postgres-setup
Set up PostgreSQL database with standardized schema.sql pattern. Use when starting a new project that needs PostgreSQL, setting up database schema, or creating setup scripts for postgres.
flask-smorest-api
Set up Flask REST API with flask-smorest, OpenAPI docs, blueprint architecture, and dataclass models. Use when creating a new Flask API server, building REST endpoints, or setting up a production API.
mcp-docker-deployment
Set up Docker deployment for Python MCP servers (FastMCP or low-level mcp.server.Server SDK) with SSE/streamable-http transport, automated versioning, and container registry publishing. Use when dockerizing an MCP server, containerizing for remote access, deploying an MCP server behind nginx, or setting up a production MCP server with Docker. Covers Dockerfile, build scripts, docker-compose, and nginx reverse proxy for SSE streaming.
kurrentdb
Provides KurrentDB (EventStoreDB) client code for event sourcing and CQRS. Generates correct package names, connection strings, and API patterns for Python, Node.js, .NET, F#, Go, Java, Rust. Triggers on "kurrentdb", "eventstore", "event sourcing", "append events", "read stream", "subscription", "aggregate", "CQRS".
cc-track-tools
Provides paths to cc-track utility scripts for validation, task completion, and git operations. Use when a cc-track command needs to run a script.
repo-clipboard
Snapshot the current directory into pseudo-XML for LLM context. Use when you need to share a repo (or a sub-tree) with Codex/LLMs, especially for code review/debugging, generating an agent-friendly “repo snapshot”, or piping context into tools like `llm` (see skill $llm-cli). Supports `.gitignore`-aware file discovery, common ignore patterns, extension filtering, regex include/exclude, optional file-list printing, line-range snippets, and writes `/tmp/repo_clipboard.{stdout,stderr}` for reuse.
deep-reason
Esta skill debe usarse cuando el usuario pide \"pensemos esto a fondo\", \"analicemos paso a paso\", \"evaluemos opciones\", \"razonemos sobre esto\", \"pensemos detenidamente\", \"meditemos\", \"qué me sugieres\", \"qué me recomiendas\", \"esto es complejo\", \"esto es complicado\", \"problema grande\", o ante problemas que requieren análisis profundo, afectan múltiples componentes del sistema, tienen implicaciones arquitectónicas importantes, o representan decisiones de diseño con impacto significativo. IMPORTANTE: Invocar esta skill en lugar de usar seq-think MCP directamente - la skill proporciona workflow estructurado con generación de documentos de análisis.
check-third-party-docs
Esta skill debe usarse cuando el usuario pide \"investiga documentación de\", \"consulta documentación de\", \"quiero integrar X en\", \"cómo integrar\", \"implementar X en proyecto\", \"qué librería para\", \"instalar\", \"configurar\", \"error con\", o menciona agregar funcionalidad que requiere dependencias especializadas (validación con zod, procesamiento imágenes con sharp, colas con bull, caché con ioredis, formularios con react-hook-form). IMPORTANTE: Invocar esta skill en lugar de usar Context7 MCP directamente - la skill proporciona framework de decisión sobre cuándo usar Context7 vs dependency-docs-collector agent según complejidad. No usar para paquetes ampliamente conocidos como express, react, vue, lodash, axios, jest; Claude tiene datos de entrenamiento suficientes para estos. Usar solo para paquetes especializados, plugins de framework, o librerías poco conocidas.
create-pr
Esta skill debe usarse cuando el usuario pide "crea el PR", "prepara el PR", "escribe el PR", "genera el PR", "draft del PR", "documenta el PR", "crea la descripción del PR", o menciona crear pull request manualmente. Proporciona formato estructurado adaptado al tamaño de los cambios.
add-testing
Esta skill debe usarse cuando el usuario pide \"agregar tests a proyecto existente\", \"add testing to existing project\", \"setup de testing\", \"configurar infraestructura de tests\", \"pipeline completo de testing\", \"quiero tests para este modulo\", \"agregar cobertura de tests\", \"add test coverage\", \"integrar testing al proyecto\". Ejecuta pipeline completo de 7 pasos: auditoria de testabilidad, adaptacion condicional de codigo, investigacion de dependencias, implementacion de tests, auditoria de calidad de inputs, reporte de cobertura y generacion de reglas del proyecto.
document
Esta skill debe usarse cuando el usuario pide "documentar un patrón", "documentar un problema", "documentar una decisión", "documentar guías", "documentar la sesión", "documentar el conocimiento de la sesión", "document session knowledge", "capturar lo aprendido en la sesión", "crear documentación de", o quiere capturar conocimiento técnico de la sesión usando plantillas estructuradas.
dropletify
Esta skill debe usarse cuando el usuario pide "desplegar en DigitalOcean", "configurar droplet", "deploy a droplet", "CI/CD para DigitalOcean", "GitHub Actions para droplet", o quiere preparar un proyecto para despliegue con Docker en DigitalOcean.
