Added course ingestion pipeline.

README.md

@@ -8,6 +8,40 @@
 
 ## Recent revisions
 
+### Course Ingestion Pipeline
+
+This revision adds a **Course-to-Pack Ingestion Pipeline** plus a **stable rule-policy adapter layer**.
+
+The design goal is to turn open or user-supplied course materials into draft
+Didactopus domain packs without introducing a brittle external rule-engine dependency.
+
+#### Why no third-party rule engine here?
+
+To minimize dependency risk, this scaffold uses a small declarative rule-policy
+adapter implemented in pure Python and standard-library data structures.
+
+That gives Didactopus:
+- portable rules
+- inspectable rule definitions
+- deterministic behavior
+- zero extra runtime dependency for policy evaluation
+
+If a stronger rule engine is needed later, this adapter can remain the stable API surface.
+
+#### What is included
+
+- normalized course schema
+- Markdown/HTML-ish text ingestion adapter
+- module / lesson / objective extraction
+- concept candidate extraction
+- prerequisite guess generation
+- rule-policy adapter
+- draft pack emitter
+- review report generation
+- sample course input
+- sample generated pack outputs
+
+
 ### Mastery Ledger
 
 This revision adds a **Mastery Ledger + Capability Export** layer.
@@ -147,3 +181,4 @@ didactopus/
 └── tests/
 ```
 
+

configs/config.example.yaml

@@ -1,18 +1,11 @@
-model_provider:
-  mode: local_first
-  local:
-    backend: ollama
-    endpoint: http://localhost:11434
-    model_name: llama3.1:8b
+course_ingest:
+  default_pack_author: "Wesley R. Elsberry"
+  default_license: "REVIEW-REQUIRED"
+  min_term_length: 4
+  max_terms_per_lesson: 8
 
-platform:
-  default_dimension_thresholds:
-    correctness: 0.8
-    explanation: 0.75
-    transfer: 0.7
-    project_execution: 0.75
-    critique: 0.7
-
-artifacts:
-  local_pack_dirs:
-    - domain-packs
+rule_policy:
+  enable_prerequisite_order_rule: true
+  enable_duplicate_term_merge_rule: true
+  enable_project_detection_rule: true
+  enable_review_flags: true

docs/course-to-pack.md

@@ -0,0 +1,35 @@
+# Course-to-Pack Ingestion Pipeline
+
+The course-to-pack pipeline transforms educational material into Didactopus-native artifacts.
+
+## Inputs
+
+Typical sources:
+- syllabus text
+- lesson outlines
+- markdown notes
+- HTML course pages
+- assignment sheets
+- quiz prompts
+- lecture transcripts
+
+## Normalized intermediate structure
+
+The pipeline builds a `NormalizedCourse` object containing:
+- title
+- source metadata
+- modules
+- lessons
+- learning objectives
+- exercises
+- key terms
+- project prompts
+
+## Rule-policy adapter
+
+The pipeline includes a small rule layer for stable policy transforms such as:
+- suggest prerequisites from ordering
+- merge repeated key-term candidates
+- flag modules with no exercises
+- flag concepts with weak evidence of distinctness
+- suggest project concepts from capstone markers

docs/faq.md

@@ -1,37 +1,32 @@
 # FAQ
 
-## How is an AI student's learned mastery represented?
+## Why add course ingestion?
 
-As structured operational state, including:
-- mastered concepts
-- evaluator summaries
-- weak dimensions
-- evidence records
-- artifacts
-- capability export
+Because many open or user-supplied courses already encode:
+- topic sequencing
+- learning objectives
+- exercises
+- project prompts
+- terminology
 
-## Does Didactopus change the AI model weights?
+That makes them strong starting material for draft domain packs.
 
-No. In the current architecture, Didactopus supervises and evaluates a learner
-agent, but it does not retrain the foundation model.
+## Why not just embed all course text?
 
-## How is an AI student ready to be put to work?
