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Canonical Format Checker

Listed
panaversity·vlatest·utilities

Ensures documents adhere to Panaversity's defined canonical format for consistent data extraction and processing.

Install on your platform

This skill no longer installs under the name canonical-format-checker. panaversity/agentfactory is deleted or private, so it cannot be installed from.

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Repository install instructions

What we know about this skill

Publisher

panaversity

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

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scanned

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Not scanned. repository not found or private Source recorded as panaversity/agentfactory.
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What each check looks for →

About this skill

The canonical-format-checker skill validates that AI agent outputs adhere to a specific, standardized format. It ensures consistency in data structures like JSON or XML before the agent proceeds with further processing tasks.

When to use it

  • Enforcing strict output schemas for downstream API integrations.
  • Preventing parsing errors when agents generate code or configuration files.
  • Standardizing responses across multiple agents within a single workflow.
  • Validating structured data before storing it in a database.

Key capabilities

  • Detects deviations from predefined canonical formats.
  • Validates structural integrity of generated content.
  • Ensures uniformity in agent output for reliable automation.

Example prompts

  • "Generate a JSON response that strictly follows the canonical format for user profile data."
  • "Check if the following XML output matches the required canonical structure for event logging."
  • "Refactor this agent's output to ensure it conforms to the standard API response schema."

Tips & gotchas

Ensure your agent is explicitly provided with the target canonical format definition, as the skill relies on comparing outputs against these standards. This tool is best used in automated pipelines where data consistency is critical for subsequent processing steps.