Jerry
120 lines
4.8 KiB
Markdown
120 lines
4.8 KiB
Markdown
---
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name: cad-experience-builder
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description: 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.
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---
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# CAD Experience Builder
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Induce methods, not copied answers. Keep source-part evidence outside the
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published library and require repeated support before promoting any experience.
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## Required workflow
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1. Put source `.step` or `.stp` files in `parser/input/`.
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2. Parse every new, content-unique STEP into visible private JSON under
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`parser/output/`:
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```bash
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../../scripts/cad-experience extract-folder
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```
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3. Prepare a sanitized semantic batch:
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```bash
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../../scripts/cad-experience prepare
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```
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4. Read `work/review/semantic-batch.json` completely. Act as the semantic
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distiller: compare families, features, relations, prior vocabulary, failure
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rules, and validation targets; then author
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`work/review/experience-draft.json` using
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`references/llm-review-contract.md`.
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Propose methods and invariants, not raw co-occurrence pairs. Do not infer
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operation order, manufacturing intent, or failure causes that final B-Rep
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evidence cannot support.
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5. Publish only after the model draft exists:
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```bash
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../../scripts/cad-experience publish \
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--draft ../../work/review/experience-draft.json
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```
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The publisher recomputes support from the private corpus, rejects numeric
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instance answers, keeps weak proposals as candidates, and marks the library
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router-consumable only after this verification.
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6. Audit the public library. Treat any failure as a hard publishing failure:
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```bash
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python scripts/cad_experience.py audit \
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/path/to/cad-experience-library/library.json
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```
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7. Query only the published library for creation, modification, or rebuilding:
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```bash
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python scripts/cad_experience.py query \
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/path/to/cad-experience-library/library.json \
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--family flanged_hub_adapter \
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--features base_flange,hollow_sleeve,counterbore
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```
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8. Pass the query result, never `parser/input`, `parser/output`, or a case JSON,
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to `$cad-router` or a backend.
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Direct `distill` without an LLM-authored draft is intentionally blocked. The
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active Codex model supplies semantic judgment; no separate web API key is
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required.
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## STEP extraction boundary
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- The extractor reads analytic B-Rep surfaces, bounding boxes, topology counts,
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axes, radii, and locations into a private evidence record.
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- It derives only semantic candidates such as rotational stacks, axis
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relationships, transitions, and repeated-hole motifs.
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- Exact source paths, hashes, dimensions, coordinates, and face references are
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permitted in private case JSON and forbidden in the published library.
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- Keep input/output as a human-visible staging area and induction corpus. Never
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use it directly during creation, modification, or reconstruction.
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- A semantic candidate is not a trusted design method until repeated,
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independently sourced cases satisfy the promotion policy.
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## Promotion rules
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- One case is evidence, not experience.
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- Publish generalized, dimension-free observations below threshold as visible
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`candidate_experiences`, but never expose them to generation consumers.
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- Require `min_support` independent cases and `min_confidence` before promotion.
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- Promote feature compositions, semantic constraints, dimensionless
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distributions, validation checks, and failure-repair methods.
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- Reject absolute dimensions, coordinates, face references, source paths,
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source hashes, and full parameter dictionaries from published experience.
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- Keep generated examples out of the trusted corpus until independently
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validated.
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- Evaluate generalization on held-out cases that were not used for induction.
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## Backend contract
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The library is backend-neutral. A consumer receives:
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- `feature_motif`: reusable compositions of feature types;
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- `constraint`: invariant semantic relationships;
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- `design_rule`: conditional modeling guidance;
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- `dimensionless_distribution`: multi-case ratio ranges;
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- `validation_rule`: checks independent of one part's coordinates;
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- `failure_repair`: reusable detection and repair methods.
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build123d, SimpleCADAPI, or another backend translates the same queried
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experience into its own operations. Experience must never contain executable
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teacher geometry.
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## References
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- Read `references/case-input-contract.md` before producing batch input.
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- Read `references/experience-library-schema.md` before extending the output
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schema or adding a consumer.
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- Read `references/llm-review-contract.md` before authoring or revising a
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semantic proposal draft.
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