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build123d-core

Use when custom Build123d Python CAD code, geometry operations, selectors, locations, joints, assemblies, imports, exports, or DFM details are needed. Read `references/dfm-rules.md` BEFORE writing the first line of geometry code for any screw boss, standoff, snap fit, wall interface, or overhang-sensitive feature — not after the shape exists. A screw fastening into a 3D-printed part additionally requires the `build123d-libs` kit; never hand-model that joint.

68

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured index-style skill body: lean conventions, a numbered workflow with genuine validation checkpoints, and a clean one-level-deep reference table whose files all exist. The only real defects are minor: a duplicated dfm-rules directive, a cad.edit example that is named but not shown, and the 'Plane Id Convention' section header does not match its content (cad.inspect call examples).

Suggestions

Rename the 'Plane Id Convention' section to something matching its content (e.g. 'Inspection Calls'), since it contains cad.inspect examples rather than a plane/id convention beyond the opening sentence about publish ids.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence (no explanation of what Build123d or CAD is; conventions are stated as terse directives). Minor trimming is possible: the dfm-rules read-directive is stated twice (intro paragraph and Core Workflow step 3), and the five-example cad.inspect block is slightly heavy. This fits the 4 anchor ('efficient; minor instances that could be trimmed') rather than the 5 anchor's every-token-earns-its-place.

4 / 5

Actionability

Provides concrete, executable material: a working code snippet (Box(40, 30, 12); publish("body", body, "Main body")), full JSON shapes for cad.inspect calls with specific fields (object_id, topology_ref, buildNumber, mode), and exact tool names and options (cad.look views/isolate/context/hide). Gaps are minor: cad.edit usage is named ('action="add_part"') but never shown as a concrete call, fitting the 4 anchor ('mostly executable; minor gaps') rather than 5.

4 / 5

Workflow Clarity

The Core Workflow is a clear numbered sequence with explicit validation checkpoints and feedback loops: 'inspect its returned image before the next edit', embedded assert/print self-checks of key dimensions, 'never claim an unseen property is resolved', and exports gated on a Host-owned review ('proceed only on Pass'). This matches the 5 anchor (clear sequence, explicit validation, error-recovery guidance).

5 / 5

Progressive Disclosure

The body is a pure index: conventions and workflow inline, with an annotated reference table pointing one level deep to 11 files (pitfalls, objects, operations, topology-selection, locations, import-export, tips, joints, assemblies, dfm-rules, connectors), each with a 'what it answers' column; all referenced files exist in references/. This matches the 5 anchor (clear overview, well-signaled one-level-deep references, easy navigation).

5 / 5

Total

18

/

20

Passed

Description

78%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, well-targeted description with an explicit 'Use when' trigger clause, natural CAD/DFM terminology, and an explicit boundary against the build123d-libs kit. The main gap is that the skill's core function (a reference index for Build123d authoring/debugging) is implied through directives rather than stated outright, and a few natural trigger synonyms/extensions are absent.

Suggestions

State the 'what' explicitly alongside the trigger, e.g. 'Reference kit for authoring and debugging custom Build123d Python CAD code. Use when...' so both halves of the description are explicit.

Add a few natural trigger variations users say, such as 'CAD model', 'parametric part', or file extensions like '.step' and '.stl', to broaden keyword coverage.

DimensionReasoningScore

Specificity

Names the Build123d CAD domain and gives several concrete directives ('Read references/dfm-rules.md BEFORE writing the first line of geometry code', 'never hand-model that joint', load the build123d-libs kit), plus a specific feature list (screw boss, standoff, snap fit, wall interface, overhang-sensitive feature). It is a strong trigger-plus-directive description but not a comprehensive list of capability actions, so it fits the 4 anchor ('several specific actions; minor gaps') rather than the 5 anchor.

4 / 5

Completeness

The 'when' is explicit and concrete ('Use when custom Build123d Python CAD code... are needed'), and the 'what' is conveyed through the domain list and reading directives. However, the skill's actual role (a reference index for authoring/debugging Build123d code) is implied rather than stated as a capability, fitting the 4 anchor (both present, one could be more explicit) rather than the 5 anchor's fully explicit what+when.

4 / 5

Trigger Term Quality

Includes natural terms users would actually say: 'Python CAD code', 'selectors', 'joints', 'assemblies', 'screw boss', 'standoff', 'snap fit', 'overhang', '3D-printed'. Coverage is good but misses common synonyms and file extensions users mention (.step, .stl, 'CAD model'), matching the 4 anchor ('good keyword coverage; a few natural terms missing') rather than the 5 anchor's comprehensive synonym/extension coverage.

4 / 5

Distinctiveness Conflict Risk

Build123d is a clear, narrow niche with distinct triggers, and the description even draws an explicit boundary against a sibling skill ('A screw fastening into a 3D-printed part additionally requires the build123d-libs kit; never hand-model that joint'). Minimal conflict risk with other skills, matching the 5 anchor.

5 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
JimmyPang02/open-replicator
Reviewed

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