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dsteven12
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file-input-schema

Define file metadata and folder tree schemas for normalization.

1|Updated Mar 24, 2026
Search Tags:#schema#provenance#normalization#xml#prompting#structured-input#file-input
One-click install
npx skills add https://github.com/dsteven12/airtable-sa-skills --skill file-input-schema
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: file-input-schema
Source: https://github.com/dsteven12/airtable-sa-skills/tree/main/skills/file-input-schema
Command: npx skills add https://github.com/dsteven12/airtable-sa-skills --skill file-input-schema

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Shared XML schema and normalization rules for file-based inputs. This schema is imported by structured-input during its normalization pass — it is never invoked directly and consuming skills never need to read it. It defines: file metadata fields, folder tree representation, source provenance tracking, a confidence-tiered prompting protocol for inference vs. flagging vs. asking, and cost-of-wrong-guess thresholds per field.

Core Features & Use Cases

  • File Entry Schema: standardizes metadata for every file, ensuring consistent extraction of path, type, mime, size_bytes, modified, source, and provenance.
  • Folder Tree Schema: defines how directories are represented, including depth, file_count, file_types, and last_modified; supports recursive but capped depth to keep context manageable.
  • Normalization Protocol: outlines the steps, confidence scoring, and prompting behavior used by structured-input during file normalization.

Quick Start

Use this schema by placing the file-input-schema at the root of the skill and referencing it from the structured-input pipeline to enforce consistent file metadata and provenance tracking.

Frequently Asked Questions about file-input-schema

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

FAQPage Schema
How do I standardize file inputs for reliable AI workflows?▼

Standardize file inputs by defining a shared XML schema for file metadata, folder trees, and provenance tracking to ensure consistent extraction. This schema specifies fields like path, type, mime, size_bytes, and source for deterministic normalization.

What is file provenance tracking in structured-input normalization?▼

File provenance tracking records the origin and context of uploads, vault references, and MCP attachments. It captures source and source_context fields during file normalization to maintain reliable metadata history for AI workflows.

How do I represent folder trees in AI prompts without exceeding context limits?▼

Represent folder trees with a recursive but capped depth schema that defines directory metadata like file_count, file_types, and last_modified. Capping depth keeps context manageable while preserving the structure needed for reliable file normalization.

How does confidence scoring work for file metadata extraction?▼

Confidence scoring for file metadata extraction uses a tiered prompting protocol to decide between inference, flagging, or asking. It applies cost-of-wrong-guess thresholds per field to prevent errors during structured-input normalization.

Do I need to directly invoke this file-input schema in my AI workflow?▼

You do not need to directly invoke the file-input schema or have consuming skills read it. Place the schema at the root and reference it from the structured-input pipeline to automatically enforce consistent metadata and provenance tracking.

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