+Because Didactopus needs structured artifacts:
+- concepts
+- prerequisites
+- projects
+- rubrics
+- mastery cues
 
-Readiness is represented operationally. A downstream system can inspect:
-- which concepts are mastered
-- which weak dimensions remain
-- what artifacts were produced
-- what evaluator evidence supports deployment
+A flat embedding store is not enough for mastery planning.
 
-## Is the capability export a certification?
+## Why avoid PyKE or another heavy rule engine here?
 
-Not by itself. It is a structured mastery report. In future, it could be combined
-with formal evaluators, signed evidence records, and policy rules.
+Dependency stability matters. The current rule-policy adapter keeps rules simple,
+transparent, and dependency-light.
 
-## Why is this useful?
+## Can the rule layer be replaced later?
 
-Because it allows Didactopus outputs to feed into:
-- task routing
-- portfolio review
-- benchmark comparison
-- agent deployment policies
+Yes. The adapter is designed so a future engine can be plugged in behind the same interface.

examples/generated_pack/concepts.yaml

@@ -0,0 +1,35 @@
+concepts:
+- id: descriptive-statistics
+  title: Descriptive Statistics
+  description: Descriptive Statistics introduces measures of center and spread.
+  prerequisites: []
+  mastery_signals:
+  - Explain mean, median, and variance.
+  mastery_profile: {}
+- id: probability-basics
+  title: Probability Basics
+  description: Probability Basics introduces events, likelihood, and Bayes-style reasoning.
+  prerequisites:
+  - descriptive-statistics
+  mastery_signals:
+  - Explain conditional probability.
+  mastery_profile: {}
+- id: prior-and-posterior
+  title: Prior and Posterior
+  description: A Prior expresses assumptions before evidence. Posterior reasoning
+    updates belief after evidence.
+  prerequisites:
+  - probability-basics
+  mastery_signals:
+  - Explain a prior distribution.
+  - Explain how evidence changes belief.
+  mastery_profile: {}
+- id: capstone-mini-project
+  title: Capstone Mini Project
+  description: This project asks learners to critique assumptions and produce a small
+    capstone artifact.
+  prerequisites:
+  - prior-and-posterior
+  mastery_signals:
+  - Write a short project report comparing priors and posteriors.
+  mastery_profile: {}

examples/generated_pack/license_attribution.json

@@ -0,0 +1,5 @@
+{
+  "source_name": "Sample Course",
+  "source_url": "",
+  "rights_note": "REVIEW REQUIRED"
+}

examples/generated_pack/pack.yaml

@@ -0,0 +1,13 @@
+name: introductory-bayesian-inference
+display_name: Introductory Bayesian Inference
+version: 0.1.0-draft
+schema_version: '1'
+didactopus_min_version: 0.1.0
+didactopus_max_version: 0.9.99
+description: Draft pack generated from sample course.
+author: Wesley R. Elsberry
+license: REVIEW-REQUIRED
+dependencies: []
+overrides: []
+profile_templates: {}
+cross_pack_links: []

examples/generated_pack/projects.yaml

@@ -0,0 +1,7 @@
+projects:
+- id: capstone-mini-project
+  title: Capstone Mini Project
+  difficulty: review-required
+  prerequisites: []
+  deliverables:
+  - project artifact

examples/generated_pack/review_report.md

@@ -0,0 +1,3 @@
+# Review Report
+
+- Module 'Module 2: Bayesian Updating' appears to contain project-like material; review project extraction.

examples/generated_pack/roadmap.yaml

@@ -0,0 +1,17 @@
+stages:
+- id: stage-1
+  title: 'Module 1: Foundations'
+  concepts:
+  - descriptive-statistics
+  - probability-basics
+  checkpoint:
+  - Summarize a small dataset.
+  - Compute a simple conditional probability.
+- id: stage-2
+  title: 'Module 2: Bayesian Updating'
+  concepts:
+  - prior-and-posterior
+  - capstone-mini-project
+  checkpoint:
+  - Compare prior and posterior beliefs.
