Builds and evaluates Python module layers for the Gentleman-Skills ecosystem. Generates eval scenarios, adapts skills for LLM targets, and performs code analysis (dead code, complexity, blast radius). Trigger: When working in `build_modules/` — adding, modifying, or debugging module generation, adaptation, or analysis.
Installs into .claude/skills of the current project.
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---
name: build_modules-layer
description: >
Builds and evaluates Python module layers for the Gentleman-Skills ecosystem.
Generates eval scenarios, adapts skills for LLM targets, and performs code
analysis (dead code, complexity, blast radius).
Trigger: When working in `build_modules/` — adding, modifying, or debugging
module generation, adaptation, or analysis.
license: Apache-2.0
metadata:
author: repoforge
version: "1.0"
complexity: medium
token_estimate: 1200
dependencies: []
related_skills: [eval-layer, repoforge-layer]
---
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# build_modules-layer
This layer provides tools for building, evaluating, and analyzing Python modules within the Gentleman-Skills ecosystem. It handles module generation, LLM target adaptation, and code quality analysis.
**Trigger**: When working in `build_modules/` directory — adding, modifying, or debugging module generation, adaptation, or analysis.
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## Quick Reference
| Task | Pattern |
|------|---------|
| Run eval scenarios | `python -m eval.harness run_all` |
| Adapt skills for LLM targets | `adapt_for_cursor/Codex/Gemini/Copilot` |
| Detect dead code | `detect_dead_code()` |
## Critical Patterns (Summary)
- **Pattern 1: Module Generation**: Use `make_fastapi_crud_module`, `make_nextjs_page_module`, `make_mixed_layer`, or `make_go_service_module` from `eval/harness.py` to generate module skeletons in various frameworks.
- **Pattern 2: Skill Adaptation**: Use `adapt_for_cursor`, `adapt_for_codex`, `adapt_for_gemini`, or `adapt_for_copilot` from `repoforge/adapters.py` to transform skill dictionaries for specific LLM targets.
## When to Use
- Adding a new module generation function to `eval/harness.py`
- Adapting existing skills for a different LLM target platform
- Detecting dead code or analyzing module complexity
- Running eval scenarios against generated modules
## Adding a New Module Generation Function
1. Add a new function to `eval/harness.py` following the pattern `make_<framework>_module() -> tuple[dict, dict]`
2. Export the function in `eval/harness.py` `__all__` if present
3. Test with `run_scenario` using the new module config
4. Verify output with `print_report`
## Commands
```bash
python -m eval.harness run_all
python -m eval.harness run_scenario <scenario_name>
adapt_for_cursor <skills_json>
adapt_for_copilot <skills_json>
detect_dead_code <ast_symbols_dict>
```
## Anti-Patterns
### Don't: Mix adaptation targets — using `adapt_for_copilot` skills with `adapt_for_cursor` expectations — each adapter formats skill dicts differently for its target LLM, causing integration failures.
```python
# BAD: Mixing adaptation targets
skills = adapt_for_cursor(skills_dict)
# Later using skills with Cursor — works
# But passing these same skills to adapt_for_codex will produce
# incorrectly formatted output for Codex's expectations.
```
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