tdd-workflow
Esta skill debe usarse cuando el usuario pide \"aplicar TDD\", \"desarrollo guiado por tests\", \"test-driven development\", \"red green refactor\", \"ciclo RGR\", \"escribir tests primero\", \"nueva feature con TDD\", \"mejorar testabilidad\", \"filosofia TDD\", o menciona Iron Laws, TDD philosophy, o quiere entender el proceso TDD. NO usar cuando el usuario pide agregar tests a un proyecto existente o setup de testing — eso es testing:add-testing.
typescript-advanced-types
Esta skill debe usarse cuando el usuario pide "planear biblioteca TypeScript", "crear módulo reutilizable", "refactorizar tipados", "refactorizar tipos de un módulo", o cuando estés en modo plan y el diseño requiera tipados fuertes de TypeScript, sistemas extensibles, o arquitecturas type-safe. Especialmente útil para planear sistemas de notificaciones, background jobs, cores de aplicación, o módulos pluggables que requieran tipos complejos. Proporciona guía completa para diseñar y construir módulos y bibliotecas type-safe con el sistema de tipos avanzado de TypeScript.
plan-feature
Esta skill debe usarse cuando el usuario pide "planear una feature", "plan a feature", "crear un plan de acción", "disenar una implementacion", "design an implementation", "crear un plan de desarrollo", "create a development plan", "quiero planear", "quiero hacer un plan", "quiero disenar", o cuando solicite un feature cuya complejidad lo requiera. Ejecuta workflow de 5 fases: discovery, exploracion del codebase, clarificacion, arquitectura, y escritura del plan. Al aprobar el plan, invoca smart-delegation automaticamente para ejecutar la implementacion.
post-implementation
Esta skill debe usarse cuando el usuario pide "haz review de los cambios", "review the changes", "aplica el post-implementacion", "apply post-implementation", "run post-implementation", o cuando el plan aprobado instruye "invoke /smart-plan:post-implementation". Ejecuta workflow post-implementacion despues de que el codigo este completo con quality review (3 reviewers paralelos), auto-fix de issues (confianza >= 80%), documentacion del feature, y commit opcional. Puede usarse despues de cualquier implementacion, no solo smart-plan.
smart-delegation
Esta skill debe usarse cuando Claude detecta que una implementacion es lo suficientemente grande como para dividirla en sub-agentes (5+ archivos, dependencias entre cambios, nuevas abstracciones que otros archivos consumen), cuando el usuario pide "delega la implementacion", "orquesta los implementadores", "usa sub-agentes", "delegate to sub-agents", "orchestrate implementers", o cuando plan-feature invoca la delegacion tras aprobar un plan.
smart-interview
Esta skill debe usarse cuando el usuario pregunta "¿Preguntas?", "¿Tienes dudas?", "¿Tienes preguntas?", "¿Quieres aclarar algo?", "¿Necesitas aclarar algo?", "aclara lo que necesites", o cuando quiere aterrizar requerimientos antes de planear. Tambien la invoca plan-feature en Phase 3. Ejecuta entrevista estructurada para obtener requerimientos cuantificables, reglas de negocio traducibles a codigo, y flujos del sistema; luego anota los resultados en el plan.
cl-condition-system
condition/restartパターンを適用。エラーハンドリング実装時に使用
cl-mallet-linter
malletリンターのルールと設定を適用。コードレビュー・品質チェック時に使用
cl-clos-patterns
CLOS設計パターンを適用。クラス設計・メソッド実装時に使用
cl-asdf-system
ASDFシステム定義のベストプラクティス。.asdファイル作成・編集時に使用
browser-automation
Playwright MCPを使用したブラウザ自動化。ページ操作、スクリーンショット、フォーム入力のパターン集
cl-macro-design
マクロ設計のベストプラクティスを適用。マクロ作成・レビュー時に使用
cl-coding-style
Common Lispのコーディング規約を適用。Lispコード作成・レビュー時に使用
wayland-automation
Wayland環境でのGUI自動化。スクリーンショット取得、テキスト入力、クリップボード操作のパターン集
refactor-claude-md
CLAUDE.mdをベストプラクティスに基づいてリファクタリング
obsidian-skill
Expert guidance for working with Obsidian vaults including Obsidian Flavored Markdown (OFM) syntax, organization best practices, daily/weekly task workflows, vault maintenance, and automation. This skill should be used when working with Obsidian notes, organizing vault structure, setting up task management workflows, or integrating with Obsidian tooling.
cli-ninja-tools
CLI power tools for AI-assisted development. Use when (1) needing recommendations for CLI tools to install, (2) processing JSON/YAML data with jq/yq, (3) searching code with ripgrep or ast-grep, (4) documenting a CLI tool or multi-tool recipe you've discovered, (5) wanting to learn CLI patterns for data pipelines, or (6) setting up a new project and want CLI recommendations. Supports three modes - init (project scan), document (capture new recipes), and recommend (codebase analysis).