+  - Write a short project report comparing priors and posteriors.

examples/generated_pack/rubrics.yaml

@@ -0,0 +1,6 @@
+rubrics:
+- id: draft-rubric
+  title: Draft Rubric
+  criteria:
+  - correctness
+  - explanation

examples/sample_course.md

@@ -0,0 +1,23 @@
+# Introductory Bayesian Inference
+
+## Module 1: Foundations
+### Descriptive Statistics
+- Objective: Explain mean, median, and variance.
+- Exercise: Summarize a small dataset.
+Descriptive Statistics introduces measures of center and spread.
+
+### Probability Basics
+- Objective: Explain conditional probability.
+- Exercise: Compute a simple conditional probability.
+Probability Basics introduces events, likelihood, and Bayes-style reasoning.
+
+## Module 2: Bayesian Updating
+### Prior and Posterior
+- Objective: Explain a prior distribution.
+- Objective: Explain how evidence changes belief.
+- Exercise: Compare prior and posterior beliefs.
+A Prior expresses assumptions before evidence. Posterior reasoning updates belief after evidence.
+
+### Capstone Mini Project
+- Exercise: Write a short project report comparing priors and posteriors.
+This project asks learners to critique assumptions and produce a small capstone artifact.

pyproject.toml

@@ -5,21 +5,18 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "didactopus"
 version = "0.1.0"
-description = "Didactopus: local-first AI-assisted autodidactic mastery platform"
+description = "Didactopus: course-to-pack ingestion scaffold"
 readme = "README.md"
 requires-python = ">=3.10"
 license = {text = "MIT"}
 authors = [{name = "Wesley R. Elsberry"}]
-dependencies = [
-  "pydantic>=2.7",
-  "pyyaml>=6.0",
-  "networkx>=3.2",
-]
+dependencies = ["pydantic>=2.7", "pyyaml>=6.0"]
+
 [project.optional-dependencies]
 dev = ["pytest>=8.0", "ruff>=0.6"]
 
 [project.scripts]
-didactopus = "didactopus.main:main"
+didactopus-course-ingest = "didactopus.main:main"
 
 [tool.setuptools.packages.find]
 where = ["src"]

src/didactopus/config.py

@@ -3,45 +3,23 @@ from pydantic import BaseModel, Field
 import yaml
 
 
-class PlatformConfig(BaseModel):
-    default_dimension_thresholds: dict[str, float] = Field(
-        default_factory=lambda: {
-            "correctness": 0.8,
-            "explanation": 0.75,
-            "transfer": 0.7,
-            "project_execution": 0.75,
-            "critique": 0.7,
-        }
-    )
+class CourseIngestConfig(BaseModel):
+    default_pack_author: str = "Unknown"
+    default_license: str = "REVIEW-REQUIRED"
+    min_term_length: int = 4
+    max_terms_per_lesson: int = 8
 
 
-class PlannerConfig(BaseModel):
-    readiness_bonus: float = 2.0
-    target_distance_weight: float = 1.0
-    weak_dimension_bonus: float = 1.2
-    fragile_review_bonus: float = 1.5
-    project_unlock_bonus: float = 0.8
-    semantic_similarity_weight: float = 1.0
-
-
-class EvidenceConfig(BaseModel):
-    resurfacing_threshold: float = 0.55
-    confidence_threshold: float = 0.8
-    evidence_weights: dict[str, float] = Field(
-        default_factory=lambda: {
-            "explanation": 1.0,
-            "problem": 1.5,
-            "project": 2.5,
-            "transfer": 2.0,
-        }
-    )
-    recent_evidence_multiplier: float = 1.35
+class RulePolicyConfig(BaseModel):
+    enable_prerequisite_order_rule: bool = True
+    enable_duplicate_term_merge_rule: bool = True
+    enable_project_detection_rule: bool = True
+    enable_review_flags: bool = True
 
 
 class AppConfig(BaseModel):
-    platform: PlatformConfig = Field(default_factory=PlatformConfig)
-    planner: PlannerConfig = Field(default_factory=PlannerConfig)
-    evidence: EvidenceConfig = Field(default_factory=EvidenceConfig)
+    course_ingest: CourseIngestConfig = Field(default_factory=CourseIngestConfig)
+    rule_policy: RulePolicyConfig = Field(default_factory=RulePolicyConfig)
 
 
 def load_config(path: str | Path) -> AppConfig:

src/didactopus/course_ingest.py

@@ -0,0 +1,128 @@
+from __future__ import annotations
+
+import re
