learningfoundry 0.73.0

A curriculum engine that turns a YAML curriculum definition into a deployable SvelteKit learning application.

learningfoundry

A curriculum engine that turns a YAML curriculum definition into a deployable SvelteKit learning application — with interactive assessments, executable notebooks, and data visualizations — in a single pipeline.

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Table of Contents

• Overview
• Installation
• Quick Start
• CLI Reference
• Curriculum YAML Format
• Video blocks
• Lesson titles and markdown headings
• Images and assets
• Pedagogical authoring
• Content locking
• Configuration File
• Development Setup
• Maintenance

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Overview

learningfoundry takes a single curriculum.yml file and generates a fully self-contained SvelteKit learning application. The generated app supports:

• Text — Markdown content rendered in the browser
• Video — YouTube embeds
• Assessment — Interactive assessments via quizazz (optional)
• Exercise — Executable notebooks via nbfoundry (stub provided)
• Visualization — D3-based charts via d3foundry (stub provided)

Learner progress is persisted locally in SQLite (via sql.js) — no backend required.

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Installation

pip install learningfoundry

With optional quizazz support:

pip install "learningfoundry[quizazz]"

Requirements:

• Python 3.12+
• pnpm (for preview command and generated app development)
• Node.js 18+ (for the generated SvelteKit app)

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Quick Start

1. Create a curriculum file (see Curriculum YAML Format):

   cat > curriculum.yml << 'EOF'
   version: "1.0.0"
   curriculum:
     title: "My Course"
     description: "A short description."
     modules:
       - id: mod-01
         title: "Module One"
         lessons:
           - id: lesson-01
             title: "Getting Started"
             content_blocks:
               - type: text
                 ref: content/lesson-01.md
               - type: video
                 url: "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
   EOF
   

2. Validate the curriculum:

   learningfoundry validate
   # OK — curriculum is valid.
   

3. Build and preview locally:

   learningfoundry preview
   # Preview server started at http://localhost:5173
   

   learningfoundry preview is the canonical "see your work" command — it builds the SvelteKit project, installs Node dependencies on first run (and again whenever they change), and starts a Vite dev server. On subsequent runs it skips the install step automatically.

   learningfoundry build alone is also available if you want to generate the SvelteKit project without serving it (e.g. to inspect output, deploy a static export via cd dist && pnpm build, or wire into your own toolchain).

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CLI Reference

learningfoundry build

Parse → resolve → generate a SvelteKit project.

Usage: learningfoundry build [OPTIONS]

Options:
  -c, --config PATH       Path to the curriculum YAML file.  [default: curriculum.yml]
  --log-level LEVEL       Logging verbosity.  [default: INFO]
                          Choices: DEBUG, INFO, WARNING, ERROR
  -o, --output PATH       Output directory for the generated SvelteKit project.
                          [default: dist]
  --base-dir PATH         Base directory for content refs.
                          (default: curriculum file's parent directory)
  --help                  Show this message and exit.

Exit codes:

Code	Meaning
0	Success
1	Curriculum validation error
2	Content resolution error (missing file, bad URL, etc.)
3	SvelteKit generation error
4	Configuration file error

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learningfoundry validate

Validate a curriculum YAML without generating any output.

Usage: learningfoundry validate [OPTIONS]

Options:
  -c, --config PATH       Path to the curriculum YAML file.  [default: curriculum.yml]
  --log-level LEVEL       Logging verbosity.  [default: INFO]
  --base-dir PATH         Base directory for resolving content refs.
  --help                  Show this message and exit.

Prints OK — curriculum is valid. on success, or a list of errors and exits with code 1.

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learningfoundry preview

Build then launch a local Vite dev server.

Usage: learningfoundry preview [OPTIONS]

Options:
  -c, --config PATH       Path to the curriculum YAML file.  [default: curriculum.yml]
  --log-level LEVEL       Logging verbosity.  [default: INFO]
  -o, --output PATH       Output directory for the generated SvelteKit project.
                          [default: dist]
  --base-dir PATH         Base directory for content refs.
  --port INTEGER          Port for the local dev server.  [default: 5173]
  --help                  Show this message and exit.

