CAD Spec Generator — Universal AI Skill for CAD Pipelines

From Markdown to machining-ready drawings and photorealistic renders — powered by any LLM.

A cross-platform AI skill for the complete CAD pipeline. Works with Claude Code, GPT-4, GLM-4, Qwen, LangChain, AutoGen, Dify — or any LLM with shell execution. One skill gives your AI agent the ability to: extract specs from design docs, generate GB/T-compliant 2D drawings, produce geometrically accurate 3D renders, and create photorealistic presentation images.

Latest: v2.24.0 — Model-library invocation loop release covering stable model-project paths, read-only SW export planning, hash-bound user STEP imports, and geometry-quality reporting through codegen. See CHANGELOG.md.

v2.9.1 — End-to-end regression-hardening release, shipped after a full real-document pipeline test on the GISBOT end-effector design doc. Four skill bugs fixed: (1) check_env.py now reads blender_path from pipeline_config.json so Level 4 RENDER is detected correctly when Blender isn't on PATH; (2) assembly_validator.py GATE-3.5 no longer crashes on parse_envelopes() v2.9 dict return — new _envelope_dims() adapter tolerates both tuple and dict shapes; (3) cad_pipeline.py enhance --backend engineering now accepted in argparse choices (was silently falling through to gemini); (4) new engineering_enhancer.py — fully implements the zero-AI backend long documented in pipeline_config.json (Blender PBR PNG → PIL sharpness/contrast/saturation → JPG). See RELEASE_v2.9.1.md.

v2.9.0 — Section-header walker (cad_spec_section_walker.py) with 4-tier hybrid matching (structured pattern / CJK+ASCII subsequence / Jaccard + Tier 0 regression guard) attributes 模块包络尺寸 markers to the correct BOM assembly. Six-step granularity enforcement chain prevents station-level envelopes from silently sizing individual purchased parts. Vendor STEP auto-synthesizer warms ~/.cad-spec-gen/step_cache/ on first use so fresh projects route Maxon/LEMO/ATI rows to real geometry without hand-crafted YAML. cad_pipeline.py --out-dir flag + subsystem-configurable walker kwargs (trigger_terms, station_patterns, axis_label_default) make the pipeline work on non-GISBOT subsystems. See RELEASE_v2.9.0.md and docs/PARTS_LIBRARY.md for the full picture.

Installation

From PyPI (Recommended)

pip install cad-spec-gen
cad-skill-setup

The interactive wizard guides you through:

1. Language selection (中文 / English)
2. Environment detection (Python, CadQuery, ezdxf, Blender, etc.)
3. Optional dependency installation
4. Blender configuration
5. Pipeline config generation
6. Skill file registration

Non-interactive mode: cad-skill-setup --lang en --target . --skip-deps

Codex registration:

# Install project tools and generate Codex SKILL.md files under ~/.agents/skills
cad-skill-setup --lang zh --target . --skip-deps --agent codex

# Install both Claude Code slash commands and Codex skills
cad-skill-setup --lang zh --target . --skip-deps --agent both

Check environment: cad-skill-check

After Claude installation, type /cad-help in Claude Code to get started. After Codex installation, start a new Codex session so it can discover ~/.agents/skills/cad-*/SKILL.md, then ask for CAD spec/codegen/rendering work in natural language.

Manual skill registration (if not using cad-skill-setup):

• Project-level: .claude/commands/*.md (legacy) — auto-discovered in project dir
• Codex global: ~/.agents/skills/<name>/SKILL.md — generated by cad-skill-setup --agent codex|both
• Claude global (optional): Copy to ~/.claude/skills/<name>/SKILL.md with YAML frontmatter for all-project access

Update

pip install --upgrade cad-spec-gen
cad-skill-setup --update

Other Platforms

All tools are plain Python CLI scripts — no framework lock-in, no vendor dependency.