+from .course_schema import NormalizedCourse, Module, Lesson, ConceptCandidate
+
+HEADING_RE = re.compile(r"^(#{1,3})\s+(.*)$")
+BULLET_RE = re.compile(r"^\s*[-*+]\s+(.*)$")
+
+
+def slugify(text: str) -> str:
+    cleaned = re.sub(r"[^a-zA-Z0-9]+", "-", text.strip().lower()).strip("-")
+    return cleaned or "untitled"
+
+
+def extract_key_terms(text: str, min_term_length: int = 4, max_terms: int = 8) -> list[str]:
+    candidates = re.findall(r"\b[A-Z][A-Za-z0-9\-]{%d,}\b" % (min_term_length - 1), text)
+    seen = set()
+    ordered = []
+    for term in candidates:
+        if term not in seen:
+            seen.add(term)
+            ordered.append(term)
+        if len(ordered) >= max_terms:
+            break
+    return ordered
+
+
+def parse_markdown_course(text: str, title: str, source_name: str = "", source_url: str = "", rights_note: str = "") -> NormalizedCourse:
+    lines = text.splitlines()
+    modules: list[Module] = []
+    current_module: Module | None = None
+    current_lesson: Lesson | None = None
+    body_buffer: list[str] = []
+
+    def flush_body():
+        nonlocal body_buffer, current_lesson
+        if current_lesson is not None and body_buffer:
+            current_lesson.body = "\n".join(body_buffer).strip()
+            body_buffer = []
+
+    for line in lines:
+        m = HEADING_RE.match(line)
+        if m:
+            level = len(m.group(1))
+            heading = m.group(2).strip()
+            if level == 1:
+                continue
+            elif level == 2:
+                flush_body()
+                if current_lesson is not None and current_module is not None:
+                    current_module.lessons.append(current_lesson)
+                    current_lesson = None
+                if current_module is not None:
+                    modules.append(current_module)
+                current_module = Module(title=heading, lessons=[])
+            elif level == 3:
+                flush_body()
+                if current_lesson is not None and current_module is not None:
+                    current_module.lessons.append(current_lesson)
+                current_lesson = Lesson(title=heading)
+            continue
+
+        bullet = BULLET_RE.match(line)
+        if bullet and current_lesson is not None:
+            item = bullet.group(1).strip()
+            lower = item.lower()
+            if lower.startswith("objective:"):
+                current_lesson.objectives.append(item.split(":", 1)[1].strip())
+            elif lower.startswith("exercise:"):
+                current_lesson.exercises.append(item.split(":", 1)[1].strip())
+            else:
+                body_buffer.append(line)
+        else:
+            body_buffer.append(line)
+
+    flush_body()
+    if current_lesson is not None and current_module is not None:
+        current_module.lessons.append(current_lesson)
+    if current_module is not None:
+        modules.append(current_module)
+
+    course = NormalizedCourse(
+        title=title,
+        source_name=source_name,
+        source_url=source_url,
+        rights_note=rights_note,
+        modules=modules,
+    )
+    for module in course.modules:
+        for lesson in module.lessons:
+            lesson.key_terms = extract_key_terms(f"{lesson.title}\n{lesson.body}")
+    return course
+
+
+def extract_concept_candidates(course: NormalizedCourse) -> list[ConceptCandidate]:
+    concepts: list[ConceptCandidate] = []
+    seen_ids: set[str] = set()
+    for module in course.modules:
+        for lesson in module.lessons:
+            title_id = slugify(lesson.title)
+            if title_id not in seen_ids:
+                seen_ids.add(title_id)
+                concepts.append(
+                    ConceptCandidate(
+                        id=title_id,
+                        title=lesson.title,
+                        description=lesson.body[:240].strip(),
+                        source_modules=[module.title],
+                        source_lessons=[lesson.title],
