use when designing storage, granting access, or deciding how carefully a dataset must be handled.Votes: 0GitHub stars: 7
- Data MinimizationCollect and keep only the personal data a feature actually needs, so exposure, cost, and compliance burden all shrink at once. Use when designing a form, an event schema, a log line, or any system that touches personal data.Votes: 0GitHub stars: 7
- Privacy By DesignBuild privacy into a feature from the first design rather than bolting it on before launch. Use when starting any feature that will touch personal data.Votes: 0GitHub stars: 7
- Privacy Impact AssessmentAssess a high-risk processing activity before it ships, documenting risks, mitigations, and the decision. Use when processing sensitive data, profiling at scale, or introducing a novel use of personal data.Votes: 0GitHub stars: 7
- Right To ErasureDelete a person's data on request across every store, copy, and backup, and prove it happened. Use when implementing deletion, handling an erasure request, or auditing whether delete really deletes.Votes: 0GitHub stars: 7
- Subject Access RequestsAnswer a request for a copy of someone's data completely, on time, and without exposing anyone else. Use when building an export path or responding to an access request.Votes: 0GitHub stars: 7
- Vendor Data ProcessingAssess and control what third-party services do with personal data you send them, before and after integration. Use when adding a vendor, SDK, or API that will receive user data.Votes: 0GitHub stars: 7
- Cohort AnalysisGroup users by a shared start and track them over time to see retention, behavior, and trends that aggregates hide. Use when a blended metric looks stable or improving but you suspect the underlying behavior is changing.Votes: 0GitHub stars: 7
- Correlation CausationTell correlation from causation and avoid the confounding, selection, and reverse-causation traps. Use when data shows a relationship and someone is about to claim one thing causes another.Votes: 0GitHub stars: 7
- Data CleaningClean and prepare messy data (missing values, outliers, types, duplicates) with decisions that preserve signal and avoid leakage. Use when raw data needs to be made analysis-ready without corrupting it.Votes: 0GitHub stars: 7
- Data StorytellingTurn analysis into a clear narrative that drives a decision, leading with the insight and backing it with the right evidence. Use when presenting findings to stakeholders who need to act, not admire charts.Votes: 0GitHub stars: 7
- Data VisualizationChoose and design charts that reveal the truth in data clearly and honestly, matching the chart to the question. Use when visualizing data for exploration or communication, or fixing a misleading or cluttered chart.Votes: 0GitHub stars: 7
- Experiment AnalysisAnalyze A/B test results correctly: effect size with intervals, guardrails, segments, and the peeking and Simpson traps. Use when reading out an experiment and deciding whether the change worked, before you ship it.Votes: 0GitHub stars: 7
- Exploratory Data AnalysisExplore a new dataset systematically to understand its shape, quality, and signal before modeling. Use when you first get a dataset and need to know what is in it, what is wrong with it, and what is worth pursuing.Votes: 0GitHub stars: 7
- Feature Engineering TabularEngineer features for tabular models (aggregations, encodings, interactions) that add signal without leaking. Use when building features for a tabular ML or Kaggle problem, where features often matter more than the model.Votes: 0GitHub stars: 7
- Funnel AnalysisAnalyze conversion funnels to find where users drop off and why, and size the opportunity of fixing each step. Use when diagnosing where a multi-step flow loses people, or prioritizing what to fix.Votes: 0GitHub stars: 7
- Gradient Boosting TuningTune gradient-boosted trees (XGBoost, LightGBM, CatBoost) effectively: the parameters that matter and the order to tune them. Use when using gradient boosting on tabular data and wanting real gains from tuning.Votes: 0GitHub stars: 7
- Kaggle Competition WorkflowApproach a Kaggle (or any ML competition) systematically: trustworthy validation, strong baseline, then disciplined iteration. Use when entering a data competition and want to place well without wasting the timeline.Votes: 0GitHub stars: 7
- Leaderboard StrategyTrust your cross-validation over the public leaderboard, avoid overfitting it, and select final submissions wisely. Use in the endgame of a Kaggle competition, where rank is won or lost by validation discipline.Votes: 0GitHub stars: 7
- Model EnsemblingCombine diverse models through averaging, blending, and stacking to beat any single model. Use when squeezing maximum accuracy from a competition or high-stakes prediction, after strong single models exist.Votes: 0GitHub stars: 7
- Probability FundamentalsReason correctly about probability, base rates, conditional probability, and expected value, avoiding the common intuition traps. Use when interpreting likelihoods, test results, risks, or any uncertain quantity.Votes: 0GitHub stars: 7
- Regression AnalysisFit and interpret regression models for insight, reading coefficients, fit, and caveats honestly rather than as causal truth. Use when using regression to understand relationships in data, not to build a predictive model.Votes: 0GitHub stars: 7
- Sampling And BiasJudge whether a sample represents the population and spot the selection, survivorship, and response biases that invalidate conclusions. Use when drawing conclusions from data that is a sample of something larger, which is almost always.Votes: 0GitHub stars: 7
- Statistical InferenceDraw honest conclusions from samples using hypothesis tests, confidence intervals, and significance read correctly. Use when deciding whether an effect is real, comparing groups, or reporting uncertainty.Votes: 0GitHub stars: 7
- Time Series AnalysisAnalyze and forecast time-ordered data respecting trend, seasonality, autocorrelation, and the arrow of time. Use when working with data indexed by time: metrics, sales, sensor readings, or any forecast.Votes: 0GitHub stars: 7
- Backup RestoreDesign backups around tested restores, point-in-time recovery, and backup security. Use when setting up database backups or verifying that existing backups would actually work in a disaster.Votes: 0GitHub stars: 7
- Database MigrationsChange schemas safely with expand-contract, online DDL, batched backfills, and rollback awareness. Use when altering a production schema or when a migration risks locking or downtime.Votes: 0GitHub stars: 7
- Database NormalizationApply normal forms pragmatically and denormalize deliberately, weighing update anomalies against read performance. Use when designing a relational schema or deciding whether to denormalize.Votes: 0GitHub stars: 7
- Indexing StrategyDesign indexes from query patterns with correct column order, covering indexes, and write-cost awareness. Use when queries are slow or a table has too many or too few indexes.Votes: 0GitHub stars: 7
- Nosql ModelingModel NoSQL data from access patterns first, with single-table and denormalization discipline. Use when designing for a document or key-value store, or when a relational mindset is fighting a NoSQL database.Votes: 0GitHub stars: 7
- Orm TradeoffsUse ORMs for their productivity while avoiding N+1 queries and knowing when to drop to raw SQL. Use when working with an ORM or debugging the performance problems ORMs quietly cause.Votes: 0GitHub stars: 7