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Simplify working code while preserving behavior—remove dead paths, flatten nesting, clarify names, reduce duplication. Use after features work but code feels over-engineered.

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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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Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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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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This skill provides expert-level guidance for refactoring VS Code extension code. Use when extracting classes or functions, reducing code duplication, improving type safety, reorganizing module structure, applying design patterns, or optimizing performance. Covers systematic refactoring workflows, code smell detection, safe transformation techniques, and VS Code-specific patterns.

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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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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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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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Improve code structure without changing behavior. Use when refactoring, restructuring, simplifying, or extracting code. Also for reducing duplication, renaming for clarity, or addressing code smells. Enforces one change → test → commit cycle. NOT for style/formatting (use /lint), features, or bug fixes.

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Use when auditing DecisionsAI, Codex, Claude, Cursor, Gemini, Pi, Cline, RTK, workflow pre_chain skills, hooks, projected commands, skill routing, or harness setup for drift, duplication, dead references, slow hooks, bloated context, or stale config.

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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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DRY (Don't Repeat Yourself) review rules for code reviews: find the same knowledge — a rule, a constant, a parser, a validation, a protocol detail — implemented in several places that must change together, and tell it apart from code that merely looks alike. Use when reviewing a diff, a pull request or recent changes for duplication, on "DRY review", "is this duplicated", "copy-paste check", or when a review checklist asks for DRY. Pair it with the solid skill for design and with owasp-asvs for security.

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SOLID design review rules (SRP, OCP, LSP, ISP, DIP) for code reviews: what counts as a real design finding, what is only taste, how to cite it and how severe it is. Use when reviewing a diff, a pull request or recent changes for design problems, when asked for a "SOLID review", "design review", "is this class doing too much", or when a review checklist asks for SOLID. Pair it with the dry skill for duplication and with owasp-asvs for security.

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Codebase audit + refactoring workflow. Dead code detection, God Object splitting, duplicate function removal, design flaw identification, frontier comparison verification. Triggered by "audit" ("감사"), "dead code" ("데드코드"), "refactor" ("리팩토링"), "god object", "duplication" ("중복"), "design flaw" ("설계 결함") keywords.

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Code review from the Kent Beck Simple Design 4 Rules perspective. God Object splitting, SRP, duplication removal, naming, cyclomatic complexity. Triggered by "kent beck", "simple design", "simplify", "refactoring" ("리팩토링"), "refactor", "god object", "SRP" keywords.

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Evaluate repeated code and consolidate duplication when a shared abstraction improves correctness or maintenance. Use after introducing similar logic or when repeated changes reveal a missing source of truth.

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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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Refactor duplicated code by extracting helpers, shared abstractions, or clearer reusable structure. Use when asked to apply DRY, remove duplication, consolidate repeated logic, or run `/refactor:dry`.

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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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