+                        mastery_signals=list(lesson.objectives[:3] or lesson.exercises[:2]),
+                    )
+                )
+            for term in lesson.key_terms:
+                term_id = slugify(term)
+                if term_id in seen_ids:
+                    continue
+                seen_ids.add(term_id)
+                concepts.append(
+                    ConceptCandidate(
+                        id=term_id,
+                        title=term,
+                        description=f"Candidate concept extracted from lesson '{lesson.title}'.",
+                        source_modules=[module.title],
+                        source_lessons=[lesson.title],
+                        mastery_signals=list(lesson.objectives[:2]),
+                    )
+                )
+    return concepts

src/didactopus/course_schema.py

@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+
+class Lesson(BaseModel):
+    title: str
+    body: str = ""
+    objectives: list[str] = Field(default_factory=list)
+    exercises: list[str] = Field(default_factory=list)
+    key_terms: list[str] = Field(default_factory=list)
+
+
+class Module(BaseModel):
+    title: str
+    lessons: list[Lesson] = Field(default_factory=list)
+
+
+class NormalizedCourse(BaseModel):
+    title: str
+    source_name: str = ""
+    source_url: str = ""
+    rights_note: str = ""
+    modules: list[Module] = Field(default_factory=list)
+
+
+class ConceptCandidate(BaseModel):
+    id: str
+    title: str
+    description: str = ""
+    source_modules: list[str] = Field(default_factory=list)
+    source_lessons: list[str] = Field(default_factory=list)
+    prerequisites: list[str] = Field(default_factory=list)
+    mastery_signals: list[str] = Field(default_factory=list)
+
+
+class DraftPack(BaseModel):
+    pack: dict
+    concepts: dict
+    roadmap: dict
+    projects: dict
+    rubrics: dict
+    review_report: list[str] = Field(default_factory=list)
+    attribution: dict = Field(default_factory=dict)

src/didactopus/main.py

@@ -1,49 +1,65 @@
+from __future__ import annotations
+
 import argparse
 from pathlib import Path
 
-from .agentic_loop import run_demo_agentic_loop
-from .mastery_ledger import (
-    build_capability_profile,
-    export_capability_profile_json,
-    export_capability_report_markdown,
-    export_artifact_manifest,
-)
+from .config import load_config
+from .course_ingest import parse_markdown_course, extract_concept_candidates
+from .rule_policy import RuleContext, build_default_rules, run_rules
+from .pack_emitter import build_draft_pack, write_draft_pack
 
 
 def build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(description="Didactopus mastery ledger demo")
-    parser.add_argument("--domain", default="Bayesian inference")
-    parser.add_argument("--outdir", default="exports")
+    parser = argparse.ArgumentParser(description="Didactopus course-to-pack ingestion pipeline")
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--title", required=True)
+    parser.add_argument("--source-name", default="")
+    parser.add_argument("--source-url", default="")
+    parser.add_argument("--rights-note", default="REVIEW REQUIRED")
+    parser.add_argument("--output-dir", default="generated-pack")
+    parser.add_argument("--config", default="configs/config.example.yaml")
     return parser
 
 
 def main() -> None:
     args = build_parser().parse_args()
-    outdir = Path(args.outdir)
-    outdir.mkdir(parents=True, exist_ok=True)
+    config = load_config(args.config)
+    text = Path(args.input).read_text(encoding="utf-8")
 
-    concepts = [
-        "foundations-statistics::descriptive-statistics",
-        "foundations-statistics::probability-basics",
-        "bayes-extension::prior",
-        "bayes-extension::posterior",
-        "applied-inference::model-checking",
-    ]
-    state = run_demo_agentic_loop(concepts)
-    profile = build_capability_profile(state, args.domain)
+    course = parse_markdown_course(
+        text=text,
+        title=args.title,
+        source_name=args.source_name,
