Results for “deduplication”

Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.

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Create, merge, and deduplicate BibTeX bibliography files using R packages (RefManageR, bibtex). Parse .bib files into structured R objects, merge multiple bibliographies with deduplication by DOI or title similarity, generate entries from DOI/ISBN/arXiv ID, and export clean sorted .bib files. Use when creating a new .bib file for an R Markdown or Quarto project, merging bibliographies from multiple collaborators, deduplicating a .bib that has grown through copy-paste accumulation, or generating BibTeX entries programmatically from DOIs or other identifiers.

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Manage an engagement buffer that ingests, prioritizes, rate-limits, deduplicates, and tracks state for incoming engagement items across platforms. Generates periodic digests and enforces cooldown periods. Composes with du-dum: du-dum sets the observation/action cadence, this skill manages the queue between beats.

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Configure Prometheus Alertmanager with routing trees, receivers (Slack, PagerDuty, email), inhibition rules, silences, and notification templates for actionable incident alerting. Use when implementing proactive monitoring with automated incident detection, routing alerts to the appropriate team by severity, reducing alert fatigue through grouping and deduplication, integrating with on-call systems like PagerDuty, or migrating from legacy alerting to Prometheus-based alerting.

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Unified web and social media search across 5 providers (Brave, Tavily, Xpoz, Exa, Firecrawl). Auto-routes queries to the best API, supports parallel multi-provider search with deduplication, AI-generated answers, and social media search. Triggers on web search, find news, search Twitter, search Reddit, social media search, multi-provider search, omnisearch, research news, OSINT, search for, look up, find information about.

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OSINT and reconnaissance automation skill for passive collection, pivoting, deduplication, and graph-based analysis using subfinder, amass, dnsx, chaos, crt.sh, github-dorks, waybackurls, gau, gitleaks, trufflehog, theHarvester, h8mail, holehe, sherlock, Shodan, Censys, FOFA, and Hunter. Use for asset discovery, exposure monitoring, and authorized reconnaissance only.

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Bug bounty workflow skill for program scope mapping, recon-to-report automation, deduplication against prior submissions, and high-signal reporting on HackerOne, Bugcrowd, Intigriti, YesWeHack, and self-hosted programs. Use to organize bounty work end to end while staying inside program rules.

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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"Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.".

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Organize files so you can find anything: naming conventions, folder structures, archiving, and deduplication. Use when digital clutter slows you down or you're setting up a new system.

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Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Focuses on recently modified code unless instructed otherwise. This skill should be used when code-quality checks pass but the code would benefit from structural cleanup — deduplication, branching simplification, naming improvements, or dead-code removal. Invoked as a subagent from /code-quality or directly via the Task tool.

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Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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Audit e-discovery and litigation document review systems -- data collection pipelines (PST, MBOX, SharePoint, Slack), document processing (OCR via Tesseract/ABBYY, metadata extraction, deduplication), Technology Assisted Review (TAR 1.0/2.0/CAL with recall/precision tracking).

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Use when designing, reviewing, or troubleshooting Salesforce duplicate prevention, matching rules, duplicate rules, and merge governance. Covers cleaning up duplicates that already exist at ordinary volume: merge sequencing, survivorship, stewardship ownership. Also covers the deployable MatchingRule / DuplicateRule metadata, the Alert-vs-Block action pair, DuplicateRecordSet reporting, and the Apex DMLOptions.DuplicateRuleHeader.allowSave bypass. Triggers: 'duplicate rule', 'matching rule', 'merge strategy', 'dedupe', 'survivorship', 'D&B', 'same contact twice', 'clean up duplicate records', 'clean up duplicate accounts', 'DUPLICATES_DETECTED', 'allowSave', 'FindDuplicates'. NOT for cleanup at volume — hundreds of thousands of records, batch merge jobs, DemandTools/Cloudingo — use data/large-scale-deduplication. NOT for which field values and child records the platform keeps in a merge - use data/record-merge-implications.

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Use this skill when configuring or troubleshooting Data Cloud identity resolution rulesets — matching rules, reconciliation rules, unified profiles, and cross-device identity linking. Trigger keywords: identity resolution ruleset, unified individual, match rule, reconciliation rule, unified profile, cross-device identity, Data Cloud deduplication, cluster creation. NOT for CRM duplicate and matching rules — use admin/duplicate-management. NOT for overall Data Cloud layer and activation design — use architect/data-cloud-architecture.

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Router for the 101 SfSkills `data` skill packages. Data model, data movement and data quality: migrations, bulk loads, query optimisation, deduplicating at volume, archival. Ordinary-volume duplicate cleanup and prevention use salesforce-admin; come here for hundreds-of-thousands+ dedup or third-party tools. LDV architecture uses salesforce-architect. Use when the request mentions data model, data migration, data load, Data Loader, Bulk API, external id, deduplication at volume, archival, SOSL, cross-object search, sandbox seed data, SandboxPostCopy, native Data Seeding. Finds and opens the exact skill package to read; it does not contain the guidance itself.

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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

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Guides AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record linkage, deduplication, Senzing SDK integration, data mapping for Senzing ingestion, or troubleshooting Senzing error codes. Also use when the user mentions matching records across data sources, finding duplicate entities, identity resolution, master data management, or needs to resolve who is who across datasets.

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Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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Curação de dados acelerada por GPU para treinamento de LLM. Suporta texto/imagem/vídeo/áudio. Recursos incluem deduplicação fuzzy (16× mais rápida), filtragem de qualidade (30+ heurísticas), deduplicação semântica, redação de PII, detecção NSFW. Escala em GPUs com RAPIDS. Use para preparar datasets de treinamento de alta qualidade, limpar dados web ou deduplicar grandes corpora.

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Ordnet freiwillig bereitgestellte Kontaktdaten datensparsam und erkennt mögliche Dubletten.

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Audit server-side paid-media measurement including server-side tag management, platform conversion APIs, event taxonomy, browser/server deduplication, consent, hashing, data quality, observability, and privacy. Use for server-side tracking, sGTM, server-side tagging, CAPI, Events API, event_id, pixel debugging, first-party measurement, or conversion data loss.

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Implements BLoC (v9.x) event-driven state management for Flutter with unidirectional data flow using the flutter_bloc package. Use when implementing Bloc/Cubit classes, mapping Events to States, consuming state with BlocBuilder/BlocListener/BlocConsumer, applying event transformers (restartable/droppable/sequential) for concurrency control, setting up BlocProvider/MultiBlocProvider DI, integrating Freezed for sealed union types and exhaustive pattern matching, or testing with blocTest. Ideal for enterprise apps requiring business logic separation, precise concurrency control (search debouncing, form deduplication), or event replay debugging.

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