aggregate-codify-proposals

Aggregate inferred proposal artifacts into a validated master proposals.yaml index.

3|Updated Jan 25, 2026

Search Tags:#proposals #pipeline orchestration #YAML indexing #taxonomy classification #enterprise codify #STM artifacts #deterministic aggregation

Ready to run

npx skills add https://github.com/kapilvirenahuja/garura --skill aggregate-codify-proposals

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aggregate-codify-proposals
Source: https://github.com/kapilvirenahuja/garura/tree/main/core/components/skills/aggregate-codify-proposals
Command: npx skills add https://github.com/kapilvirenahuja/garura --skill aggregate-codify-proposals

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It turns a folder of many inferred proposal artifacts into one validated, taxonomy-classified master proposals index that downstream systems can reliably consume.

Core Features & Use Cases

Deterministic proposal aggregation: Scans the STM inference output tree and composes a single proposals.yaml master index.
Taxonomy classification by learning category: Classifies each proposal into the correct two-level learning taxonomy (learning_category + sub_category).
Validation with strict failure modes: Enforces required metadata fields, confidence tiers, and tier/path alignment to prevent silent data loss.
Downstream contract for enrichment: Produces the exact artifact that /garura:enrich uses to promote proposals into product LTM.

Quick Start

Ask the system to run the /codify play so it calls aggregate-codify-proposals and writes proposals.yaml to the expected STM evidence output path.

Frequently Asked Questions about aggregate-codify-proposals

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

FAQPage Schema

How do I aggregate multiple inferred YAML artifacts into a single master proposals index?▼

To aggregate YAML artifacts into a master proposals index, you recursively scan the inference output tree, parse strict metadata, validate schemas against taxonomy tiers, and compose a single proposals.yaml file with deterministic proposal_id generation from content hashes.

What is deterministic proposal aggregation and how does it prevent silent data loss?▼

Deterministic proposal aggregation generates stable proposal_id values from content hashes during YAML parsing. It enforces strict failure modes for required metadata fields, confidence tiers, and tier/path alignment to prevent silent data loss during the composition process.

How do I classify inferred proposal artifacts into a two-level learning taxonomy?▼

You classify inferred proposal artifacts into a two-level learning taxonomy by applying schema validation against taxonomy and tier expectations. The process assigns both learning_category and sub_category labels to each proposal within the master proposals.yaml index.

Can I use this proposal aggregation pipeline without an existing STM evidence tree?▼

No, this proposal aggregation pipeline requires an existing STM evidence tree. It applies to codify-phase workflows where many infer-*-from-code skills emit per-target proposal files under a shared STM evidence output path for recursive directory walking.

What is the best way to prepare inferred artifacts for downstream product LTM promotion?▼

The best way to prepare inferred artifacts for product LTM promotion is to validate metadata fields, classify proposals by learning category, and output a strict master proposals.yaml index. Downstream systems like the enrich process then reliably consume this exact artifact.

Why does my taxonomy classification fail when aggregating inferred proposal files?▼

Taxonomy classification fails during proposal aggregation when strict schema validation detects missing required metadata fields, misaligned confidence tiers, or incorrect tier/path alignment. These strict failure modes intentionally halt the process to prevent silent data loss.