Design Document (.md)
    ↓ cad_spec_gen.py --review — mechanical / assembly / material / completeness checks
DESIGN_REVIEW.md (issues & recommendations, user iterates or proceeds)
    ↓ cad_spec_gen.py — extract 9 categories of structured data
CAD_SPEC.md (single source of truth for all downstream CAD work)
    ↓ codegen/gen_*.py — Jinja2 templates → CadQuery scaffolds
params.py + build_all.py + station_*.py + std_*.py + assembly.py (per-part offsets + station transforms, generic part number support)
    ↓ CadQuery parametric modeling
STEP + STD-STEP (standard parts) + DXF (GB/T 2D drawings) + GLB
    ↓ render_dxf.py — auto DXF→PNG engineering drawing previews (if script exists)
DXF PNG previews (for design review)
    ↓ Blender Cycles rendering (GPU auto-detect, CPU fallback)
N-view PNG — 100% geometry-accurate, cross-view consistent (default 5, configurable)
    ↓ AI enhancement (reskin only, geometry locked) — 4 backends: gemini | fal | comfyui | engineering
       python cad_pipeline.py enhance --subsystem <name> [--dir <dir>] [--backend <backend>]
Photorealistic PNG — presentation / defense / business plan ready
    ↓ Enhancement acceptance — manifest-bound delivery status
       python cad_pipeline.py enhance-check --subsystem <name> --dir <render_dir>
ENHANCEMENT_REPORT.json — accepted / preview / blocked
    ↓ python cad_pipeline.py annotate — PIL-based component labels (CN/EN)
Labeled PNG — with leader lines and component names

Why This Tool?

Architecture

┌──────────────────────────────────────────────────────────┐
│  Platform Adapters  (pick one, or use system_prompt.md)   │
│  ├── .claude/commands/ → Claude Code slash commands        │
│  ├── ~/.agents/skills/ → Codex SKILL.md files              │
│  ├── openai/       → Function Calling JSON schema         │
│  ├── langchain/    → LangChain/AutoGen Tool wrapper       │
│  └── dify/         → Knowledge base import guide          │
├──────────────────────────────────────────────────────────┤
│  Universal Skill Layer                                    │
│  ├── skill.json         → machine-readable skill manifest │
│  ├── system_prompt.md   → paste into any LLM              │
│  └── skill_cad_help.md  → 16-intent knowledge base        │
├──────────────────────────────────────────────────────────┤
│  Tool Layer  (pure Python CLI, no LLM dependency)         │
│  ├── cad_pipeline.py    → unified 6-phase orchestrator    │
│  ├── cad_spec_gen.py    → spec extraction                 │
│  ├── cad_spec_reviewer.py → design review (4 categories)  │
│  ├── codegen/gen_*.py   → Jinja2 code generation          │
│  ├── bom_parser.py      → BOM parsing                     │
│  └── config/templates/  → subsystem configs + Jinja2 .j2  │
└──────────────────────────────────────────────────────────┘

