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manage-identifiers

Generate sequential zero-padded identifiers in RepoName:NNNNNN format and validate for duplicates and gaps.

1|Updated Mar 7, 2026
Search Tags:#knowledge graph#validation#data integrity#repository management#batch processing#id management
One-click install
npx skills add https://github.com/CultureBotAI/MediaIngredientMech --skill manage-identifiers
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: manage-identifiers
Source: https://github.com/CultureBotAI/MediaIngredientMech/tree/main/.claude/skills/manage-identifiers
Command: npx skills add https://github.com/CultureBotAI/MediaIngredientMech --skill manage-identifiers

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, PyYAML, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a robust and standardized method for generating, managing, and validating unique identifiers across multiple related repositories, ensuring data integrity and cross-referencing capabilities.

Core Features & Use Cases

  • ID Minting: Generates sequential, zero-padded IDs in the RepoName:NNNNNN format.
  • Collection Type Handling: Supports both single-file and multi-file data structures for ID management.
  • Batch Operations: Offers scripts for bulk ID assignment and registry updates.
  • Validation: Includes functions to check for duplicate IDs and gaps in sequences.
  • Use Case: When adding a new ingredient to MediaIngredientMech, this Skill can find the next available ID, format it correctly, and guide you through adding it to the unmapped_ingredients.yaml file, ensuring consistency with all other ingredients.

Quick Start

Use the manage-identifiers skill to mint the next available ID for a new record in the MediaIngredientMech repository.

Frequently Asked Questions about manage-identifiers

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate sequential identifiers for YAML files in a knowledge graph repository?▼

Sequential identifiers for YAML files are generated using a standardized RepoName:NNNNNN format with zero-padded numbers. This Skill mints unique IDs for single-file and multi-file data structures, ensuring cross-referencing and data integrity across related knowledge graph projects.

What's the best way to check for duplicate IDs and sequence gaps in a repository?▼

Checking for duplicate IDs and sequence gaps is handled by built-in validation utilities that analyze your registry. These functions scan existing identifiers to detect duplicates and missing numbers, ensuring sequential integrity across your repository data.

Can I assign identifiers in bulk across multiple files?▼

Yes, bulk identifier assignment across multiple files is supported through batch processing scripts. The Skill handles both single-file and multi-file collection types, allowing you to update registries and assign sequential IDs in bulk.

Do I need pandas and PyYAML to manage repository identifiers?▼

Yes, pandas and PyYAML are required dependencies for managing repository identifiers. Pandas handles batch data processing and validation analysis, while PyYAML enables reading and updating the YAML data files where identifiers are stored.

Why use a standardized ID format for cross-referencing knowledge graph projects?▼

A standardized ID format like RepoName:NNNNNN is used to maintain data integrity and enable reliable cross-referencing across related knowledge graph projects. Consistent formatting prevents ID collisions and ensures each record remains uniquely identifiable.

How do I find the next available ID before adding a new record to a YAML file?▼

Finding the next available ID involves scanning existing identifiers in your YAML registry to determine the highest sequence number. The Skill automates this lookup, formats the next zero-padded ID, and guides you through adding it to files like unmapped_ingredients.yaml.

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