Habitat-Thinking/ai-literacy-superpowers

The AI Literacy framework's complete development workflow — harness engineering, agent orchestration, literate programming, CUPID code review, compound learning, and the three enforcement loops

Agents accountable for helping to maintain human understanding. Ships the `diagnostic-legibility` agent — builds and self-challenges two models of a codebase scope (architectural moving parts and domain concepts) via a five-question retained-challenge cycle, then cross-checks the two collections against each other via a five-question per-direction cycle. The `/diagnose` command surfaces the mutually-corrected models on demand as a readable report. The `ConceptualPipelineMap` template adds a standalone, presentation-agnostic flow-perspective data model; the agent's `scope-resolution` mode answers 'what does my task touch?' (a bounded, disclosed scope from a work task); and its `pipeline` mode traces control flow within that bound into a ConceptualPipelineMap alongside the architectural/domain collections, then cross-checks all three against each other (the maximal six directed pairs) for mutual correction. The `/pipeline-map "<task>"` command renders the task-scoped map as a self-contained HTML flowchart (pinned, SHA-verified Mermaid inlined; no CDN) with a scope-resolution panel, node-detail table, and no-JS fallback; `--predict-change` adds an opt-in change-site prediction (which stages the task will modify and where it will insert new ones) disclosed as a prediction, never a directive.

Researches and authors Mitchell-extended model cards from a model name.