Pipeline Architecture

┌────────────────────────────────────────────────────────────────┐
│                   CAD Hybrid Rendering Pipeline                 │
│                                                                 │
│  1. DESIGN REVIEW (optional, recommended)                         │
│     Design doc (.md) → cad_spec_gen.py --review                   │
│     → DESIGN_REVIEW.md (A.mechanical / B.assembly incl. B5-B8    │
│       floating parts & connection checks / C.material / D.gaps)  │
│     User: "继续审查" / "自动补全" (--auto-fill) / "下一步"          │
│  ✋ [GATE-1] CRITICAL issues block SPEC phase until confirmed     │
│                                                                 │
│  2. SPEC EXTRACTION (this repo)                                   │
│     Design doc (.md) → cad_spec_gen.py → CAD_SPEC.md            │
│     9 sections: params, tolerances, fasteners, connections,     │
│     BOM tree, assembly pose, visual IDs, render plan, gaps      │
│     v2.3: auto-extracts per-part feature list (holes/slots)     │
│     by cross-referencing §2/§3/§4/§8 (extract_part_features)   │
│     v2.9: §6.4 envelope attribution via stateful SectionWalker  │
│     (cad_spec_section_walker.py) — 4-tier hybrid matching       │
│     (Tier 0 part_no regression guard + Tier 1 structured        │
│     pattern + Tier 2 CJK+ASCII subsequence + Tier 3 Jaccard)    │
│     with 2-phase dispatch. Per-instance kwargs (trigger_terms / │
│     station_patterns / axis_label_default) make it work across  │
│     non-GISBOT subsystems. Canonical (X,Y,Z) axis rewrite +     │
│     machine-readable reason codes → §6.4.1 UNMATCHED subsection.│
│                                                                 │
│  3. CODE GENERATION (Jinja2)                                      │
│     CAD_SPEC.md → codegen/gen_*.py → params.py + build_all.py   │
│     + station_*.py scaffolds + std_*.py (standard parts)         │
│     + assembly.py (per-part offsets + station radial transforms)  │
│     Templates: templates/*.j2 (scaffold mode, never overwrites) │
│     ⚠ Scaffolds are incomplete: params.py needs correct naming,  │
│     build_all.py needs valid module refs, assembly.py needs       │
│     hand-written mate logic. Complete before Phase 4.             │
│     v2.21.2: parts_library.yaml routes purchased parts through   │
│     user/project STEP, shared STEP cache, SW Toolbox,             │
│     bd_warehouse / PartCAD adapters before jinja_primitive        │
│     terminal fallback. v2.8.1: extends:default registry          │
│     inheritance + per-build resolver coverage report.            │
│     v2.8.2: F1+F3 disc_arms flange (full-thickness arms +        │
│     chamfer/fillet) + GLB consolidator (per-face → per-part).    │
│     v2.9: six-step granularity enforcement chain — walker →      │
│     parse_envelopes → PartQuery.spec_envelope_granularity →      │
│     JinjaPrimitiveAdapter REJECTS station_constraint envelopes,  │
│     falling through to lookup_std_part_dims instead of sizing    │
│     individual purchased parts as the full station bounding box. │
│     Vendor STEP auto-synthesizer (adapters/parts/                │
│     vendor_synthesizer.py) warms ~/.cad-spec-gen/step_cache/     │
│     on first use. geometry_report.json records A-E quality and   │
│     flags remaining D/E simplified placeholders for upgrade.     │
│  ✋ [GATE-2] TODO scan — exit code 2 if unfilled TODO: markers   │
│                                                                 │
│  4. PARAMETRIC MODELING                                         │
│  ✋ [GATE-3] orientation_check.py — asserts bounding-box axes    │
│     CadQuery scripts → STEP + GLB + DXF                        │
│     - 3D: assemblies with precise mate constraints              │
│     - 2D: GB/T A3 drawings, 3-view + section views              │
│     v2.3: position dims, orthogonal dim angles, A-A section     │
│     hatch (holes excluded), dynamic tech notes placement        │
│                                                                 │
│  5. 3D RENDERING (Blender Cycles, GPU auto-detect)                │
│     GLB → N-view PNG (geometry 100% accurate, default 5 views)  │
│     15 PBR material presets · spherical camera system            │
│     Default views: front-iso / rear / side / exploded / ortho   │
│     Views are config-driven: render_config.json camera section   │
│                                                                 │
│  6. AI ENHANCEMENT (optional) — 4 backends                      │
│     PNG → photorealistic JPG (reskin only, geometry locked)     │
│     Backends: gemini (~$0.02/img, cloud, soft geometry lock)    │
│               fal (fal.ai Flux ControlNet, ~$0.20/img, hard    │
│                    depth+canny lock) [NEW in v2.3]              │
│               comfyui (local GPU ControlNet, free, hard lock)   │
│               engineering (Blender PBR direct→JPG, free,        │
│                    perfect geometry, no AI) [NEW in v2.3]       │
│     Auto-detect: FAL_KEY→fal, ComfyUI→comfyui, gemini→gemini,  │
│                  else→engineering                                │
│     Fallback: fal→gemini→engineering (batch-locked on downgrade)│
│     CLI: --backend gemini|fal|comfyui|engineering               │
│     v2.3: unified MATERIAL_PRESETS appearance (single source    │
│     of truth for both Blender PBR + AI prompt), view-aware      │
│     material emphasis (Fresnel/specular per camera angle),      │
│     material_type→preset auto-fallback from params.py           │
│     Material bridging: bom_id→component→material auto-lookup    │
│                                                                 │
│  Output:  PNG → engineering review / machining reference        │
│           JPG → presentations / proposals / business plans      │
└────────────────────────────────────────────────────────────────┘

Quality Gates

Three mandatory checkpoints block the pipeline on failure:

Gate 3 is skipped if orientation_check.py does not exist in the subsystem directory (non-mandatory for new subsystems).

