cadSet/cad-experience-plugin/skills/cad-experience-builder/SKILL.md

name, description

name

description

cad-experience-builder

Batch-extract STEP files into private JSON, use the active large model to semantically distill transferable CAD methods from a sanitized batch, and deterministically verify and publish a backend-neutral experience library. Use when distilling STEP/STP corpora, reviewing candidate methods, inducing feature motifs and constraints, auditing instance-data leakage, or preparing promoted experience for cad-router and CAD backends.

CAD Experience Builder

Induce methods, not copied answers. Keep source-part evidence outside the published library and require repeated support before promoting any experience.

Required workflow

1. Put source .step or .stp files in parser/input/.

2. Parse every new, content-unique STEP into visible private JSON under parser/output/:

   ../../scripts/cad-experience extract-folder
   

3. Prepare a sanitized semantic batch:

   ../../scripts/cad-experience prepare
   

4. Read work/review/semantic-batch.json completely. Act as the semantic distiller: compare families, parameter roles, datum roles, canonical reconstruction stages, feature cardinality classes, relations, prior vocabulary, failure rules, and validation targets; then author work/review/experience-draft.json using references/llm-review-contract.md.

   Propose methods, invariants, and family-level reconstruction_grammar records, not raw co-occurrence pairs. A reconstruction grammar is a canonical backend-neutral strategy inferred from repeated geometry; it is not the source model's recovered feature history. Do not infer manufacturing intent or failure causes that final B-Rep evidence cannot support.

5. Publish only after the model draft exists:

   ../../scripts/cad-experience publish \
     --draft ../../work/review/experience-draft.json
   

   The publisher recomputes support from the private corpus, rejects numeric instance answers, keeps weak proposals as candidates, and marks the library router-consumable only after this verification.

6. Audit the public library. Treat any failure as a hard publishing failure:

   python scripts/cad_experience.py audit \
     /path/to/cad-experience-library/library.json
   

7. Query only the published library for creation, modification, or rebuilding:

   python scripts/cad_experience.py query \
     /path/to/cad-experience-library/library.json \
     --family flanged_hub_adapter \
     --features base_flange,hollow_sleeve,counterbore
   

8. Pass the query result, never parser/input, parser/output, or a case JSON, to $cad-router or a backend.

Direct distill without an LLM-authored draft is intentionally blocked. The active Codex model supplies semantic judgment; no separate web API key is required.

STEP extraction boundary

• The V2 extractor reads analytic B-Rep surfaces, bounding boxes, topology counts, axes, radii, and locations into a private evidence record.
• It derives scale-independent body classes, part families, rotational and prismatic motifs, passage and pattern relationships, complexity roles, and normalized ratio observations before sanitization.
• It also derives private exact cardinality evidence plus sanitized symbolic parameter roles, datum roles, feature-count classes, and canonical reconstruction stages. These are the evidence used to learn how to rebuild a family without publishing any teacher dimensions.
• Exact source paths, hashes, dimensions, coordinates, and face references are permitted in private case JSON and forbidden in the published library.
• Keep input/output as a human-visible staging area and induction corpus. Never use it directly during creation, modification, or reconstruction.
• A semantic candidate is not a trusted design method until repeated, independently sourced cases satisfy the promotion policy.

Promotion rules

• One case is evidence, not experience.
• Publish generalized, dimension-free observations below threshold as visible candidate_experiences, but never expose them to generation consumers.
• Require min_support independent cases and conditional min_confidence within the proposal's semantic context before promotion.
• Promote feature compositions, semantic constraints, dimensionless distributions, validation checks, and failure-repair methods.
• Reject absolute dimensions, coordinates, face references, source paths, source hashes, and full parameter dictionaries from published experience.
• Keep generated examples out of the trusted corpus until independently validated.
• Evaluate generalization on held-out cases that were not used for induction.

Backend contract

The library is backend-neutral. A consumer receives:

• feature_motif: reusable compositions of feature types;
• constraint: invariant semantic relationships;
• design_rule: conditional modeling guidance;
• dimensionless_distribution: multi-case ratio ranges;
• validation_rule: checks independent of one part's coordinates;
• failure_repair: reusable detection and repair methods.
• reconstruction_grammar: symbolic parameters, datums, canonical feature stages, optional/required feature roles, and validation roles for a family.

build123d, SimpleCADAPI, or another backend translates the same queried experience into its own operations. Experience must never contain executable teacher geometry.

References

• Read references/case-input-contract.md before producing batch input.
• Read references/experience-library-schema.md before extending the output schema or adding a consumer.
• Read references/llm-review-contract.md before authoring or revising a semantic proposal draft.