Runs learningfoundry build, then pnpm install (skipped when every declared dependency is already present in node_modules/), then pnpm run dev in the generated project directory. Requires pnpm on PATH.

This serves the SvelteKit project from source via Vite's dev server; it does not serve the static pnpm build output in dist/build/. For static deploys, use cd dist && pnpm build and host the resulting dist/build/ directory on any static host.

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Curriculum YAML Format

version: "1.0.0"

curriculum:
  title: "Course Title"           # required
  description: "Course overview." # optional

  modules:
    - id: mod-01                  # required, kebab-case
      title: "Module One"         # required
      description: "..."          # optional

      # Optional assessments (requires quizazz-builder).
      # Each assessment carries an open-string `role` and a `position`
      # (`before_lessons`, `after_lessons`, or `{ before_lesson: <id> }`
      # / `{ after_lesson: <id> }`). The order in `assessments` after
      # build is the canonical placement order.
      assessments:
        - role: pre
          position: before_lessons
          source: quizazz
          ref: assessments/mod-01-pre.yml

        - role: practice
          position: { before_lesson: lesson-04 }
          source: quizazz
          ref: assessments/mod-01-practice.yml
          pass_threshold: 0.7

        - role: post
          position: after_lessons
          source: quizazz
          ref: assessments/mod-01-post.yml
          pass_threshold: 0.8

      lessons:
        - id: lesson-01           # required, kebab-case; unique within module
          title: "Lesson One"     # required

          content_blocks:

            # Text block — Markdown file
            - type: text
              ref: content/mod-01/lesson-01.md

            # Video block — `provider` selects the player (default: youtube)
            - type: video
              url: "https://www.youtube.com/watch?v=XXXXXXXXXXX"
              # provider: youtube          # optional today; only youtube is implemented
              # extensions: {}            # optional; player-specific payload (see "Video blocks")

            # Assessment block — requires learningfoundry[quizazz];
            # see "Embedding a quizazz assessment" below for the full author flow
            - type: assessment
              source: quizazz
              ref: assessments/mod-01-assessment.yml

            # Exercise block — requires nbfoundry (stub included)
            - type: exercise
              source: nbfoundry
              ref: exercises/mod-01-exercise.yml

            # Visualization block — requires d3foundry (stub included)
            - type: visualization
              source: d3foundry
              ref: visualizations/mod-01-vis.yml

Rules:

• Module and lesson id values must be unique within their scope, and match the pattern [a-z0-9][a-z0-9-]*.
• Every curriculum must have at least one module; every module at least one lesson.
• All ref paths are resolved relative to --base-dir (default: directory containing the curriculum YAML).
• Only YouTube URLs are accepted for video blocks when provider is youtube (the default): youtube.com/watch?v= or youtu.be/.

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Video blocks

Each video content block carries:

• url — Watch URL for the provider (validated for YouTube when provider: youtube).
• provider — Which player to use. Omitted in YAML means youtube. New providers (e.g. Vimeo) will add new literal values here together with resolver + frontend support.
• extensions — Optional mapping of player-specific data. There is no cross-player generic schema: keys and shapes are defined per provider. Examples you might add later for YouTube: chapters (timestamp + title list), transcript_ref (path to WebVTT or plain text), autoplay. The build passes extensions through to curriculum.json unchanged; the Svelte app can grow per-provider components that read content.extensions.

Older generated apps only had url in each video block’s content; the template still treats missing provider as youtube.

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Lesson titles and markdown headings

Each lesson page renders two title strings, from two different sources:

1. The lesson title from curriculum.yml (the title: on a lesson). Used by the sidebar, the breadcrumb, the browser tab, and the page's outer <h1>.
2. The leading heading in the lesson's markdown file (the # Heading at the top, if any). Rendered inside the lesson body.

If both strings are identical, the page renders the same title twice and looks broken. The fix is purely an authoring convention — there is no rendering bug to chase.

Convention

• Keep the YAML title: short and navigation-shaped. Either a number ("3"), a label ("Lesson 3"), or label-plus-abbreviation ("Lesson 3: Cultural Diffusion").
• Make the markdown # Heading the descriptive long-form title that complements the YAML title — never echoes it. Imagine reading them together as "<yaml title>: <markdown H1>"; that sentence should flow naturally and contain no repeated words.
• If the lesson genuinely has nothing extra to add in a heading, omit the markdown # Heading entirely and start the lesson with body prose. The page already has the YAML title rendered as its <h1>.