Key Features

Spec Extraction (cad_spec_gen.py)

• 9-section structured output: parameters, tolerances, fasteners, connection matrix, BOM tree, assembly pose, visual IDs, render plan, completeness report
• Design review mode (--review): mechanical stress (A1-A3), assembly fit & connection graph (B1-B8), material compatibility (C1-C3), completeness gaps (D1+) → DESIGN_REVIEW.md
• User-driven iteration: 3 options — "继续审查" (iterate) / "自动补全" (--auto-fill, computes missing torques/Ra/units) / "下一步" (proceed)
• Idempotent: MD5-based skip — won't regenerate if source unchanged
• Auto-defaults: standard bolt torques (8.8 grade), surface Ra by material type
• Derived calculations: total cost, part count, BOM completeness %
• Configurable: subsystem mapping via JSON config, no hardcoded paths
• Generic part numbers: supports any prefix format (GIS-EE-xxx, SLP-xxx, ACME-xxx) — not limited to GIS
• Flat BOM support: subsystems without sub-assembly hierarchy are handled correctly

Interactive Help (16 Intents)

• Natural language — no need to memorize CLI syntax, just ask in plain language
• 16 intents: environment check, config validation, next-step recommendation, materials, camera, exploded view, rendering, AI enhancement, troubleshooting, file structure, status, cross-model integration, parts/BOM, CAD spec, design review
• Smart "what's next?" — scans project artifacts (STEP/DXF/GLB/PNG/JPG) and recommends the highest-priority next action

2D Engineering Drawings

• GB/T national standard: first-angle projection, A3 sheet, title block
• 12-layer DXF system with 0.5mm line widths per GB/T 17450
• FangSong font, 3.5mm annotation height
• Section views (A-A cut lines), datum triangles, default Ra callouts

3D Rendering

• Blender Cycles with GPU auto-detect (OptiX > CUDA > HIP > OneAPI > CPU fallback) — also works on remote desktops without GPU
• 5 standard views (default, configurable per subsystem): V1 front-iso, V2 rear-oblique, V3 side-elevation, V4 exploded, V5 ortho-front
• 15 PBR material presets: brushed aluminum, PEEK, carbon fiber, rubber, glass, etc.
• Exploded views: radial / axial / custom explosion with assembly lines
• Config-driven: render_config.json controls materials, cameras, explosion rules
• Material bridging: resolve_bom_materials() auto-derives PBR materials from BOM part IDs via bom_id→component→material lookup chain; auto-creates missing entries with consistency validation

AI Enhancement (Hybrid Rendering) — 4 Backends

• Four backends: gemini (cloud AI, ~$0.02/img), fal (fal.ai Flux ControlNet, ~$0.20/img), comfyui (local GPU ControlNet, free), engineering (Blender PBR direct, free)
• Auto-detect: checks FAL_KEY → ComfyUI server → Gemini config → falls back to engineering
• Fallback chain: fal → gemini → engineering (batch-locked after downgrade to ensure consistency)
• Geometry-locked: Blender PNG provides exact geometry; AI only changes surface appearance
• Standard parts: real STEP / SW Toolbox / parametric library models are preferred; simplified CadQuery shapes are terminal fallback and are reported through geometry_report.json
• Cross-view consistent: all 5 views share the same 3D source
• Dual output: PNG for engineering, JPG for presentation
• Prompt templates: auto-generated from render config variables
• CLI: --backend gemini|fal|comfyui|engineering on both enhance and full commands

Quick Start

Two reference subsystems included: End Effector (GIS-EE, radial layout, 24 parts) and Lifting Platform (SLP, vertical linear actuator, 32 parts). Adapt paths for your own subsystem.

# Scaffold a new subsystem (generates render_config.json, params.py, design doc template)
python cad_pipeline.py init --subsystem robot_arm --name-cn 机器人臂 --prefix RA
# → output/robot_arm/render_config.json, output/robot_arm/params.py, docs/design/XX-robot_arm.md

# One-click full pipeline (all 6 phases)
python cad_pipeline.py full --subsystem end_effector \
    --design-doc docs/design/04-末端执行机构设计.md --timestamp

# Or step-by-step:

# Phase 1: Design review + spec (recommended first)
python cad_pipeline.py spec --design-doc docs/design/04-末端执行机构设计.md --auto-fill
# → cad/end_effector/DESIGN_REVIEW.md + CAD_SPEC.md

# Phase 2: Generate CadQuery scaffolds
python cad_pipeline.py codegen --subsystem end_effector
# → params.py, build_all.py, station_*.py, std_*.py, assembly.py

# Phase 3-4: Build + render
python cad_pipeline.py build --subsystem end_effector
python cad_pipeline.py render --subsystem end_effector --timestamp
# Note: view script selection is automatic from render_config.json `type` field
# (type=exploded → render_exploded.py, type=section → render_section.py, etc.)

