normalize-proposals-for-enrichment

Normalize extractor-specific proposals.yaml into reconciliation-proposals.yaml with taxonomy validation.

3|Updated Jan 25, 2026
One-click install
npx skills add https://github.com/kapilvirenahuja/garura --skill normalize-proposals-for-enrichment
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: normalize-proposals-for-enrichment
Source: https://github.com/kapilvirenahuja/garura/tree/main/core/components/skills/normalize-proposals-for-enrichment
Command: npx skills add https://github.com/kapilvirenahuja/garura --skill normalize-proposals-for-enrichment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill reconciles diverse, extractor-specific proposal formats into a single normalized reconciliation file so reviewers and downstream LTM writers can approve enrichments consistently.

Core Features & Use Cases

  • Source detection by STM path: Detects whether proposals came from distill, reap, codify, or decode and selects the correct mapping logic.
  • Taxonomy contract validation with visible rejects: Ensures required taxonomy fields are present and emits rejected entries (not silent drops) when the contract is violated.
  • Reviewer-ready reconciliation output: Produces a proposals file where every entry is initialized to approval_status: pending, ready for the enrich play’s reviewer gate.

Quick Start

Use normalize-proposals-for-enrichment to read a staged STM proposals.yaml from an extractor evidence folder and write a reconciliation-proposals.yaml at the enrich output path for reviewer approval.

Frequently Asked Questions about normalize-proposals-for-enrichment

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

FAQPage Schema
How do I normalize multiple YAML proposal formats into a single reconciliation file for review?

Normalizing multiple YAML proposal formats applies deterministic per-source transformations to reconcile diverse extractor outputs into a single file. It maps fields based on STM path detection, ensuring consistent reviewer approval and downstream LTM enrichment.

What happens when taxonomy contract validation fails during proposal normalization?

When taxonomy contract validation fails during proposal normalization, the process emits rejected entries rather than silently dropping them. This visible reject mechanism ensures missing or undetectable sources are explicitly reported in the output reconciliation file.

How are missing or undetectable data sources handled during YAML reconciliation?

Missing or undetectable data sources are handled through explicit rejected-entry reporting during YAML reconciliation. The process performs taxonomy contract validation and outputs rejected entries to ensure no source data is silently dropped before LTM enrichment.

How do I prepare reconciliation proposals for downstream LTM enrichment?

Preparing reconciliation proposals for LTM enrichment involves initializing every normalized entry with a pending approval status. This creates a reviewer-ready output file where target-path scope checks and taxonomy validation have already been applied.

Can I apply deterministic mapping logic for different extractor types in an agentic pipeline?

Yes, deterministic mapping logic can be applied for different extractor types by detecting the STM path source, such as distill, reap, codify, or decode. The process selects the correct mapping transformation for each specific extractor evidence folder.