Examples

Good — complementary, reads as one sentence:

# curriculum.yml
- id: lesson-03
  title: "Lesson 3"

<!-- content/mod-01/lesson-03.md -->
# The Diffusion of Cultural Artifacts

Most cultural products fail. A reasonable estimate places the fraction…

Renders as:

Lesson 3

The Diffusion of Cultural Artifacts

Most cultural products fail. …

Good — slightly more YAML detail, still no echo:

- id: lesson-03
  title: "Lesson 3: Cultural Diffusion"

# Why Most Pop Releases Disappear

Bad — duplicative; both titles render the same string:

- id: lesson-03
  title: "The Diffusion of Cultural Artifacts"

# The Diffusion of Cultural Artifacts

Also fine — no markdown heading at all:

- id: lesson-03
  title: "Lesson 3: Cultural Diffusion"

Most cultural products fail. A reasonable estimate places the fraction…

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Images and assets

Lesson markdown can embed images directly. Place the image file alongside the markdown that uses it and reference it with a relative path:

content/
└── mod-01/
    ├── lesson-01.md
    ├── diagram.png
    └── figures/
        └── architecture.svg

# Lesson One

![Architecture diagram](figures/architecture.svg "Hover title")

Here is a smaller inline diagram:

<img src="diagram.png" alt="Diagram" />

How it works:

• Relative URLs (diagram.png, figures/architecture.svg) are resolved against the markdown file's own directory. learningfoundry build copies each unique image into dist/static/content/<sha256[:12]>/<basename> and rewrites the markdown URL to the absolute path /content/<sha256[:12]>/<basename> so it resolves at every nested route in the generated app.
• Both the markdown form (![alt](path), ![alt](path "title")) and the HTML form (<img src="path">) are recognised.
• Absolute URLs (https://, http://, protocol-relative //..., root-absolute /...) and data: URIs pass through unchanged — useful for CDN-hosted assets you don't want copied into the build.
• Image references inside fenced code blocks (``` or ~~~) are left as literal text, so code samples that demonstrate image syntax aren't silently rewritten.
• The same image referenced from N lessons is copied exactly once (deduped by content hash).
• A missing image fails the build with the lesson location and the expected on-disk path in the error message.

For production deployment to a CDN, just run cd dist && pnpm build — the static/content/ tree gets bundled into the static export under build/content/, so deploying build/ to any static host (Cloudflare Pages, Netlify, S3+CloudFront, …) serves the images at the same URLs the markdown references.

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Pedagogical authoring

Phase J adds first-class authoring affordances for the worked-example → faded-example → independent-practice progression and for declaring pedagogical context the build pipeline can act on. Three building blocks compose into one story:

1. meta blocks declare the intent of a module or lesson — its theme, role, opening hook, learning items, and time estimate.
2. Container directives in lesson markdown style worked / faded / independent-practice cards inline.
3. assessments[] on each module places assessments at named positions (before all lessons, before/after a specific lesson, after all lessons) — replacing the legacy pre_assessment / post_assessment pair (see migration note below).

The subsections below cover each in detail — the meta reference and custom meta fields, the tutorial scaffold directives, embedding a quizazz assessment (full author flow for the assessment content-block type), the module-level Assessments model, and the migration path from the legacy pre_assessment / post_assessment pair.

A small worked example bringing the three together:

modules:
  - id: mod-01
    title: "Why convolutions exist"

    meta:
      theme: "Why convolutions exist"
      objectives:
        - "Explain why FC nets fail on images"
        - "Describe weight sharing"
      target_audience: "Intermediate Python; high-school math"

    assessments:
      - role: pre
        position: before_lessons
        source: quizazz
        ref: assessments/mod-01-pre.yml
      - role: practice
        position: { before_lesson: lesson-02 }
        source: quizazz
        ref: assessments/mod-01-practice.yml
        pass_threshold: 0.7
      - role: post
        position: after_lessons
        source: quizazz
        ref: assessments/mod-01-post.yml
        pass_threshold: 0.8

    lessons:
      - id: lesson-01
        title: "What is a convolution?"
        meta:
          role: opener
          hook:
            tagline: "What if your first layer of vision was just a flashlight on the world?"
          introduces: [receptive_field, simple_cells]
          duration_minutes: 15
        content_blocks:
          - type: text
            ref: content/mod-01/lesson-01.md

And the lesson markdown can sprinkle in the three directives:

# What is a convolution?

::: worked-example
Compute the output shape for a 32×32 input, 3×3 kernel, stride 1, padding 0.