# Phase 5-6: AI enhance + annotate (optional)
# enhance auto-reads render_manifest.json (only current-session renders); use --dir to override
python cad_pipeline.py enhance --subsystem end_effector
python cad_pipeline.py enhance-check --subsystem end_effector --dir cad/output/renders/end_effector/<run_id>
python cad_pipeline.py annotate --subsystem end_effector --lang cn,en

# Check pipeline status
python cad_pipeline.py status

# Check environment
python cad_pipeline.py env-check

多轮渐进确认（v2.31.0+）

不必一次说全 KPI；起手只说大方向，系统主动追问：

# 第一步：起手（不全也 ok）
$ python cad_pipeline.py project-guide --product-goal "做升降平台"
# → status=needs_kpi_confirmation；状态已记到 ./PROJECT_GOAL_STATE.json
# → 提示：cad-spec-gen project-guide --resume --answer load_kg=50

# 第二步：续答任一项（可重复）
$ python cad_pipeline.py project-guide --resume --answer load_kg=50
$ python cad_pipeline.py project-guide --resume --answer stroke_mm=800
$ python cad_pipeline.py project-guide --resume --answer platform_size_mm=600x600

# 全齐自动删 state + ready_for_cad_spec

也可一次答多个 KPI：

$ python cad_pipeline.py project-guide --resume --answer load_kg=50 --answer stroke_mm=800

./PROJECT_GOAL_STATE.json 是 project-local 状态文件（已加 .gitignore）。

AI Enhancement Quick Start

After Blender renders your PNGs, enhance them to photorealistic images via the pipeline.
Four backends are supported: gemini (cloud AI, ~$0.02/img), fal (fal.ai Flux ControlNet, ~$0.20/img, hard geometry lock), comfyui (local GPU ControlNet, free, hard lock), and engineering (Blender PBR direct, free, perfect geometry, no AI).

v2.1 — Multi-view consistency (Gemini): four-layer defense ensures each view keeps its correct camera angle after enhancement: auto-computed azimuth/elevation written into prompt, source image placed first (locks composition), V1 result used as material anchor for V2–VN, source PNG sent at full resolution (≤4 MB uncompressed).

First-time Gemini setup — configure your API proxy:

python gemini_gen.py --config
# Prompts for: API Key, API Base URL (your proxy), model name, output dir
# Saved to: ~/.claude/gemini_image_config.json

# Auto-detect best backend (FAL_KEY→fal, ComfyUI→comfyui, gemini→gemini, else→engineering)
python cad_pipeline.py enhance --subsystem <name>

# Specify custom output directory (also reads manifest from that dir)
python cad_pipeline.py enhance --dir /path/to/renders

# Force a specific backend
python cad_pipeline.py enhance --subsystem <name> --backend fal
python cad_pipeline.py enhance --subsystem <name> --backend gemini
python cad_pipeline.py enhance --subsystem <name> --backend comfyui
python cad_pipeline.py enhance --subsystem <name> --backend engineering

# Backend also works on full pipeline
python cad_pipeline.py full --subsystem <name> --backend fal

# Override Gemini model temporarily
python cad_pipeline.py enhance --dir /path/to/renders --model nano_banana_pro

# Check ComfyUI environment manually before first use
python comfyui_env_check.py

The enhance step automatically:

• Reads render_manifest.json from --dir or default renders dir to process only latest render files
• Auto-enriches prompt data from params.py via prompt_data_builder.py (materials, assembly description, constraints)
• gemini (v2.1): geometry and viewpoint locked via prompt — auto-computed camera angle (azimuth/elevation), source image first, V1-anchor reference, full-res PNG input
• fal: fal.ai Flux ControlNet — hard depth+canny geometry lock, cloud-based, ~$0.20/image
• comfyui: local GPU ControlNet depth+canny to hard-lock geometry; requires local GPU
• engineering: Blender PBR rendered directly to JPG — no AI, free, perfect geometry fidelity
• Auto-detect priority: FAL_KEY env var → ComfyUI server running → Gemini config → engineering fallback
• Fallback chain: if fal fails mid-batch → downgrade to gemini → engineering (batch-locked after downgrade)
• Skips *_enhanced.* files to prevent re-processing

Switch backend permanently in pipeline_config.json:

"enhance": { "backend": "fal" }

Output: <render_dir>/<VN>_<name>_<timestamp>_enhanced.png per view, photorealistic studio quality.

