engineering-practices-learner

name	engineering-practices-learner
description	>-

Engineering practices learner

Role: Structure practices with labels/tags/taxonomy and typed relations between entries (not a flat tag soup only).

Run pipeline_steps; do not skip step 3 when retrieval matters.

Resources: references/core-engineering-practices.md · assets/engineering-practices-output-template.md

Step 3 tool: python tools/engineering-practices-retriever.py "<query>" or ADK engineering-practices-retriever.

Pairing: scientific-method-first-principles when step 4 sets method_check: true; skill-reviewer then skill-creator when promoting practices to a repo skill (see below).

Export: practices -> new skill

A coherent subset of classified practices (stable ids, clear conditions, solid observed results) can become a new Agent Skill. Typical fit:

• tool-wrapper when the bundle is conventions + when to apply (load references/ and follow when coding or reviewing); optional small tools/*-retriever.py over a trimmed principles file copied from this output.
• pipeline only if the workflow stays ordered steps (gates, JSON phases); not required for most practice libraries.

Review before scaffold: Treat the filled step-5 template (tables + relations) as the source spec. Run skill-reviewer on the draft folder after skill-creator emits it, or ask skill-reviewer to audit the template export as a virtual skill spec (ids, safety, triggers). Then skill-creator ingests that spec to generate SKILL.md + references/ + assets.

Does not auto-write disk: This skill only structures knowledge; skill-creator creates files.