2.0 KiB
Didactopus
Didactopus is a local-first AI-assisted autodidactic mastery platform built around concept graphs, evaluator-driven evidence, adaptive planning, mastery ledgers, curriculum ingestion, and human review of generated draft packs.
This revision
This revision adds a full pack-validation layer that checks cross-file coherence for Didactopus draft packs before import and during review.
The goal is to move beyond “does the directory exist and parse?” toward a more Didactopus-native notion of whether a pack is structurally coherent enough to use.
Why this matters
A generated pack may look fine at first glance and still contain internal problems:
- roadmap stages referencing missing concepts
- projects depending on nonexistent concepts
- duplicate concept ids
- rubrics with malformed structure
- empty or weak metadata
- inconsistent pack identity information
Those issues can become another activation-energy barrier. A user who has already done the hard work of finding course materials and generating a draft pack should not have to manually discover every structural issue one file at a time.
What is included
- full pack validator
- cross-file validation across:
pack.yamlconcepts.yamlroadmap.yamlprojects.yamlrubrics.yaml
- validation summary model
- import preview now includes pack-validation findings
- review UI panels for validation errors and warnings
- sample valid and invalid packs
- tests for coherence checks
Core checks
Current scaffold validates:
- required files exist
- YAML parsing for all key files
- pack metadata presence
- duplicate concept ids
- roadmap concepts exist in
concepts.yaml - project prerequisites exist in
concepts.yaml - rubric structure presence
- empty or suspiciously weak concept entries
Design stance
This is a structural coherence layer, not a guarantee of pedagogical quality. It makes the import path safer and clearer, while still leaving room for later semantic and domain-specific validation.