+        source_url=args.source_url,
+        rights_note=args.rights_note,
+    )
+    concepts = extract_concept_candidates(course)
+    context = RuleContext(course=course, concepts=concepts)
 
-    json_path = outdir / "capability_profile.json"
-    md_path = outdir / "capability_report.md"
-    manifest_path = outdir / "artifact_manifest.json"
+    rules = build_default_rules(
+        enable_prereq=config.rule_policy.enable_prerequisite_order_rule,
+        enable_merge=config.rule_policy.enable_duplicate_term_merge_rule,
+        enable_projects=config.rule_policy.enable_project_detection_rule,
+        enable_review=config.rule_policy.enable_review_flags,
+    )
+    run_rules(context, rules)
 
-    export_capability_profile_json(profile, str(json_path))
-    export_capability_report_markdown(profile, str(md_path))
-    export_artifact_manifest(profile, str(manifest_path))
+    draft = build_draft_pack(
+        course=course,
+        concepts=context.concepts,
+        author=config.course_ingest.default_pack_author,
+        license_name=config.course_ingest.default_license,
+        review_flags=context.review_flags,
+    )
+    write_draft_pack(draft, args.output_dir)
 
-    print("== Didactopus Mastery Ledger Demo ==")
-    print(f"Domain: {args.domain}")
-    print(f"Mastered concepts: {len(profile.mastered_concepts)}")
-    print(f"Artifacts: {len(profile.artifacts)}")
-    print(f"Capability profile JSON: {json_path}")
-    print(f"Capability report Markdown: {md_path}")
-    print(f"Artifact manifest JSON: {manifest_path}")
+    print("== Didactopus Course-to-Pack Ingest ==")
+    print(f"Course: {course.title}")
+    print(f"Modules: {len(course.modules)}")
+    print(f"Concept candidates: {len(context.concepts)}")
+    print(f"Review flags: {len(context.review_flags)}")
+    print(f"Output dir: {args.output_dir}")
+
+
+if __name__ == "__main__":
+    main()

src/didactopus/pack_emitter.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from pathlib import Path
+import json
+import yaml
+from .course_schema import NormalizedCourse, ConceptCandidate, DraftPack
+
+
+def build_draft_pack(course: NormalizedCourse, concepts: list[ConceptCandidate], author: str, license_name: str, review_flags: list[str]) -> DraftPack:
+    pack_name = course.title.lower().replace(" ", "-")
+    pack = {
+        "name": pack_name,
+        "display_name": course.title,
+        "version": "0.1.0-draft",
+        "schema_version": "1",
+        "didactopus_min_version": "0.1.0",
+        "didactopus_max_version": "0.9.99",
+        "description": f"Draft pack generated from course source '{course.source_name or course.title}'.",
+        "author": author,
+        "license": license_name,
+        "dependencies": [],
+        "overrides": [],
+        "profile_templates": {},
+        "cross_pack_links": [],
+    }
+    concepts_yaml = {
+        "concepts": [
+            {
+                "id": c.id,
+                "title": c.title,
+                "description": c.description,
+                "prerequisites": c.prerequisites,
+                "mastery_signals": c.mastery_signals,
+                "mastery_profile": {},
+            }
+            for c in concepts
+        ]
+    }
+    roadmap = {
+        "stages": [
+            {
+                "id": f"stage-{i+1}",
+                "title": module.title,
+                "concepts": [c.id for c in concepts if module.title in c.source_modules and c.title in c.source_lessons],
+                "checkpoint": [ex for lesson in module.lessons for ex in lesson.exercises[:2]],
+            }
+            for i, module in enumerate(course.modules)
+        ]
+    }
+    project_items = []
+    for module in course.modules:
+        for lesson in module.lessons:
+            text = f"{lesson.title}\n{lesson.body}".lower()
+            if "project" in text or "capstone" in text:
+                project_items.append({
+                    "id": lesson.title.lower().replace(" ", "-"),