We apply $(W - K + 2P) / S + 1 = 30$. Output: **30×30**.
:::

::: faded-example
For a 64×64 input, 5×5 kernel, stride 1, padding 2 — what's the output shape?
:::

::: independent-practice
Given a 28×28 input, design a `Conv2d` that outputs 14×14. State your kernel, stride, and padding.
:::

meta reference

Lesson meta carries:

• role — open string, conventional values opener, concept, story, math, tutorial, practice, hands_on, bonus. Renders as a small chip in the sidebar.
• hook — { tagline, image_prompt? }. The tagline renders as a quiet italic line above the lesson title.
• introduces / reinforces — lists of learning-item ids (open vocabulary; useful for downstream tooling).
• duration_minutes — integer; aggregated across the curriculum into total_duration_minutes and surfaced on the index page as ≈ Xh Ym.

Module meta carries:

• theme, big_problem, objectives, experiential_summary, target_audience. Rendering of these is deferred; today they pass through to curriculum.json for downstream tooling.

Curriculum meta (Story J.h) carries:

• target_audience, objectives, prerequisites. Curriculum-wide pedagogical context — passed through to curriculum.json for downstream tooling, no rendering in v1.

Custom meta fields

All three meta models — CurriculumMeta, ModuleMeta, LessonMeta (and the hook sub-block) — accept undeclared fields. Authors can attach genre-specific data alongside the declared ones at any layer without a schema change:

version: "1.0.0"
curriculum:
  title: "Convolutional Neural Networks"
  meta:
    # Declared fields — type-checked:
    target_audience: "Working software engineers new to ML"
    objectives: ["Explain backprop", "Build a conv net"]

    # Author-defined extras — accepted as-is:
    pedagogical_approach: "spiral"
    estimated_total_minutes: 480

  modules:
    - id: mod-01
      title: "Why convolutions exist"
      meta:
        # Declared + extras compose the same way at module level:
        theme: "Why convolutions exist"
        objectives: ["Explain weight sharing"]
        curriculum_thread: "vision"          # author-defined extra

      lessons:
        - id: lesson-01
          title: "What is a convolution?"
          meta:
            # Declared fields — type-checked:
            role: opener
            introduces: [receptive_field]
            duration_minutes: 15

            # Author-defined extras — accepted as-is:
            covers: ["pe:hubel-wiesel", "hi:receptive-field-discovery"]
            difficulty: intermediate
            prerequisites: [lesson-00]
            author_notes: "Revisit after the kernel-size deep-dive lands."
          content_blocks:
            - type: text
              ref: content/mod-01/lesson-01.md

The escape hatch is scoped to meta (and hook) only. CurriculumDef, Module, Lesson, and the top-level curriculum: mapping itself reject unknown fields — so a misplaced difficulty: at the lesson level (sibling of meta, not nested inside it) still fails the build. Same for a stray pedagogical_approach: at the curriculum level outside the meta: block. The strictness that catches typos like a mis-nested sequential: true is preserved everywhere outside the meta blocks at all three layers.

Declared fields keep their normal type checks; only undeclared keys ride through unvalidated. Extras pass through unchanged into the generated curriculum.json, so downstream tooling (custom Svelte components, analytics dashboards, external reports) can read them without any further pipeline wiring.

Strict project-specific extensions

The permissive extra="allow" posture above is too loose for LLM-driven authoring — an LLM that writes prequisites instead of prerequisites will pass validation, lose the data in curriculum.json, and break downstream consumers silently. The schema-extensions mechanism is an opt-in tightening: a project drops learningfoundry-schema-extensions.yml next to its curriculum.yml, declares the additional fields it cares about, and learningfoundry flips the meta blocks from "allow anything" to "reject anything not on the list."