Run acceptance before delivery:

python cad_pipeline.py enhance-check --subsystem <name> --dir <render_dir>

enhance-check reads the explicit render directory's render_manifest.json, requires every manifest view to have exactly one same-directory *_enhanced.* image, compares source/enhanced shape and basic image QA, and writes ENHANCEMENT_REPORT.json with accepted, preview, or blocked. It does not scan directories for newest files and rejects enhanced images outside the bound render directory.

Component Label Annotation

After AI enhancement, add component labels (Chinese/English) via PIL:

# Annotate all views in Chinese (auto-reads manifest)
python cad_pipeline.py annotate --subsystem <name> --lang cn

# Annotate from a specific directory
python cad_pipeline.py annotate --dir /path/to/renders --lang cn,en

Labels are defined in render_config.json:

• components section: maps IDs to CN/EN names + BOM IDs (from design doc §X.8 BOM)
• labels section: per-view coordinates for visible components only (occluded = not labeled)
• Coordinates at 1920×1080 reference (configurable via reference_resolution), auto-scaled to actual image size

一条命令跑完 photo3d 验收闭环（v2.28.0+）

# 第一步：预览（不执行；看下一步要跑什么）
python cad_pipeline.py photo3d-handoff --subsystem lifting_platform --with-jury

# 第二步：加 --confirm 实跑（触发 enhance + check + jury 自动验收 + enhance-review）
python cad_pipeline.py photo3d-handoff --subsystem lifting_platform --with-jury --confirm

详细 flag 矩阵 / 故障恢复 / CI 集成示例见 docs/cad-jury-config.md。

Adding a New Subsystem

Option A: One-command scaffold (recommended)

python cad_pipeline.py init --subsystem <your_subsystem> --name-cn <中文名> --prefix <PREFIX>

Generates three files automatically:

• output/<your_subsystem>/render_config.json — camera views (V1-V5), materials, CN/EN component names
• output/<your_subsystem>/params.py — dimension skeleton
• docs/design/XX-<your_subsystem>.md — design doc template

Then edit each file and run the full pipeline:

python cad_pipeline.py full --subsystem <your_subsystem> --design-doc docs/design/XX-<your_subsystem>.md

Option B: Manual setup

1. Create directory and config:

   mkdir cad/<your_subsystem>/
   cp templates/render_config_template.json cad/<your_subsystem>/render_config.json
   

   Edit render_config.json — fill in subsystem info, materials, camera views, and components.

2. Auto-generate scaffolds (if design doc exists):

   python cad_pipeline.py spec --design-doc docs/design/NN-*.md
   python cad_pipeline.py codegen --subsystem <your_subsystem>
   

3. Refine scaffolds: Edit generated files — params.py needs correct descriptive parameter names (codegen produces line-number based names), build_all.py needs valid module references, assembly.py needs real mate logic. Replace placeholder boxes in station_*.py with actual CadQuery geometry.

4. Build + render:

   python cad_pipeline.py full --subsystem <your_subsystem> --skip-spec --skip-codegen --timestamp
   

See templates/render_config_template.json for field documentation.

Usage

python cad_spec_gen.py [FILES...] --config CONFIG [OPTIONS]

Required:
  --config PATH         JSON config with subsystem mapping

Options:
  --output-dir DIR      Output directory (default: ./output)
  --doc-dir DIR         Design docs directory for --all
  --all                 Process all NN-*.md in doc-dir
  --force               Force regeneration (ignore MD5 check)
  --review              Run design review before spec generation
  --review-only         Run design review only (no spec generation)
  --auto-fill           Auto-fill computable missing values (torques, units, Ra)

Process all subsystems at once

python cad_spec_gen.py --all --config config/gisbot.json --doc-dir docs/design

BOM parser (standalone)

python bom_parser.py examples/04-末端执行机构设计.md          # tree view (GIS-EE format)
python bom_parser.py examples/04-末端执行机构设计.md --json   # JSON output
python bom_parser.py examples/04-末端执行机构设计.md --summary # one-line summary
# Supports any BOM table header: 料号/图号/编号 + 名称 + 数量 (+ optional 材质/类型/备注)

Configuration

Create a JSON config file (see config/gisbot.json for a full 19-subsystem example):