+                    "title": lesson.title,
+                    "difficulty": "review-required",
+                    "prerequisites": [],
+                    "deliverables": ["project artifact"],
+                })
+    projects = {"projects": project_items}
+    rubrics = {"rubrics": [{"id": "draft-rubric", "title": "Draft Rubric", "criteria": ["correctness", "explanation"]}]}
+    attribution = {"source_name": course.source_name, "source_url": course.source_url, "rights_note": course.rights_note}
+    return DraftPack(pack=pack, concepts=concepts_yaml, roadmap=roadmap, projects=projects, rubrics=rubrics, review_report=review_flags, attribution=attribution)
+
+
+def write_draft_pack(pack: DraftPack, outdir: str | Path) -> None:
+    out = Path(outdir)
+    out.mkdir(parents=True, exist_ok=True)
+    (out / "pack.yaml").write_text(yaml.safe_dump(pack.pack, sort_keys=False), encoding="utf-8")
+    (out / "concepts.yaml").write_text(yaml.safe_dump(pack.concepts, sort_keys=False), encoding="utf-8")
+    (out / "roadmap.yaml").write_text(yaml.safe_dump(pack.roadmap, sort_keys=False), encoding="utf-8")
+    (out / "projects.yaml").write_text(yaml.safe_dump(pack.projects, sort_keys=False), encoding="utf-8")
+    (out / "rubrics.yaml").write_text(yaml.safe_dump(pack.rubrics, sort_keys=False), encoding="utf-8")
+    review_lines = ["# Review Report", ""] + [f"- {flag}" for flag in pack.review_report] if pack.review_report else ["# Review Report", "", "- none"]
+    (out / "review_report.md").write_text("\n".join(review_lines), encoding="utf-8")
+    (out / "license_attribution.json").write_text(json.dumps(pack.attribution, indent=2), encoding="utf-8")

src/didactopus/rule_policy.py

@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Callable
+from .course_schema import NormalizedCourse, ConceptCandidate
+
+
+@dataclass
+class RuleContext:
+    course: NormalizedCourse
+    concepts: list[ConceptCandidate]
+    review_flags: list[str] = field(default_factory=list)
+
+
+@dataclass
+class Rule:
+    name: str
+    predicate: Callable[[RuleContext], bool]
+    action: Callable[[RuleContext], None]
+
+
+def order_based_prerequisite_rule(context: RuleContext) -> None:
+    concept_titles = {c.title: c for c in context.concepts}
+    previous = None
+    for module in context.course.modules:
+        for lesson in module.lessons:
+            current = concept_titles.get(lesson.title)
+            if current is not None and previous is not None and previous.id not in current.prerequisites:
+                current.prerequisites.append(previous.id)
+            if current is not None:
+                previous = current
+
+
+def duplicate_term_merge_rule(context: RuleContext) -> None:
+    seen = {}
+    deduped = []
+    for concept in context.concepts:
+        key = concept.title.strip().lower()
+        if key in seen:
+            seen[key].source_modules.extend(x for x in concept.source_modules if x not in seen[key].source_modules)
+            seen[key].source_lessons.extend(x for x in concept.source_lessons if x not in seen[key].source_lessons)
+            if concept.description and len(seen[key].description) < len(concept.description):
+                seen[key].description = concept.description
+        else:
+            seen[key] = concept
+            deduped.append(concept)
+    context.concepts[:] = deduped
+
+
+def project_detection_rule(context: RuleContext) -> None:
+    for module in context.course.modules:
+        joined = " ".join(lesson.body for lesson in module.lessons).lower()
+        if "project" in joined or "capstone" in joined:
+            context.review_flags.append(f"Module '{module.title}' appears to contain project-like material; review project extraction.")
+
+
+def review_flag_rule(context: RuleContext) -> None:
+    for module in context.course.modules:
+        if not any(lesson.exercises for lesson in module.lessons):
+            context.review_flags.append(f"Module '{module.title}' has no explicit exercises; mastery signals may be weak.")
