---

name: datasets-and-mcts description: "Work with DeTikZify dataset helpers, local dataset fallbacks, Paper2Fig and SciCap builders, and the generic Monte Carlo tree-search engine." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Datasets and MCTS

Use this sub-skill when the task is about DeTikZify's dataset helpers, local dataset fallback behavior, Paper2Fig or SciCap builders, or the standalone Node / MonteCarlo tree-search engine.

Route away from this sub-skill when the main task is programmatic inference, training, or evaluation scoring. Those workflows depend on these helpers, but they have their own dedicated sub-skills.

Fast Path

1. Run the safe MCTS helper if you only need to confirm the search engine imports correctly:

   python scripts/mcts_smoke.py
   

2. Check the API snapshot if you need the loader and tree-search signatures:

   python scripts/api_smoke.py
   

3. Read the data reference before using the dataset builders:

   references/data-and-mcts.md
   

What This Sub-Skill Owns

• detikzify.dataset.load_dataset(...) and its local fallback behavior
• the Paper2Fig and SciCap dataset builders
• dataset feature shapes and field expectations for the figure datasets
• the generic Node and MonteCarlo classes from detikzify.mcts
• tree-search behavior such as simulate, expand, random_rollout, make_choice, and make_exploratory_choice

Common Decisions

• Use the local dataset fallback when a package-relative dataset directory is present.
• Use the dataset builder references to understand feature names before trying a conversion or load.
• Use Node / MonteCarlo when you need generic search behavior rather than the inference-specific wrapped tree used by the pipeline.
• Keep the state object compatible with the tree-printing helpers if you want debug output.

Bundled References

• references/data-and-mcts.md: dataset builder fields, local fallback behavior, and tree-search semantics.
• references/workflows.md: how the dataset loaders and generic MCTS engine are typically used.
• references/troubleshooting.md: missing local datasets, empty child lists, and state-shape issues.

Related Helpers

• ../../scripts/mcts_smoke.py: safe dummy-state MCTS sanity check.
• ../../scripts/api_smoke.py: safe import and signature snapshot.

Guardrails

• Do not treat a successful dataset import as proof that the remote dataset or its artifacts are reachable.
• Do not assume the generic Node state shape matches the inference pipeline's wrapped internal state.
• Do not call the generic search engine a success if child_finder never adds children.