Minimal example — learningfoundry-schema-extensions.yml:

version: "1"
lesson_meta:
  fields:
    covers:        { type: list[str], default: [] }
    difficulty:    { type: enum, values: [intro, intermediate, advanced] }
    prerequisites: { type: list[str], default: [] }

With this file in place:

# curriculum.yml lesson — typo `prequisites` instead of `prerequisites`
meta:
  difficulty: intermediate
  prequisites: [lesson-00]   # ❌ now a build error

learningfoundry validate exits non-zero with a message naming the offending field (the Pydantic ValidationError puts prequisites directly in the output). Without the extensions file, the same typo silently passes — the original extra="allow" posture is preserved.

Supported field types: str, int, bool, list[str], enum (with values: list). Each field accepts required: bool (default true) and default: (presence makes the field optional). Per-model extra: allow overrides the default extra: forbid if you want one meta layer tight and another loose during a staged rollout.

File-path resolution order (highest precedence first):

1. --schema-extensions PATH CLI flag on build, validate, preview.
2. [tool.learningfoundry] schema_extensions = "..." in pyproject.toml next to the curriculum.
3. Auto-discovery: learningfoundry-schema-extensions.yml next to the curriculum.
4. None — base extra="allow" behaviour, no enforcement.

The extensions file is itself strict-validated, so a typo there (e.g. defalt: instead of default:) fails at load time naming the field, not silently degrading the contract the file is supposed to tighten. The mechanism applies to all three meta layers (curriculum_meta, module_meta, lesson_meta) independently — declaring one does not require declaring the others.

Tutorial scaffold directives

Three named container directives:

• ::: worked-example — filled gray card. Use it for fully worked solutions.
• ::: faded-example — outlined dim card. Use it for similar problems with reduced scaffolding.
• ::: independent-practice — amber-highlighted challenge prompt. Use it for problems the learner solves on their own.

Inner markdown (headings, lists, math, emphasis) renders normally inside each directive. Unknown directive names pass through untouched at render time. Static styling only — no progressive-reveal interactivity in v1. An unbalanced known-name block (open with no ::: close on its own line) fails the build with the lesson location, so the failure mode is loud rather than rendered as silent prose.

Embedding a quizazz assessment

quizazz is the default assessment provider — interactive question/answer blocks with scoring, review, and progress persistence. This subsection walks the full author flow once; cross-links point at the canonical sources for the parts owned by quizazz (assessment-YAML schema, vendor component behavior).

What you need. pip install learningfoundry[quizazz] installs the Python builder side (the quizazz PyPI package) so learningfoundry build can compile assessment YAML at build time. The SvelteKit template already declares @pointmatic/quizazz as a runtime dependency, so the vendor component is wired into the generated app automatically — no separate npm / pnpm install step for the author.

Where the assessment content lives. One *.yml file per assessment, conventionally under an assessments/ directory inside your curriculum source tree. The assessment-YAML schema is owned by quizazz — for the authoritative format (question types, answer counts, weighted scoring rules, review options), see quizazz README and quizazz features.md. Do not consult learningfoundry's docs for the schema; this README only covers how learningfoundry references quizazz YAML files.

Two ways to embed. Pick based on the pedagogical role:

1. Inline in a lesson — add a type: assessment entry to a lesson's content_blocks. Use this for in-lesson knowledge checks that interrupt the reading flow at a specific point.
2. At module level — add an entry to the module's assessments[] array with a position. Use this for module-level pre / practice / post placements that bracket multiple lessons. See the Assessments subsection below for the full positional vocabulary.

Both shapes select quizazz via source: quizazz; both accept an optional pass_threshold. The schema of the referenced YAML files is identical regardless of which embedding shape points at them — quizazz doesn't know or care which one called.

