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Found 86 Skills
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Implement masked text input controls in WinForms applications. Use this skill whenever the user needs to create input fields with format masks (phone numbers, IP addresses, dates, currency), validate formatted input, restrict data entry to specific patterns, or configure how user input behaves with mask constraints.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
Side role: find and correct bad signals, earn leaderboard points per Publisher-approved correction (max 3/day)
Migrates databases between providers (Postgres, MySQL, Supabase, PlanetScale, MongoDB). Reads source schema, generates migration scripts, handles data type mapping, foreign keys, indexes, triggers, stored procedures. Validates migration with row counts and checksums. Generates migration-plan.md with step-by-step execution guide, rollback procedures, estimated downtime.
Validate, format, and convert between JSON, YAML, and TOML. Parse and query structured data files. No API key required.
Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says "审查论文数据", "check paper claims", "verify numbers", "论文数字核对", or before submission to ensure paper-to-evidence fidelity.
Data analysis, visualization, and storytelling skill for financial and RevOps contexts. Use when: analyzing revenue data, building forecasts, cohort analysis, churn modeling, pipeline analytics, creating data-driven reports, building dashboards, cleaning messy data, sanity-checking analytical claims, exporting to Excel with formulas, or extracting data from PDFs. Features decision logging, bias-aware interpretation, and progressive disclosure (slide deck -> detailed report -> full notebook with all decisions documented).
Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.
Parseur et explicateur complet du format HPK (format de message propriétaire santé). Supporte plus de 100 types de messages couvrant l'administration des patients (ID, MV, CV), la chaîne logistique (PR, FO, MA, CO, LI, RO, FA), les stocks (SO, IM), la structure organisationnelle (ST, UT) et les opérations financières (RD, DD). Utilise @erp-pas/hpk-dictionary comme source de vérité. Valide la structure, extrait les champs, explique le contexte métier, mappe vers HL7 v2.5/IHE PAM et aide au dépannage des problèmes d'intégration.
Review football data code and visualisations for correctness. Use after building a chart, data pipeline, or analysis. Dispatches specialised reviewers for data correctness, chart conventions, visual inspection, and interactive edge cases.
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.