hello-extended
Multi-language greetings in 6 languages. Use for non-English greetings or multiple people.
dev-browser
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.
fast-playwright
Fast, persistent, and token-optimized browser automation using Playwright. Supports navigation, clicking, typing, screenshots, batch execution, and more. Use when working with web pages, browser automation, or when the user mentions browsing, clicking, or web scraping.
react-native-testing
Generate and write tests for React Native applications using React Native Testing Library (RNTL), Jest, and userEvent. Use this skill when the user asks to write tests, create test files, add unit tests, add component tests, or generate test suites for React Native or Expo projects. Also use when working with .test.tsx files, jest.config.js, or when the user mentions testing React Native components, screens, hooks, or forms. Covers getByRole, getByText, getByLabelText queries, userEvent.press, userEvent.type interactions, waitFor, findBy async patterns, and toBeOnTheScreen matchers.
totp-generator
Generate TOTP codes for 2FA authentication. Essential for my survival!
github-api
How to interact with GitHub API. Use this skill for repos, PRs, issues, and user operations.
file-editor
Edit files locally using built-in tools instead of PowerShell string replacement. 5000% efficiency boost!
browser
Browser automation using @sdjz/pup. AXTree scanning, stealth mode, DevTools access. Designed for AI agents.
shadow-directory + git
Full .ciallo directory ownership with git capabilities. Clone repos, edit files, commit changes directly.
aggregating-event-datasets
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_by for grouping, aggregation functions (count, sum, avg, percentile), and topk for top N results. Returns single summary row per group across entire time range. For time-series trends, see time-series-analysis skill.
filtering-event-datasets
Filter and search event datasets (logs) using OPAL. Use when you need to find specific log events by text search, regex patterns, or field values. Covers contains(), tilda operator ~, field comparisons, boolean logic, and limit for sampling results. Does NOT cover aggregation (see aggregating-event-datasets skill).
field-extraction-parsing
Extract structured fields from unstructured log data using OPAL parsing functions. Covers extract_regex() for pattern matching with type casting, split() for delimited data, parse_json() for JSON logs, and JSONPath for navigating parsed structures. Use when you need to convert raw log text into queryable fields for analysis, filtering, or aggregation.
subquery-patterns-and-union
Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Covers @label <- @ syntax, timeshift for temporal shifts, union for combining results, and any_not_null() for collapsing grouped data.
analyzing-text-patterns
Extract and analyze recurring patterns from log messages, span names, and event names using punctuation-based template discovery. Use when you need to understand log diversity, identify common message structures, detect unusual formats, or prepare for log parser development. Works by removing variable content and preserving structural markers.
analyzing-tdigest-metrics
Analyze percentile metrics (tdigest type) using OPAL for latency analysis and SLO tracking. Use when calculating p50, p95, p99 from pre-aggregated duration or latency metrics. Covers the critical double-combine pattern with align + m_tdigest() + tdigest_combine + aggregate. For simple metrics (counts, averages), see aggregating-gauge-metrics skill.
aggregating-gauge-metrics
Aggregate pre-computed metrics (gauge, counter, delta types) using OPAL. Use when analyzing request counts, error rates, resource utilization, or any numeric metrics over time. Covers align + m() + aggregate pattern, summary vs time-series output, and common aggregation functions. For percentile metrics (tdigest), see analyzing-tdigest-metrics skill.
analyzing-apm-data
Monitor application performance using the RED methodology (Rate, Errors, Duration) with Observe. Use when analyzing service health, investigating errors, tracking latency, or building APM dashboards. Covers when to use metrics vs spans, combining RED signals, and troubleshooting workflows. Cross-references working-with-intervals, aggregating-gauge-metrics, and analyzing-tdigest-metrics skills.
detecting-anomalies
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier detection (Z-score, IQR), threshold-based alerts, rate-of-change detection with window functions, and moving average baselines. Choose pattern based on data distribution and anomaly type.
working-with-resources
Work with Resource datasets (mutable state tracking) using OPAL temporal joins. Use when you need to enrich Events/Intervals with contextual state information, track resource state changes over time, or navigate between datasets using temporal relationships. Covers temporal join mechanics (lookup, join, follow), automatic field matching, and when to use Resources vs Reference Tables.
working-with-intervals
Work with Interval datasets (time-bounded data) using OPAL. Use when analyzing data with start and end timestamps like distributed traces, batch jobs, or CI/CD pipeline runs. Covers duration calculations, temporal filtering, and aggregating by time properties. Intervals are immutable completed activities with two timestamps, distinct from Events (single timestamp) and Resources (mutable state).
window-functions-deep-dive
Master OPAL window functions for row-relative calculations, rankings, and moving aggregates. Covers lag(), lead(), row_number(), rank(), dense_rank(), moving averages, first(), and last(). Use when comparing rows to neighbors, ranking within partitions, calculating rate of change, or computing time-based moving windows. CRITICAL - OPAL uses window() function wrapper, NOT SQL OVER clause.
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