Skip to content

Learning Graph Generator Session Log

  • Skill Version: 0.05
  • Date: 2026-06-12
  • Project: Learning MicroPython and Physical Computing
  • Operator: Claude Sonnet 4.6

Python Program Versions Used

  • analyze-graph.py — skill v0.05 asset
  • csv-to-json.py — v0.04 (reported in output: "csv-to-json v0.04")
  • taxonomy-distribution.py — skill v0.05 asset

Steps Completed

Step Description Outcome
0 Setup — copied Python tools to docs/learning-graph/ ✅
1 Course description quality check Skipped — quality_score: 97 (above 85)
2 Generated concept list ✅ 485 concepts across 24 topic areas
3 Generated dependency CSV ✅ learning-graph.csv — 913 edges
4 Quality validation ✅ Valid DAG, 0 cycles, 0 orphans — fixed 8 cycles before passing
5 Concept taxonomy ✅ 24 categories in concept-taxonomy.md
5b taxonomy-names.json ✅ All 24 IDs mapped to human-readable names
6 Added TaxonomyID column to CSV ✅ Pre-populated; consolidated 32 → 24 IDs via sed
7 metadata.json ✅ Created with title, description, creator, date, schema, license
8 color-config.json ✅ 24 taxonomy IDs mapped to CSS named colors from recommended palette
9 Generated learning-graph.json ✅ 485 nodes, 913 edges, 24 groups
10 Taxonomy distribution report ✅ taxonomy-distribution.md
11 Created index.md ✅ Customized for Learning MicroPython
12 Updated mkdocs.yml navigation ✅ Learning Graph section with 6 sub-pages

Key Metrics

  • Total Concepts: 485
  • Total Edges: 913
  • Taxonomy Categories: 24
  • Valid DAG: Yes
  • Cycles fixed: 8 (self-deps on 257, 260; mutual cycles on 383/384, 224/431, 278/279, 291/292, 349/351, 415/416)
  • Max dependency chain length: 15 steps
  • Longest path: Computer Program → Source Code → Module Import → MicroPython → MicroPython Modules → machine.Pin Class → Pin.OUT Mode → Digital Output → PWM → PWM Frequency → Passive Buzzer → Tone Generation → Musical Note Frequencies → Play a Melody → Mario Theme Program

Files Created

  • concept-list.md — 485 numbered concept labels
  • learning-graph.csv — full dependency graph with TaxonomyID column
  • taxonomy-names.json — 24 taxonomy ID → human-readable name mappings
  • metadata.json — Dublin Core metadata for the learning graph
  • color-config.json — 24 taxonomy IDs mapped to CSS named colors
  • learning-graph.json — complete vis-network JSON (485 nodes, 913 edges, 24 groups)
  • concept-taxonomy.md — taxonomy category definitions
  • quality-metrics.md — graph quality validation report
  • taxonomy-distribution.md — category distribution analysis
  • index.md — learning graph introduction page

Notes

  • User requested up to 500 concepts with individual entries for each sensor and display driver — 485 concepts were generated, including individual entries for DHT11, DHT22, BME280, DS18B20, HC-SR04, VL53L0X, APDS9960, HMC5883L, QMC5883L, SSD1306, SH1106, SSD1352, ILI9341, ST7789V, TM1637, MAX7219, and more.
  • Two concept labels with internal commas were renamed to avoid CSV parsing errors: "I2C Bus (SDA, SCL)" → "I2C Bus SDA and SCL" and "SPI Bus (MOSI, MISO, SCK, CS)" → "SPI Bus Pins (MOSI MISO SCK CS)".
  • Terminal node percentage (46.4%) slightly above the 40% healthy target; expected for a course with many specialized endpoint topics (individual display drivers, sensor-specific methods, kit projects).