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Explore archetype classification pipeline in a Magic deck-tracking repo

Session from merchantscroll-traces on Hugging Face by vedalken · MIT · condensed by Coders Talk

15ssession
0interventions
GOAL

Understand and document how the merchantscroll project's archetype classification system works, including its Python implementation, JSON data schemas, entrypoint orchestration, and CSV/PNG generation, without modifying any code.

  1. Prompt · +0
    Thoroughly explore the archetype classification system: implementation files in src/ and archetypes/, JSON schemas in assets/pauper/, how entrypoint.py orchestrates the pipeline, and what pauper-league-archetypes.csv/.png are. Do not modify anything.
  2. Agent did · +0m
    Globbed src/ and archetypes/, grepped for 'archetype' usage across the repo, read entrypoint.py, docs/features/deck-classification.md, scripts/analysis/poc_archetypes.py and scripts/build_archetypes.py to map the pipeline's structure.
  3. Agent did · +0m
    Read src/classifier.py and src/pipeline.py in full, sampled JSON files under assets/pauper/decks/, raw/, and players/ with a python script to produce representative excerpts, and cross-checked with tests/assets/sample_deck.json and archetypes/aliases.json and pauperwave.json.
  4. Agent did · +0m
    Traced how pauper-league-archetypes.csv/.png are generated via poc_archetypes.py, checked src/deck_stats.py, player_stats.py, constants/paths.py, index.html, and refresh_policy.py for how archetype data is consumed and refreshed.
  5. Agent did · +0m
    Reviewed src/utils.py, src/crawler.py, domain.py, the GitHub Actions workflow run-crawler.yml, and crawl.py to confirm how the crawler and CI pipeline feed into the classifier and asset generation.
  6. Outcome · +0m
    Delivered a full written report: src/classifier.py identified as the authoritative implementation (signature-based matching with aliasing via archetypes/aliases.json), plus documented JSON schemas, entrypoint.py orchestration, and the CSV/PNG generation path — no files modified.
VERDICT

Next time I'd front-load the specific files I already suspect matter (classifier.py, pipeline.py, entrypoint.py) to cut down on the exploratory grepping. There were no corrections needed here — it was a pure read-only investigation and the agent respected that constraint throughout, methodically working from directory structure down to individual JSON schemas.

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