Worked example. A curriculum.yml snippet using both shapes against the same provider:

modules:
  - id: mod-01

    # Module-level: opens the module with a pre-assessment, closes with a graded post
    assessments:
      - role: pre
        position: before_lessons
        source: quizazz
        ref: assessments/mod-01-pre.yml
      - role: post
        position: after_lessons
        source: quizazz
        ref: assessments/mod-01-post.yml
        pass_threshold: 0.8

    lessons:
      - id: lesson-02
        content_blocks:
          - type: text
            ref: content/mod-01/lesson-02.md
          # Content-block-level: a quick check inside lesson 2
          - type: assessment
            source: quizazz
            ref: assessments/lesson-02-check.yml
            pass_threshold: 0.6

The referenced file (assessments/lesson-02-check.yml) is a standalone quizazz YAML file — its schema is described in quizazz README. A minimal one looks like:

# See quizazz docs for the full schema (question types, answer counts, scoring rules).
quizName: "Lesson 2 — knowledge check"
questions:
  - question: "Which kernel produces a vertical-edge detector?"
    correct: "[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]"
    ridiculous: "[[1, 1, 1], [1, 1, 1], [1, 1, 1]]"
    # ...remaining answer slots per quizazz's schema

What the learner sees. At build time, learningfoundry's QuizazzProvider invokes quizazz's compile_assessment API on each referenced YAML, embeds the compiled manifest into the generated SvelteKit app's curriculum.json, and the frontend mounts the vendor <QuizBlock> component to render the assessment inline. Each completed assessment fires a score event; learningfoundry persists {assessmentRef, score, maxScore} to the in-browser SQLite assessment_scores table. quizazz manages its own per-assessment IndexedDB database for per-question detail — the two storage layers are separate by design (see quizazz consumer-dependency-spec.md RR-1, RR-1a, RR-1b for the full contract).

Pass-threshold gating. Optional pass_threshold: 0.0–1.0 on either embedding shape. When set, the assessment block fires its completion event upward only when the learner's score / maxScore clears the threshold, which is what gates lesson-completion progression in the sidebar. When omitted (the default), every completion attempt counts as "complete" regardless of score — useful for self-paced check-yourself assessments where the goal is exposure to the questions rather than gating.

Common gotchas:

• Refs resolve relative to --base-dir. The ref: path is not relative to the lesson markdown or to curriculum.yml; it resolves under whatever directory learningfoundry build --base-dir <path> was given. The default --base-dir is the directory containing curriculum.yml.
• learningfoundry[quizazz] is an optional extra. Plain pip install learningfoundry does not pull in quizazz; running learningfoundry build on a curriculum that references source: quizazz will fail with an ImportError. Install the extra explicitly.
• <QuizBlock> is a vendor component name — preserved at the vendor boundary. A future "consistency rename" pass that tried to rename it to <AssessmentBlock> (learningfoundry's wrapper component) would break the integration silently. See the "Vendor terminology stops at the vendor boundary" note in project-essentials.md.

Assessments

Each module declares an assessments[] array; each entry carries:

• role — open string. Conventional values: pre, practice, post, checkpoint. Surfaces as a capitalized label in the sidebar (Pre Assessment, Practice Assessment, …).
• position — discriminated union:
  • before_lessons — anchors at the start of the module flow.
  • after_lessons — anchors at the end.
  • { before_lesson: <lesson-id> } — anchors immediately before the named lesson.
  • { after_lesson: <lesson-id> } — anchors immediately after.
• source, ref — provider + path, same shape as assessment content blocks.
• pass_threshold — optional 0.0–1.0. Recorded but not gating in v1; surfaces as a "X% to pass" annotation on the assessment row when set.

Lesson-anchored refs (before_lesson / after_lesson) are validated against the module's lessons at build time — typing a wrong lesson id fails the build with the module id, role, and unknown lesson id.

Migrating from pre_assessment / post_assessment (pre-v0.68.0)

Module.pre_assessment and Module.post_assessment were removed in v0.68.0 (Story J.e). To migrate an external curriculum that pre-dates the cutover, replace each block with a single assessments[] entry using the before_lessons or after_lessons position:

# BEFORE (v0.67.x and earlier)
pre_assessment:
  source: quizazz
  ref: assessments/mod-01-pre.yml
post_assessment:
  source: quizazz
  ref: assessments/mod-01-post.yml

# AFTER (v0.68.0+)
assessments:
  - role: pre
    position: before_lessons
    source: quizazz
    ref: assessments/mod-01-pre.yml
  - role: post
    position: after_lessons
    source: quizazz
    ref: assessments/mod-01-post.yml

Strict-mode Pydantic rejects an unmigrated pre_assessment / post_assessment field with a ValidationError naming the offending field, so the build fails loudly until the migration is complete. There is no compatibility shim or deprecation warning — pre-1.0 makes the clean break acceptable.