+    for concept in context.concepts:
+        if not concept.mastery_signals:
+            context.review_flags.append(f"Concept '{concept.title}' has no extracted mastery signals; review manually.")
+
+
+def build_default_rules(enable_prereq=True, enable_merge=True, enable_projects=True, enable_review=True) -> list[Rule]:
+    rules = []
+    if enable_prereq:
+        rules.append(Rule("order_based_prerequisite_rule", lambda ctx: True, order_based_prerequisite_rule))
+    if enable_merge:
+        rules.append(Rule("duplicate_term_merge_rule", lambda ctx: True, duplicate_term_merge_rule))
+    if enable_projects:
+        rules.append(Rule("project_detection_rule", lambda ctx: True, project_detection_rule))
+    if enable_review:
+        rules.append(Rule("review_flag_rule", lambda ctx: True, review_flag_rule))
+    return rules
+
+
+def run_rules(context: RuleContext, rules: list[Rule]) -> RuleContext:
+    for rule in rules:
+        if rule.predicate(context):
+            rule.action(context)
+    return context

tests/test_course_ingest.py

@@ -0,0 +1,26 @@
+from didactopus.course_ingest import parse_markdown_course, extract_concept_candidates
+
+SAMPLE = '''
+# Sample Course
+
+## Module 1
+### Lesson A
+- Objective: Explain Topic A.
+- Exercise: Do task A.
+Topic A body.
+
+### Lesson B
+- Objective: Explain Topic B.
+Topic B body.
+'''
+
+def test_parse_markdown_course() -> None:
+    course = parse_markdown_course(SAMPLE, "Sample Course")
+    assert course.title == "Sample Course"
+    assert len(course.modules) == 1
+    assert len(course.modules[0].lessons) == 2
+
+def test_extract_concepts() -> None:
+    course = parse_markdown_course(SAMPLE, "Sample Course")
+    concepts = extract_concept_candidates(course)
+    assert len(concepts) >= 2

tests/test_pack_emitter.py

@@ -0,0 +1,24 @@
+from pathlib import Path
+from didactopus.course_ingest import parse_markdown_course, extract_concept_candidates
+from didactopus.rule_policy import RuleContext, build_default_rules, run_rules
+from didactopus.pack_emitter import build_draft_pack, write_draft_pack
+
+SAMPLE = '''
+# Sample Course
+
+## Module 1
+### Lesson A
+- Objective: Explain Topic A.
+- Exercise: Do task A.
+Topic A body.
+'''
+
+def test_emit_pack(tmp_path: Path) -> None:
+    course = parse_markdown_course(SAMPLE, "Sample Course")
+    concepts = extract_concept_candidates(course)
+    ctx = RuleContext(course=course, concepts=concepts)
+    run_rules(ctx, build_default_rules())
+    draft = build_draft_pack(course, ctx.concepts, "Tester", "REVIEW", ctx.review_flags)
+    write_draft_pack(draft, tmp_path)
+    assert (tmp_path / "pack.yaml").exists()
+    assert (tmp_path / "review_report.md").exists()

tests/test_rule_policy.py

@@ -0,0 +1,24 @@
+from didactopus.course_ingest import parse_markdown_course, extract_concept_candidates
+from didactopus.rule_policy import RuleContext, build_default_rules, run_rules
+
+SAMPLE = '''
+# Sample Course
+
+## Module 1
+### Lesson A
+- Objective: Explain Topic A.
+- Exercise: Do task A.
+Topic A body.
+
+### Lesson B
+- Objective: Explain Topic B.
+- Exercise: Do task B.
+Topic B body.
+'''
+
+def test_rules_run() -> None:
+    course = parse_markdown_course(SAMPLE, "Sample Course")
+    concepts = extract_concept_candidates(course)
+    ctx = RuleContext(course=course, concepts=concepts)
+    run_rules(ctx, build_default_rules())
+    assert len(ctx.concepts) >= 2