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github/generate-custom-instructions-from-codebase

Generador de instrucciones de migración y evolución de código para GitHub Copilot. Analiza las diferencias entre dos versiones de un proyecto (ramas, commits o lanzamientos) para crear instrucciones precisas que permitan a Copilot mantener la coherencia durante migraciones tecnológicas, refactorizaciones importantes o actualizaciones de versiones de frameworks.

¿Qué es generate-custom-instructions-from-codebase?

Esta habilidad permite a GitHub Copilot generar instrucciones personalizadas a partir del análisis de diferencias entre dos versiones de un código base. Es especialmente útil durante migraciones tecnológicas, refactorizaciones importantes o actualizaciones de versiones de frameworks, donde se necesita mantener la coherencia en el estilo y las convenciones del código. La habilidad funciona comparando dos puntos del proyecto (como ramas, commits o releases) y extrayendo los patrones de cambio. Luego, genera un conjunto de instrucciones en lenguaje natural que Copilot puede usar para guiar nuevas sugerencias de código, asegurando que sigan las mismas convenciones y estructura que los cambios ya realizados. De esta forma, los desarrolladores ahorran tiempo y evitan inconsistencias durante procesos de evolución del código.

Compatible con~Claude Code~Codex CLI~Cursor
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Documentación

¿Qué hace generate-custom-instructions-from-codebase?

Generador de instrucciones de migración y evolución de código para GitHub Copilot. Analiza las diferencias entre dos versiones de un proyecto (ramas, commits o lanzamientos) para crear instrucciones precisas que permitan a Copilot mantener la coherencia durante migraciones tecnológicas, refactorizaciones importantes o actualizaciones de versiones de frameworks.

¿Cómo funciona?

  1. Selecciona dos versiones del proyecto: Pueden ser ramas (main vs feature/nueva-api), commits (abc123 vs def456) o releases (v1.0 vs v2.0).
  2. Analiza las diferencias: Examina los cambios entre ambas versiones para identificar patrones: nombres de funciones, estructuras de importación, estilos de código, etc.
  3. Genera instrucciones: Crea un conjunto de instrucciones claras y específicas que Copilot puede usar para mantener la coherencia con la versión objetivo.

¿Por qué es útil?

  • Migraciones tecnológicas: Al cambiar de una librería a otra (ej. de axios a fetch), esta habilidad genera instrucciones para que Copilot use la nueva sintaxis de forma consistente.
  • Refactorizaciones importantes: Si renombras funciones o cambias la estructura de módulos, las instrucciones evitan que Copilot siga sugiriendo el código antiguo.
  • Actualizaciones de frameworks: Al actualizar de una versión mayor a otra (ej. React 16 a React 18), las instrucciones aseguran que Copilot adopte las nuevas APIs y patrones.

Plataformas soportadas

  • GitHub Copilot (Chat y sugerencias inline)
  • Compatible con cualquier repositorio de Git.

Ejemplo de uso

Supón que estás migrando un proyecto de JavaScript a TypeScript. Con esta habilidad:

  1. Comparas la rama main (JS) con feature/typescript-migration (TS).
  2. Se generan instrucciones como:
    • "Usa tipos explícitos para todos los parámetros de función."
    • "Define interfaces para objetos con más de 2 propiedades."
    • "Usa import ES modules en lugar de require."
  3. Copilot aplica estas reglas automáticamente en nuevas sugerencias.

Requisitos

  • Git instalado en el sistema.
  • Acceso a los repositorios de Git locales o remotos.
  • GitHub Copilot activo en tu editor.

Comandos útiles

# Comparar dos ramas
github/generate-custom-instructions-from-codebase main feature/nueva-api

# Comparar dos commits
github/generate-custom-instructions-from-codebase abc123 def456

# Comparar dos releases (tags)
github/generate-custom-instructions-from-codebase v1.0 v2.0

Personalización

Puedes ajustar las instrucciones generadas editando el archivo .github/copilot-instructions.md en la raíz del repositorio. Este archivo será sobreescrito con las nuevas instrucciones.

Limitaciones

  • Las instrucciones se basan en diferencias visibles; cambios de estilo implícitos pueden no detectarse.
  • Es necesario que las diferencias entre versiones sean representativas (al menos 10-15 archivos modificados es ideal).

Contribuciones

Si encuentras errores o mejoras, por favor abre un issue o PR en el repositorio oficial.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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