{
  "doc_dir": "docs/design",
  "output_dir": "./output",
  "subsystems": {
    "04": {
      "name": "End Effector",
      "prefix": "GIS-EE",
      "cad_dir": "end_effector",
      "aliases": ["ee", "end_effector"]
    },
    "19": {
      "name": "Lifting Platform",
      "prefix": "SLP",
      "cad_dir": "lifting_platform",
      "aliases": ["升降", "lifting"]
    }
  }
}

15 PBR Material Presets

Documentation

Project Structure

├── skill.json                      # Machine-readable skill manifest
├── system_prompt.md                # Universal system prompt (any LLM)
├── skill_cad_help.md               # Skill knowledge (16 intents + actions)
├── cad_pipeline.py                 # Unified 6-phase pipeline orchestrator
├── cad_paths.py                    # Path resolution (SKILL_ROOT / PROJECT_ROOT / Blender / Gemini)
├── render_config.py                # Render config engine (15 material presets + material bridging)
├── pipeline_config.json            # Persistent config (Blender path, render settings)
├── cad_spec_gen.py                 # Spec extraction (CLI entry point)
├── cad_spec_extractors.py          # 8 extraction functions + table parser
├── cad_spec_defaults.py            # Standard defaults, engineering constants
├── cad_spec_reviewer.py            # Design review engine (4 categories)
├── bom_parser.py                   # BOM table parser (also standalone CLI)
├── annotate_render.py              # PIL-based component label annotation (CN/EN)
├── enhance_prompt.py               # Prompt builder for AI enhancement phase
├── prompt_data_builder.py          # Auto-generates material/assembly data from params.py
├── comfyui_enhancer.py             # ComfyUI backend: ControlNet depth+canny geometry lock
├── comfyui_env_check.py            # ComfyUI environment validator (GPU, models, server mode)
├── codegen/                        # Jinja2 code generation from CAD_SPEC.md
│   ├── gen_params.py               # §1 params → params.py
│   ├── gen_build.py                # §5 BOM → build_all.py (STEP + STD + DXF)
│   ├── gen_parts.py                # §5 custom leaf parts → station_*.py scaffolds
│   ├── gen_std_parts.py            # §5 purchased parts → std_*.py + geometry_report.json
│   └── gen_assembly.py             # §4+§5+§6 → assembly.py (incl. standard parts)
├── templates/
│   ├── params.py.j2                # Jinja2: params.py generation
│   ├── build_all.py.j2             # Jinja2: build_all.py generation
│   ├── part_module.py.j2           # Jinja2: part module scaffold
│   ├── assembly.py.j2              # Jinja2: assembly scaffold
│   ├── cad_spec_template.md        # Output template reference
│   ├── design_review_template.md   # Design review output template
│   ├── prompt_enhance_unified.txt  # AI prompt: all views (unified template)
│   └── prompt_section.txt          # Section view prompt template
├── gemini_gen.py                   # Gemini image generation (OpenAI-compatible API)
├── .claude/commands/               # Claude Code slash commands (5 commands, legacy format)
├── adapters/
│   ├── openai/
│   │   ├── functions.json         # OpenAI Function Calling schema
│   │   └── README.md              # GPT-4 / Assistants setup guide
│   ├── langchain/
│   │   ├── tools.py               # LangChain Tool wrapper
│   │   └── README.md              # LangChain / AutoGen setup guide
│   └── dify/
│       └── README.md              # Dify / Coze setup guide
├── config/
│   └── gisbot.json                # Example: 19-subsystem config
├── cad/
│   ├── end_effector/              # Reference: radial 4-station layout (GIS-EE, 24 parts)
│   └── lifting_platform/         # Reference: vertical linear actuator (SLP, 32 parts)
├── examples/
│   └── 04-末端执行机构设计.md       # Example design document
└── docs/
    ├── cad-help-guide-en.md       # User guide (English)
    ├── cad-help-guide-zh.md       # User guide (Chinese)
    └── cad_pipeline_agent_guide.md # Cross-LLM agent integration guide

Codex SKILL.md files are generated outside the repository at ~/.agents/skills/cad-*/.

CI

GitHub Actions workflows（见 .github/workflows/）：

• tests.yml：常规单元/集成测试，PR 与 push 触发（ubuntu + windows matrix，不含真 SolidWorks）
• sw-smoke：真 SolidWorks 环境回归（self-hosted runner，仅 main push + 手动触发；commit 含 [skip smoke] 跳过）

License

MIT