---

Content locking

Control access to modules and lessons with a three-level configuration hierarchy (most local wins):

1. Per-module locked — explicit true/false override; trumps everything.
2. Curriculum locking.sequential — when true, module N+1 requires module N complete.
3. Global config locking.sequential — project-wide default (see Configuration File below).

curriculum:
  locking:
    sequential: true            # modules must be completed in order
    lesson_sequential: false    # lessons within a module are free-order

  modules:
    - id: mod-01
      locked: false             # always accessible regardless of sequential
      lessons:
        - id: lesson-01
          unlock_module_on_complete: true   # completing this unlocks siblings + next module
          content_blocks:
            # See "Embedding a quizazz assessment" above for the full author flow
            - type: assessment
              source: quizazz
              ref: assessments/assessment.yml
              pass_threshold: 0.7           # 70% required to count as passed

unlock_module_on_complete is useful for "gateway" lessons — a single assessment that, once passed, opens the rest of the module and the next one.

---

Configuration File

An optional config file can set defaults for logging and locking. The CLI always takes precedence.

Default location: ~/.config/learningfoundry/config.yml

logging:
  level: INFO      # DEBUG | INFO | WARNING | ERROR
  output: stdout   # stdout | stderr

locking:
  sequential: false          # default for all curricula on this machine
  lesson_sequential: false

Pass a custom config location with -c / --config.

---

Development Setup

Prerequisites

• Python 3.12+
• pyve (virtual env manager used in this project)
• pnpm 9+ and Node.js 18+

Setup

git clone https://github.com/pointmatic/learningfoundry.git
cd learningfoundry

# Create the Python environment and install the package in editable mode
pyve init
pip install -e .

# Create the test runner environment and install dev dependencies
pyve testenv --init
pyve testenv --install -r requirements-dev.txt

Running Tests

# Fast unit + integration tests (~2 min)
pyve test

# End-to-end SvelteKit smoke tests (requires pnpm, ~15 s extra)
pyve test tests/test_smoke_sveltekit.py -v

Linting and Type Checking

pyve testenv run ruff check .
pyve testenv run mypy src/

Project Structure

learningfoundry/
├── src/learningfoundry/
│   ├── cli.py              # Click CLI entry point
│   ├── config.py           # Configuration loading
│   ├── exceptions.py       # Exception hierarchy
│   ├── generator.py        # SvelteKit project generator
│   ├── integrations/       # Assessment / exercise / visualization providers
│   ├── logging_config.py   # Logging setup
│   ├── parser.py           # YAML parser + version dispatch
│   ├── pipeline.py         # run_build / run_validate / run_preview
│   ├── resolver.py         # Content reference resolver
│   └── schema_v1.py        # Pydantic v1 curriculum schema
├── sveltekit_template/     # SvelteKit app template (copied on build)
├── tests/                  # pytest test suite
├── requirements-dev.txt    # Dev dependencies
└── pyproject.toml          # Build config, ruff, mypy, pytest settings

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Maintenance

Dependency updates are tracked by GitHub Dependabot via .github/dependabot.yml:

• Weekly grouped PRs for patch and minor updates across three ecosystems — pip (Python: pyproject.toml, requirements-dev.txt), npm (SvelteKit template at src/learningfoundry/sveltekit_template/), and github-actions (.github/workflows/). Patch+minor updates are bundled per ecosystem into a single PR to keep noise manageable; major updates land as individual PRs for deliberate review.
• Security advisories file PRs immediately, independent of the weekly schedule — the security signal is what we actually want from this wiring.
• @types/node major bumps are explicitly ignored: we pin to the active LTS major; odd-numbered "Current" releases are not appropriate auto-bump targets.

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License

Apache 2.0 — see LICENSE.